System

Abstract

A system is provided.SOLUTION: A system for allowing a user having a specific dietary restriction to determine whether or not the user can eat based on ingredient information included in a menu provided by a restaurant, the system comprising: means for reading a code including ingredient information of the menu; means for transmitting the read menu ingredient information to a server; means for comparing the server with the menu ingredient information and the dietary restriction information of the user and determining whether or not the user can eat; and means for notifying the user of a determination result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a need for a system that allows people with dietary restrictions (for example, due to allergies, medical treatment, pregnancy, religious reasons, etc.) to safely and easily determine whether or not a menu item is edible when eating out. Conventional methods require users to check the detailed ingredient information on the menu and make their own judgment, which is time-consuming and laborious, and there is also a risk that information may be overlooked in the process. There is a need for a method that can solve these issues and ensure food safety safely and efficiently. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system that allows users with specific dietary restrictions to determine whether they can eat a certain item based on ingredient information included in a restaurant's menu. This system includes a means for reading a code containing the menu's ingredient information, a means for transmitting the read menu ingredient information to a server, a means for the server to compare the menu ingredient information with the user's dietary restriction information to determine whether the item can be eaten, and a means for notifying the user of the determination result. This system allows users to easily and hassle-freely find menus that are suitable for their dietary restrictions, ensuring safety. Furthermore, the server uses generated artificial intelligence to evaluate the ingredient information and dietary restriction information, improving the accuracy and efficiency of the determination.

[0006] A "user with specific dietary restrictions" is someone who needs to avoid certain ingredients or foods due to allergies, medical treatment, pregnancy, religious reasons, etc.

[0007] "Menu ingredient information" refers to all ingredients and food components included in the meal menus offered by restaurants, as well as information related to their cooking methods.

[0008] "Code" refers to a format that encodes information, such as a QR code (registered trademark) or barcode, and can be read by a smartphone or other device.

[0009] "Means of reading" refers to the means of decoding the contents of a QR code or barcode using the camera of a smartphone or dedicated device.

[0010] A "server" is a computer system that receives, stores, processes, and returns results from data over a network.

[0011] "Means of transmission" refers to the means of transmitting data to the server via a smartphone or other device.

[0012] "Dietary restriction information" refers to information about ingredients, food components, and other dietary restrictions that users should avoid.

[0013] "Means for comparison" refers to the method by which the server compares the ingredient information of the menu with the dietary restriction information of the user to evaluate compatibility.

[0014] "Means for notifying the judgment result" refers to a method for displaying the evaluation result received from the server on the device used by the user.

[0015] "Generated artificial intelligence" refers to software and algorithms that incorporate machine learning and deep learning technologies used to analyze and evaluate food ingredients and ingredient information. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat certain foods based on the ingredient information included in the menus offered by restaurants when dining out. This system is configured using the user's smartphone, a server, and QR codes provided by restaurants.

[0038] overview

[0039] First, users install a dedicated application on their smartphone. This application has the function of registering the user's dietary restrictions in advance. Next, each menu item offered by a restaurant is affixed with a QR code containing information about the ingredients of the menu item. When users dine, they can easily check whether they can eat the item by scanning this QR code with the smartphone app.

[0040] Scan and send QR codes

[0041] When a user scans a QR code using a smartphone app, the device transmits the menu ID and the user's dietary restriction information obtained from the QR code to the server. At this time, the QR code contains detailed ingredient information for each menu item, so the menu ID can be obtained by decoding it.

[0042] Server-side processing

[0043] The server receives the menu ID and the user's dietary restriction information sent from the device. The server first retrieves detailed ingredient information for the menu item from the database. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information. For example, if a menu item contains peanuts, the server will determine that a user with a peanut allergy should not eat that menu item.

[0044] Notification of the results

[0045] After the server has completed its compatibility assessment, it sends the results back to the device. The device receives the results and displays them to the user. The results include a message such as "This menu item can be eaten" or "This menu item should not be eaten," along with the reason for the decision.

[0046] Specific examples

[0047] For example, consider a scenario where a user scans the QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) to the server. The server retrieves the ingredient information for "Pad Thai" from a database and checks whether it contains peanuts. The generated AI detects the presence of peanuts, compares it with the dietary restriction information, determines that "this menu item should not be eaten," and sends the result back to the device. The device displays this result to the user, who can then change their selection with peace of mind.

[0048] As a result, we can provide a system that ensures the safety of eating out for people with dietary restrictions and supports easy menu selection. This system can efficiently manage and compare user dietary restriction information and menu ingredient information, achieving high reliability and speed.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[0052] Step 2:

[0053] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[0054] Step 3:

[0055] The menu ID read by the terminal is combined with the user's pre-set dietary restriction information (for example, allergy information or religious restrictions).

[0056] Step 4:

[0057] The device creates a request to the server, which includes the menu ID and the user's dietary restrictions.

[0058] Step 5:

[0059] The request created by the device is sent to the server. When the server receives the request, it first analyzes the contents of the request.

[0060] Step 6:

[0061] The server uses the menu ID to retrieve detailed ingredient information for the corresponding menu from the database, including each ingredient and cooking method.

[0062] Step 7:

[0063] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restriction information, and the AI ​​evaluates whether the menu contains ingredients that are incompatible with the user's dietary restrictions.

[0064] Step 8:

[0065] The server generates a suitability evaluation result, which includes a judgment such as "This menu item is OK to eat" or "This menu item should not be eaten" and the reason for the judgment.

[0066] Step 9:

[0067] The server returns the evaluation results to the device, which are sent in a standard format such as JSON.

[0068] Step 10:

[0069] The terminal analyzes the evaluation results received from the server and displays them in an easy-to-read format for the user.

[0070] Step 11:

[0071] The user checks the evaluation results on the device screen. For example, "This menu item should not be eaten. Reason: It contains peanuts" is displayed.

[0072] By following the steps above, the user can easily and safely check menus that meet their own dietary restrictions.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] To enable customers with specific dietary restrictions to quickly and accurately determine whether they can eat a certain food when dining out based on the ingredient information included in the menu. Currently, it is time-consuming to check the ingredient information individually, and there is a risk of accidentally ingesting something, so there is a need for a system that solves this problem.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's dietary restriction information to the server, means for the server to use generated artificial intelligence to compare the menu ingredient information with the user's dietary restriction information to determine whether the food is permissible to eat, and means for notifying the user of the determination result and reason. This allows users to easily check the ingredient information of menus offered when dining out and select meals that suit their own dietary restrictions.

[0078] "Users with specific dietary restrictions" are users who cannot consume certain foods or ingredients due to allergies, religious restrictions, or health reasons.

[0079] "Codes containing menu ingredient information" refer to codes such as QR codes or barcodes that encode and include detailed ingredient information for menu items offered by restaurants.

[0080] "Means for reading" refers to the functionality or equipment for scanning the code and deciphering its contents using a camera and dedicated application installed on a device such as a smartphone or tablet.

[0081] "Means for sending to server" refers to communication means or protocols for transferring the read menu ingredient information and the user's dietary restriction information to a remote server via the Internet or the like.

[0082] "Generated artificial intelligence" refers to AI technologies such as machine learning models and deep learning models that are generated by programs to perform specific data processing and analysis.

[0083] The "means for determining" refers to the algorithms and processing logic that use a generative AI model within the server to compare menu ingredient information with the user's dietary restriction information and evaluate their suitability.

[0084] "Means of notification" refers to means for providing information to users, such as a function that displays the judgment results and the reasons for them on the user's device, or by means of push notifications or in-app messages.

[0085] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on the ingredient information of the menu items offered at restaurants. The system is configured using the user's smartphone, a server, and QR codes provided by the restaurant.

[0086] First, users install a dedicated application on their smartphone. This application has a function to register their dietary restrictions in advance. Specifically, users can input information about certain ingredients that they cannot consume due to allergies, religious restrictions, or health reasons.

[0087] Next, each menu item offered by a restaurant is affixed with a QR code containing the menu's ingredient information. The QR code encodes the menu ID and detailed ingredient information. When dining out, users can simply scan this QR code with a smartphone app to easily check whether they can eat the item.

[0088] When a user scans a QR code using a smartphone app, the device extracts the menu ID from the QR code and sends it to the server along with the user's dietary restriction information. The specific processing performed by the device includes a camera with QR code reading functionality and an application. The read data is then sent to the server via the Internet using protocols such as HTTP.

[0089] The server receives the menu ID and the user's dietary restriction information sent from the terminal. The server first retrieves detailed ingredient information for the menu item from the database. Next, it compares the ingredient information retrieved using the generative AI model with the user's dietary restriction information. An example of a prompt for the generative AI model is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[0090] The server analyzes the output of the generative AI model and determines whether the menu is suitable for the user. For example, if the ingredient information includes peanuts, the server generates a result that says "This menu should not be eaten" for a user with a peanut allergy.

[0091] The server then returns the judgment result (and reason) to the terminal. The judgment result is sent as an HTTP response, and the terminal receives the response from the server and analyzes its contents. Finally, the terminal displays the judgment result to the user. For example, a message such as "You can eat this menu item" or "You should not eat this menu item" along with the reason for the decision is displayed. This allows the user to make a meal selection with peace of mind.

[0092] For example, if a user scans the QR code for a menu item called "Pad Thai," the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) from the QR code to the server. The server retrieves the ingredient information for "Pad Thai" from the database and determines suitability using a generative AI model. As a result, the device sends a message back to the device saying, "This menu item contains peanuts and is not suitable for you," and the device displays that information to the user.

[0093] The purpose of this system is to enable people with dietary restrictions to select menu items with peace of mind when eating out. The system configuration effectively combines the user's device, server, and QR codes provided by restaurants, achieving high reliability and speed.

[0094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0095] Step 1:

[0096] Users install a dedicated application on their smartphone and register their dietary restrictions.

[0097] Specifically, users download the app from the application store, install it, and then launch it. After launching the app, they can enter and save information about certain foods and ingredients they cannot consume due to allergies, religious restrictions, or health reasons.

[0098] Input: Any dietary restrictions the user has (e.g., peanut allergy).

[0099] Output: A saved profile of the user's dietary restrictions.

[0100] Step 2:

[0101] Users visit a restaurant and scan the QR code on the menu.

[0102] Specifically, the user launches a dedicated app and uses the smartphone camera to read the QR code.

[0103] Input: QR code containing menu ingredient information.

[0104] Output: Menu ID extracted from QR code.

[0105] Step 3:

[0106] The device sends the menu ID extracted from the QR code and pre-registered dietary restriction information to the server.

[0107] Specifically, the menu ID is extracted using the QR code reader function and sent to the server along with the registered dietary restriction information as an HTTP request.

[0108] Input: Menu ID, user's dietary restrictions.

[0109] Output: HTTP request to the server.

[0110] Step 4:

[0111] The server analyzes the received menu ID and dietary restriction information and retrieves the ingredient information for the corresponding menu from the database.

[0112] Specifically, the server parses the HTTP request body, extracts the menu ID and dietary restriction information, and then generates an SQL query to retrieve detailed ingredient information for the menu from the database.

[0113] Input: Menu ID.

[0114] Output: Ingredient information for the corresponding menu.

[0115] Step 5:

[0116] The server uses a generative AI model to match the acquired ingredient information with the user's dietary restrictions.

[0117] Specifically, it generates an appropriate prompt for the generative AI model and starts the matching process. An example of a prompt is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[0118] Input: Ingredient information, dietary restriction information.

[0119] Output: The relevance result.

[0120] Step 6:

[0121] The server analyzes the results of the suitability determination and the reasons for it, and returns them to the user's terminal.

[0122] Specifically, the server formats the determination result and the reason for it as an HTTP response and sends it to the user's terminal.

[0123] Input: The result of the decision and the reason for it.

[0124] Output: HTTP response to the user's device.

[0125] Step 7:

[0126] The terminal receives the HTTP response from the server and displays the contents to the user.

[0127] Specifically, the device analyzes the HTTP response and extracts the judgment result and reason. The result is displayed to the user as a pop-up notification or in-app message. For example, a message such as "This menu item can be eaten" or "This menu item should not be eaten" is displayed, along with the reason.

[0128] Input: The HTTP response from the server.

[0129] Output: The result and reason for the decision that is displayed to the user.

[0130] (Application example 1)

[0131] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0132] There is a need for a system that allows customers with specific dietary restrictions to easily and accurately check the ingredient information of dishes served when dining out, so that they can eat safely. With conventional systems, checking menu information is cumbersome, which can lead to errors and delays in judgment. Furthermore, there is a challenge in improving user convenience by displaying information in a visually easy-to-understand manner.

[0133] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0134] In this invention, the server includes means for reading a code containing information on ingredients of a dish, means for transmitting the read information on ingredients of the dish to the server, means for the server to compare the information on ingredients of the dish with the dietary restriction information of the user to determine whether the dish is edible, means for notifying the user of the determination result, and means for visually displaying the result using an augmented reality function. This allows users to instantly check the suitability of a menu for their dietary restrictions simply by scanning the QR code, and to visually confirm the result using augmented reality in an easy-to-understand manner.

[0135] "Specific dietary restrictions" are dietary rules for individuals who are required to avoid consuming certain ingredients or foodstuffs due to allergies, illness, religious reasons, etc.

[0136] "User" refers to an individual who uses the system to identify dietary restrictions at a food establishment.

[0137] "Restaurant" means a store or establishment that serves food and beverages.

[0138] "Food" refers to the food and menu served at a restaurant.

[0139] "Ingredient information" is detailed data about the specific ingredients, additives, and nutritional components contained in a dish.

[0140] "Means for reading codes" refers to devices or software that optically decode coded information such as QR codes and barcodes.

[0141] A "server" is a computer system for storing, processing, and communicating data.

[0142] "Dietary Restriction Information" refers to data regarding dietary restrictions registered by a user, including a list of allergens and ingredients to avoid.

[0143] "Augmented reality" is a technology that displays computer-generated information overlaid on the real world.

[0144] "Visual display means" refers to devices or software that use displays or projectors to provide information in a form that can be visually confirmed by the user.

[0145] This invention is a system that allows customers with specific dietary restrictions to enjoy eating out safely. This system is composed of a means for providing information on the customer's dietary restrictions and information on the ingredients of dishes served at restaurants as QR codes, smart glasses or a smartphone for reading the QR codes, a server that processes the read information, and an augmented reality function that visually displays the results.

[0146] System Overview

[0147] First, users install a dedicated application on their smart glasses or smartphone and register their dietary restrictions, including allergies and a list of ingredients they want to avoid.

[0148] Scanning QR codes

[0149] The restaurant menu includes a QR code containing the ingredient information for each dish. Customers use the camera in their smart glasses to scan the QR code, which encodes the dish's menu ID.

[0150] Server-side processing

[0151] When the QR code is scanned, the smart glasses send the menu ID and the user's dietary restriction information to the server. The server retrieves detailed ingredient information for the corresponding dish from a database based on the received menu ID. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information and determine whether the dish meets the user's dietary restrictions.

[0152] Augmented reality results display

[0153] The results are displayed on the user's smart glasses in a visually easy-to-understand format. For example, a message such as "This dish can be eaten" or "This dish should not be eaten" is displayed, along with the reason for the decision. This result display allows the user to safely select a dish.

[0154] Hardware and software used

[0155] Hardware: Smart glasses (camera, AR-enabled display), server, smartphone

[0156] Software: QR code reader, AR display software, database management system, generative AI model

[0157] Specific examples

[0158] For example, if a user scans the QR code for a dish called "Pad Thai," the server retrieves the ingredient information for "Pad Thai," and assumes that the dish contains peanuts. If the generated AI checks the peanut content and determines that the user has a peanut allergy, it will determine that "this dish should not be eaten" and display the result on the smart glasses. The user can then select a dish based on this visual display.

[0159] Prompt Sentence Examples

[0160] User ID: user123

[0161] QR code data: {"menu_id": "th001"}

[0162] User's dietary restrictions: {"allergies": ["shrimp"]}

[0163] Based on this, the server compares the ingredient information with the dietary restriction information and returns the results, allowing users to enjoy eating out safely.

[0164] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0165] Step 1:

[0166] Registering dietary restrictions

[0167] Users use smart glasses or a smartphone to launch a dedicated application and enter their dietary restrictions, including allergies and food items they want to avoid, which are then sent to a server and stored.

[0168] Input: User's dietary restrictions (e.g., allergy information)

[0169] Output: Dietary restrictions stored on the server

[0170] How it works: A user uses an application to provide input, which is received by the server and stored in a database.

[0171] Step 2:

[0172] Scan the menu QR code

[0173] At a restaurant, the user scans the QR code on the menu with the camera in the smart glasses, which retrieves the QR code data that encodes the dish's ingredients.

[0174] Input: Menu QR code

[0175] Output: QR code data (menu ID)

[0176] How it works: The camera in the smart glasses reads the QR code and captures the data.

[0177] Step 3:

[0178] Send QR code data and dietary restriction information

[0179] The smart glasses send the scanned QR code data and the user's pre-registered dietary restriction information to the server.

[0180] Input: QR code data, user's dietary restrictions

[0181] Output: Data sent to the server

[0182] How it works: The smart glasses send data over the network to a server.

[0183] Step 4:

[0184] Get menu ingredient information

[0185] The server analyzes the menu ID based on the received QR code data and retrieves detailed ingredient information for the corresponding dish from the database.

[0186] Input: QR code data (menu ID)

[0187] Output: Food ingredients information

[0188] How it works: The server queries the database and retrieves the ingredients for the corresponding dish.

[0189] Step 5:

[0190] Matching with dietary restrictions

[0191] The server compares the ingredient information of the dish with the user's dietary restrictions and uses an AI model to determine whether the user can eat the dish.

[0192] Input: Ingredients of the dish, user's dietary restrictions

[0193] Output: Judgment result on whether it is edible or not

[0194] How it works: The server's generative AI model performs data calculations and matches ingredient information with dietary restriction information.

[0195] Step 6:

[0196] Notification of results

[0197] The server sends the results to the smart glasses, which then display them in the user's field of vision using augmented reality.

[0198] Input: Judgment result

[0199] Output: Visual results displayed on an augmented reality display

[0200] How it works: The server sends the results to the smart glasses, which then use augmented reality to display the results, such as "This dish is okay to eat" or "This dish should be avoided."

[0201] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0202] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and further improves the user experience by combining it with an emotion engine that recognizes the user's emotions. This system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[0203] overview

[0204] The overall system flow begins when a user uses a smartphone app to scan a QR code attached to a restaurant menu. The QR code contains detailed information about the ingredients of the menu item. The app sends this information to a server, which then compares it with the user's pre-registered dietary restrictions to determine whether a particular menu item is safe. Furthermore, by combining this with an emotion engine, the system provides advice based on the user's emotional state.

[0205] Scan and send QR codes

[0206] The user launches the smartphone app and scans the menu's QR code using the QR code reader. The device then obtains the menu ID and sends it along with the user's dietary restrictions to the server. The emotion engine then recognizes the user's current emotional state and sends that information to the server.

[0207] Server-side processing

[0208] The server receives the menu ID, the user's dietary restriction information, and emotional state information sent from the device. First, it retrieves the ingredient information of the corresponding menu from the database using the menu ID. Then, it uses the generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information. This process determines whether a particular menu is suitable for the user.

[0209] The role of the emotional engine

[0210] The emotion engine uses facial expression and voice recognition technology to evaluate the user's emotional state. For example, if the user is feeling stressed or in a hurry, the server can provide a quicker judgment result. The emotion engine can also provide positive feedback or advice to relax based on the user's emotional state.

[0211] Notification of the results

[0212] After the server has completed its compatibility assessment, it sends the results back to the device, which then analyzes the results and displays them to the user in an appropriate format, along with advice based on the emotional state obtained by the emotion engine.

[0213] Specific examples

[0214] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0215] As described above, the present invention allows users to receive appropriate advice based on their emotional state at the time, as well as whether a meal meets their dietary restrictions, thereby making the dining out experience safer and more fulfilling for the user.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[0219] Step 2:

[0220] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[0221] Step 3:

[0222] The device's emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state. For example, if the user is smiling, it is judged to be "happiness," and if they have a stern expression, it is judged to be "stress."

[0223] Step 4:

[0224] The terminal compiles the menu ID it reads, the user's emotional state, and pre-set dietary restriction information (e.g., allergy information or religious restrictions) and creates a request to the server.

[0225] Step 5:

[0226] The device sends a request to the server, which includes the menu ID, the user's dietary restrictions, and their emotional state.

[0227] Step 6:

[0228] The server receives the request, analyzes the request, extracts the menu ID, and retrieves detailed ingredient information for the corresponding menu from the database.

[0229] Step 7:

[0230] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restrictions. For example, if a user has a peanut allergy, it can check whether the menu contains peanuts.

[0231] Step 8:

[0232] The server generates a menu suitability evaluation result, which includes a judgment such as "This menu is OK to eat" or "This menu should not be eaten" and the reason for the judgment.

[0233] Step 9:

[0234] Based on the evaluation results, the server also provides advice tailored to the user's emotional state. For example, if the user is feeling stressed, the server suggests an alternative menu to help them relax.

[0235] Step 10:

[0236] The server sends the evaluation results and advice from the emotion engine back to the device in a standard format such as JSON.

[0237] Step 11:

[0238] The device analyzes the evaluation results and advice received from the server, and then displays them in an easy-to-read format for the user.

[0239] Step 12:

[0240] The user checks the evaluation results on the device screen. For example, a message such as "You should not eat this menu item because it contains peanuts. However, we recommend this salad menu item."

[0241] Through the above steps, the user can easily and safely check a menu that suits their own dietary restrictions, and at the same time receive appropriate advice that corresponds to their current emotional state.

[0242] Example 2

[0243] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0244] In conventional restaurants, customers with specific dietary restrictions have limited means of checking the ingredient information on the menu, making it difficult for them to determine whether or not they can eat the food. Furthermore, since advice is not provided that takes into account the customer's emotional state, their dining out experience tends to be limited. The goal is to solve these problems.

[0245] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0246] In this invention, the server includes a means for checking the ingredient information of the menu to determine whether it is safe to eat, a means for recognizing the emotional state of the user and providing advice based on that information, and a means for notifying the user of the judgment result and advice, thereby enabling the user to safely select a menu and receive advice according to their emotional state at the time.

[0247] "Menu ingredient information" is data that describes the food ingredients and allergen information contained in each menu item offered at a restaurant.

[0248] "Dietary restriction information" refers to information about foods or ingredients that you are allergic to or that you need to avoid for health reasons.

[0249] A "QR code" is a two-dimensional code that stores data such as menu ingredient information and can be read by devices such as smartphones.

[0250] A "server" is a computer system that receives, analyzes, and transmits data over a network.

[0251] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their current emotional state.

[0252] "Generated artificial intelligence (AI)" is a collection of machine learning models and algorithms used for data analysis and pattern recognition, which in this invention serves to match menu ingredient information with dietary restriction information.

[0253] "Advice" refers to advice or recommendations provided by the system to the user, including feedback based on emotional state in particular.

[0254] "Notification means" refers to a function for communicating the assessment results and advice to the user, and includes screen displays and voice notifications on the smartphone app.

[0255] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and improves the user's dining out experience by combining it with an emotion engine that recognizes the user's emotions. The system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[0256] When a user launches the smartphone app, the QR code reading function becomes available. When the user uses the app's QR code reading function to scan a QR code on a restaurant menu, the device acquires this information. The QR code contains detailed information about the ingredients of the menu item. The device then sends the menu ID obtained as a result of the scan, any dietary restrictions set by the user, and the user's emotional state analyzed by the emotion engine to the server.

[0257] The server receives the menu ID, dietary restriction information, and emotional state information sent from the device. First, the server uses the menu ID to retrieve the ingredient information of the corresponding menu from the database. Then, it uses the generated AI model to match the menu ingredient information with the dietary restriction information. This matching process determines whether a particular menu is suitable for the user. Furthermore, the server analyzes the user's emotional state retrieved by the emotion engine and generates positive advice or relaxation recommendations based on that.

[0258] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated AI model. For example, if the Pad Thai contains peanuts, the server determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, the device provides positive advice such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0259] Throughout the entire system, users can receive appropriate advice based on their emotional state as well as whether the food meets their dietary restrictions, making their dining out experience safer and more fulfilling.

[0260] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0261] Step 1:

[0262] The user launches the smartphone app.

[0263] Input: The app is launched by user action.

[0264] Output: The app home screen will be displayed and the QR code reading function will be available.

[0265] Specific operation: The user launches the app and taps the "Scan QR code" button that appears on the home screen.

[0266] Step 2:

[0267] The device scans the QR code and obtains the menu ID.

[0268] Input: QR code attached to restaurant menu.

[0269] Output: The menu ID obtained from the QR code is stored in temporary memory.

[0270] Specific operation: The device activates the built-in camera and scans the QR code. The app displays the message "Menu ID acquired."

[0271] Step 3:

[0272] The device acquires dietary restriction information and emotional state and transmits them to the server.

[0273] Input: User's dietary restriction information and emotional state information.

[0274] Output: Send the acquired information to the server.

[0275] Specific operation: The device reads the user's dietary restriction information (such as peanut allergies) from memory, and the emotion engine uses the camera and microphone to analyze the user's emotions from their facial expressions and voice. This information is then sent to the server.

[0276] Step 4:

[0277] The server retrieves the menu ingredient information from a database.

[0278] Input: Menu ID sent from the terminal.

[0279] Output: Ingredient information for the corresponding menu.

[0280] Specific operation: The server queries the database using the menu ID and retrieves the ingredient information for the corresponding menu item.

[0281] Step 5:

[0282] The server uses the generated AI model to match menu ingredient information with dietary restriction information.

[0283] Input: Menu ingredient information and user dietary restriction information.

[0284] Output: A decision on whether the menu is suitable for the user.

[0285] How it works: The server runs an AI model to analyze the menu ingredient information and dietary restriction information and determine whether there are any matching ingredients. For example, if the ingredient information includes peanuts, it will evaluate the food as "not suitable."

[0286] Step 6:

[0287] The server uses an emotion engine to generate advice based on the user's emotional state.

[0288] Input: User's emotional state information.

[0289] Output: Advice based on the user's emotional state.

[0290] Specific operation: The server generates advice based on the analysis results of the emotion engine according to the user's emotional state. For example, if the user is feeling stressed, it generates a message to relax.

[0291] Step 7:

[0292] The server returns the judgment result and advice to the terminal.

[0293] Input: Judgment results from the AI ​​model and advice from the emotion engine.

[0294] Output: The judgment result and advice are sent to the terminal.

[0295] Specific operation: The server sends JSON data containing the judgment result and advice to the terminal via HTTP(S).

[0296] Step 8:

[0297] The terminal displays the judgment result and advice to the user.

[0298] Input: Verification result and advice sent from the server.

[0299] Output: Display in a format suitable for the user.

[0300] Specific operation: The device analyzes the received JSON data and displays a message on the user's smartphone screen such as, "Please avoid this menu item as it contains peanuts. We recommend this salad menu item."

[0301] (Application example 2)

[0302] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0303] When choosing a menu at a restaurant, it is difficult for users with specific dietary restrictions to select an appropriate menu that takes into account their emotional state in addition to their dietary restrictions. Conventional systems only consider dietary restrictions, and therefore lack appropriate advice and suggestions based on the user's emotional state. As a result, users often feel stressed and anxious, resulting in a poor dining out experience. This project aims to solve this problem.

[0304] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's emotional state to the server, means for the server to compare the menu ingredient information, the user's dietary restriction information, and emotional state information and use a generative AI model to suggest an appropriate menu, and means for notifying the user of advice based on the judgment results and emotions. This makes it possible to suggest menus that comprehensively take into account the user's dietary restriction information and emotional state, thereby providing a safer and more comfortable dining out experience.

[0305] Key Word Definitions

[0306] "Specific dietary restrictions" refers to specific ingredients or foods that a user should avoid due to allergies or health conditions.

[0307] "Menu ingredient information" refers to data that indicates all ingredients and their details contained in each menu item served at a restaurant.

[0308] "Code" refers to symbols, barcodes, and especially QR codes, that convert information, including menu ingredient information, into a form that can be read by a smartphone or other device.

[0309] "Emotional state" refers to the psychological state, such as stress or happiness, that the user is feeling when selecting a menu, and is analyzed from facial expressions and voice.

[0310] A "generative AI model" is a machine learning method that uses data to suggest menus that are appropriate for the user, and in particular uses artificial intelligence technologies such as neural networks.

[0311] A "server" is a central processing unit that processes data sent from a client terminal and provides necessary information.

[0312] "Advice" refers to recommendations about what meals are suitable or should be avoided based on the user's dietary restrictions and emotional state.

[0313] "Method of reading" refers to the method of obtaining information from a QR code using a smartphone camera or a dedicated scanner.

[0314] "Means of notification" refers to the functions and methods for conveying information to users through devices such as smartphones and tablets.

[0315] "Matching" refers to the process by which the server compares the menu ingredient information with the user's dietary restrictions and / or emotional state information to confirm a match.

[0316] MODE FOR CARRYING OUT THE INVENTION

[0317] The system for implementing this invention is composed of a smartphone app, a server, an emotion engine, and a QR code provided at a restaurant. A specific embodiment of the system is shown below.

[0318] Hardware and Software Configuration

[0319] Hardware

[0320] Smartphone: A device that allows users to use applications.

[0321] QR Code Reader: A function to read QR codes using a smartphone camera.

[0322] Emotion recognition system: Uses the smartphone's camera and microphone to analyze the user's facial expressions and voice.

[0323] software

[0324] Smartphone app: An application that runs on iOS and ANDROID (registered trademark).

[0325] Emotion engine: An API for analyzing the user's emotional state, such as Google® Cloud's Face API or Microsoft® Azure®'s Emotion API.

[0326] Server-side programs: Web servers such as Apache (registered trademark) or NGINX, and back-end programs using Python or Node.js.

[0327] Generative AI model: A machine learning model using Tensorflow (registered trademark) or similar, used to make menu suggestions based on analysis results.

[0328] Process Overview

[0329] Smartphone app

[0330] 1. The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code.

[0331] 2. The smartphone app obtains the menu ID from the scanned QR code and sends it to the server.

[0332] 3. At the same time, the smartphone's camera and microphone capture the user's facial expressions and voice and send them to the emotion engine.

[0333] Server Side

[0334] 1. The server retrieves ingredient information from the database based on the received menu ID.

[0335] 2. The user's dietary restriction information is also obtained from a pre-registered database.

[0336] 3. The emotional state analyzed by the emotion engine is obtained and data is sent to the generative AI model based on this.

[0337] 4. The generative AI model collates ingredient information, dietary restriction information, and emotional state information to generate optimal menu suggestions and emotion-based advice.

[0338] 5. The server sends the generated suggestions and advice back to the smartphone app.

[0339] User Experience

[0340] 1. Shows whether the menu the user scans is suitable for their dietary restrictions and how the menu selection affects the user's emotional state.

[0341] 2. If necessary, we will also provide positive advice and suggest menus that will promote relaxation.

[0342] Specific examples

[0343] For example, consider the case where a user scans a QR code for a "Pad Thai" menu. The user scans the QR code with a smartphone app, and the device sends the "Pad Thai" menu ID and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0344] Prompt Sentence Examples

[0345] Use the following prompt for the generative AI model:

[0346] A user scanned the QR code of a menu item selected at a restaurant. The user's dietary restrictions are: {'Allergies': 'Peanuts'}. The user's current emotional state is: {'Emotion': 'Stressed'}. Generate menu suggestions and advice. Menu ID: '12345'. Ingredients: {'Peanuts': True, ...}.

[0347] Based on this prompt, appropriate menu suggestions and sentiment-based advice can be generated.

[0348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0349] Program processing steps

[0350] Step 1:

[0351] The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code. The input is the QR code, and the output is the menu ID. This menu ID is sent to the server in the next step.

[0352] Step 2:

[0353] The device sends the acquired menu ID and the user's dietary restriction information to the server. At the same time, the device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine. The input is the menu ID, dietary restriction information, and emotion data, which are then processed in the next step.

[0354] Step 3:

[0355] The server retrieves the ingredient information of the corresponding menu from the database based on the received menu ID. The input is the menu ID, and the output is ingredient information. This ingredient information is used in the next step.

[0356] Step 4:

[0357] The server retrieves the user's dietary restriction information from the database. The input is the user ID, and the output is the dietary restriction information. This information is then collated in the next step.

[0358] Step 5:

[0359] The server receives the user's emotional state information from the emotion engine. The input is the captured facial and voice data, and the output is the emotional state information, which is then sent to the generative AI model in the next step.

[0360] Step 6:

[0361] The server inputs ingredient information, dietary restriction information, and emotional state information into a generative AI model to generate appropriate menu suggestions and emotional advice. The inputs are the above three pieces of information, and the output is a suggested menu and advice. This generative AI model is built using TensorFlow and other tools.

[0362] Step 7:

[0363] The server returns the generated suggestion menu and advice to the terminal. The input is the suggestion menu and advice, and the output is displayed on the terminal.

[0364] Step 8:

[0365] The device displays the received suggested menu and advice to the user. The input is the suggested menu and advice, and the output is displayed on the user's smartphone screen. In concrete terms, the user can select a menu based on the displayed information.

[0366] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0367] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0368] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0369] [Second embodiment]

[0370] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0371] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0372] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0373] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0374] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0376] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0377] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0378] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0379] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0380] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0381] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0382] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat certain foods based on the ingredient information included in the menus offered by restaurants when dining out. This system is configured using the user's smartphone, a server, and QR codes provided by restaurants.

[0383] overview

[0384] First, users install a dedicated application on their smartphone. This application has the function of registering the user's dietary restrictions in advance. Next, each menu item offered by a restaurant is affixed with a QR code containing information about the ingredients of the menu item. When users dine, they can easily check whether they can eat the item by scanning this QR code with the smartphone app.

[0385] Scan and send QR codes

[0386] When a user scans a QR code using a smartphone app, the device transmits the menu ID and the user's dietary restriction information obtained from the QR code to the server. At this time, the QR code contains detailed ingredient information for each menu item, so the menu ID can be obtained by decoding it.

[0387] Server-side processing

[0388] The server receives the menu ID and the user's dietary restriction information sent from the device. The server first retrieves detailed ingredient information for the menu item from the database. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information. For example, if a menu item contains peanuts, the server will determine that a user with a peanut allergy should not eat that menu item.

[0389] Notification of the results

[0390] After the server has completed its compatibility assessment, it sends the results back to the device. The device receives the results and displays them to the user. The results include a message such as "This menu item can be eaten" or "This menu item should not be eaten," along with the reason for the decision.

[0391] Specific examples

[0392] For example, consider a scenario where a user scans the QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) to the server. The server retrieves the ingredient information for "Pad Thai" from a database and checks whether it contains peanuts. The generated AI detects the presence of peanuts, compares it with the dietary restriction information, determines that "this menu item should not be eaten," and sends the result back to the device. The device displays this result to the user, who can then change their selection with peace of mind.

[0393] As a result, we can provide a system that ensures the safety of eating out for people with dietary restrictions and supports easy menu selection. This system can efficiently manage and compare user dietary restriction information and menu ingredient information, achieving high reliability and speed.

[0394] The processing flow will be explained below.

[0395] Step 1:

[0396] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[0397] Step 2:

[0398] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[0399] Step 3:

[0400] The menu ID read by the terminal is combined with the user's pre-set dietary restriction information (for example, allergy information or religious restrictions).

[0401] Step 4:

[0402] The device creates a request to the server, which includes the menu ID and the user's dietary restrictions.

[0403] Step 5:

[0404] The request created by the device is sent to the server. When the server receives the request, it first analyzes the contents of the request.

[0405] Step 6:

[0406] The server uses the menu ID to retrieve detailed ingredient information for the corresponding menu from the database, including each ingredient and cooking method.

[0407] Step 7:

[0408] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restriction information, and the AI ​​evaluates whether the menu contains ingredients that are incompatible with the user's dietary restrictions.

[0409] Step 8:

[0410] The server generates a suitability evaluation result, which includes a judgment such as "This menu item is OK to eat" or "This menu item should not be eaten" and the reason for the judgment.

[0411] Step 9:

[0412] The server returns the evaluation results to the device, which are sent in a standard format such as JSON.

[0413] Step 10:

[0414] The terminal analyzes the evaluation results received from the server and displays them in an easy-to-read format for the user.

[0415] Step 11:

[0416] The user checks the evaluation results on the device screen. For example, "This menu item should not be eaten. Reason: It contains peanuts" is displayed.

[0417] By following the steps above, the user can easily and safely check menus that meet their own dietary restrictions.

[0418] Example 1

[0419] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0420] To enable customers with specific dietary restrictions to quickly and accurately determine whether they can eat a certain food when dining out based on the ingredient information included in the menu. Currently, it is time-consuming to check the ingredient information individually, and there is a risk of accidentally ingesting something, so there is a need for a system that solves this problem.

[0421] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0422] In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's dietary restriction information to the server, means for the server to use generated artificial intelligence to compare the menu ingredient information with the user's dietary restriction information to determine whether the food is permissible to eat, and means for notifying the user of the determination result and reason. This allows users to easily check the ingredient information of menus offered when dining out and select meals that suit their own dietary restrictions.

[0423] "Users with specific dietary restrictions" are users who cannot consume certain foods or ingredients due to allergies, religious restrictions, or health reasons.

[0424] "Codes containing menu ingredient information" refer to codes such as QR codes or barcodes that encode and include detailed ingredient information for menu items offered by restaurants.

[0425] "Means for reading" refers to the functionality or equipment for scanning the code and deciphering its contents using a camera and dedicated application installed on a device such as a smartphone or tablet.

[0426] "Means for sending to server" refers to communication means or protocols for transferring the read menu ingredient information and the user's dietary restriction information to a remote server via the Internet or the like.

[0427] "Generated artificial intelligence" refers to AI technologies such as machine learning models and deep learning models that are generated by programs to perform specific data processing and analysis.

[0428] The "means for determining" refers to the algorithms and processing logic that use a generative AI model within the server to compare menu ingredient information with the user's dietary restriction information and evaluate their suitability.

[0429] "Means of notification" refers to means for providing information to users, such as a function that displays the judgment results and the reasons for them on the user's device, or by means of push notifications or in-app messages.

[0430] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on the ingredient information of the menu items offered at restaurants. The system is configured using the user's smartphone, a server, and QR codes provided by the restaurant.

[0431] First, users install a dedicated application on their smartphone. This application has a function to register their dietary restrictions in advance. Specifically, users can input information about certain ingredients that they cannot consume due to allergies, religious restrictions, or health reasons.

[0432] Next, each menu item offered by a restaurant is affixed with a QR code containing the menu's ingredient information. The QR code encodes the menu ID and detailed ingredient information. When dining out, users can simply scan this QR code with a smartphone app to easily check whether they can eat the item.

[0433] When a user scans a QR code using a smartphone app, the device extracts the menu ID from the QR code and sends it to the server along with the user's dietary restriction information. The specific processing performed by the device includes a camera with QR code reading functionality and an application. The read data is then sent to the server via the Internet using protocols such as HTTP.

[0434] The server receives the menu ID and the user's dietary restriction information sent from the terminal. The server first retrieves detailed ingredient information for the menu item from the database. Next, it compares the ingredient information retrieved using the generative AI model with the user's dietary restriction information. An example of a prompt for the generative AI model is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[0435] The server analyzes the output of the generative AI model and determines whether the menu is suitable for the user. For example, if the ingredient information includes peanuts, the server generates a result that says "This menu should not be eaten" for a user with a peanut allergy.

[0436] The server then returns the judgment result (and reason) to the terminal. The judgment result is sent as an HTTP response, and the terminal receives the response from the server and analyzes its contents. Finally, the terminal displays the judgment result to the user. For example, a message such as "You can eat this menu item" or "You should not eat this menu item" along with the reason for the decision is displayed. This allows the user to make a meal selection with peace of mind.

[0437] For example, if a user scans the QR code for a menu item called "Pad Thai," the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) from the QR code to the server. The server retrieves the ingredient information for "Pad Thai" from the database and determines suitability using a generative AI model. As a result, the device sends a message back to the device saying, "This menu item contains peanuts and is not suitable for you," and the device displays that information to the user.

[0438] The purpose of this system is to enable people with dietary restrictions to select menu items with peace of mind when eating out. The system configuration effectively combines the user's device, server, and QR codes provided by restaurants, achieving high reliability and speed.

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] Users install a dedicated application on their smartphone and register their dietary restrictions.

[0442] Specifically, users download the app from the application store, install it, and then launch it. After launching the app, they can enter and save information about certain foods and ingredients they cannot consume due to allergies, religious restrictions, or health reasons.

[0443] Input: Any dietary restrictions the user has (e.g., peanut allergy).

[0444] Output: A saved profile of the user's dietary restrictions.

[0445] Step 2:

[0446] Users visit a restaurant and scan the QR code on the menu.

[0447] Specifically, the user launches a dedicated app and uses the smartphone camera to read the QR code.

[0448] Input: QR code containing menu ingredient information.

[0449] Output: Menu ID extracted from QR code.

[0450] Step 3:

[0451] The device sends the menu ID extracted from the QR code and pre-registered dietary restriction information to the server.

[0452] Specifically, the menu ID is extracted using the QR code reader function and sent to the server along with the registered dietary restriction information as an HTTP request.

[0453] Input: Menu ID, user's dietary restrictions.

[0454] Output: HTTP request to the server.

[0455] Step 4:

[0456] The server analyzes the received menu ID and dietary restriction information and retrieves the ingredient information for the corresponding menu from the database.

[0457] Specifically, the server parses the HTTP request body, extracts the menu ID and dietary restriction information, and then generates an SQL query to retrieve detailed ingredient information for the menu from the database.

[0458] Input: Menu ID.

[0459] Output: Ingredient information for the corresponding menu.

[0460] Step 5:

[0461] The server uses a generative AI model to match the acquired ingredient information with the user's dietary restrictions.

[0462] Specifically, it generates an appropriate prompt for the generative AI model and starts the matching process. An example of a prompt is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[0463] Input: Ingredient information, dietary restriction information.

[0464] Output: The relevance result.

[0465] Step 6:

[0466] The server analyzes the results of the suitability determination and the reasons for it, and returns them to the user's terminal.

[0467] Specifically, the server formats the determination result and the reason for it as an HTTP response and sends it to the user's terminal.

[0468] Input: The result of the decision and the reason for it.

[0469] Output: HTTP response to the user's device.

[0470] Step 7:

[0471] The terminal receives the HTTP response from the server and displays the contents to the user.

[0472] Specifically, the device analyzes the HTTP response and extracts the judgment result and reason. The result is displayed to the user as a pop-up notification or in-app message. For example, a message such as "This menu item can be eaten" or "This menu item should not be eaten" is displayed, along with the reason.

[0473] Input: The HTTP response from the server.

[0474] Output: The result and reason for the decision that is displayed to the user.

[0475] (Application example 1)

[0476] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0477] There is a need for a system that allows customers with specific dietary restrictions to easily and accurately check the ingredient information of dishes served when dining out, so that they can eat safely. With conventional systems, checking menu information is cumbersome, which can lead to errors and delays in judgment. Furthermore, there is a challenge in improving user convenience by displaying information in a visually easy-to-understand manner.

[0478] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0479] In this invention, the server includes means for reading a code containing information on ingredients of a dish, means for transmitting the read information on ingredients of the dish to the server, means for the server to compare the information on ingredients of the dish with the dietary restriction information of the user to determine whether the dish is edible, means for notifying the user of the determination result, and means for visually displaying the result using an augmented reality function. This allows users to instantly check the suitability of a menu for their dietary restrictions simply by scanning the QR code, and to visually confirm the result using augmented reality in an easy-to-understand manner.

[0480] "Specific dietary restrictions" are dietary rules for individuals who are required to avoid consuming certain ingredients or foodstuffs due to allergies, illness, religious reasons, etc.

[0481] "User" refers to an individual who uses the system to identify dietary restrictions at a food establishment.

[0482] "Restaurant" means a store or establishment that serves food and beverages.

[0483] "Food" refers to the food and menu served at a restaurant.

[0484] "Ingredient information" is detailed data about the specific ingredients, additives, and nutritional components contained in a dish.

[0485] "Means for reading codes" refers to devices or software that optically decode coded information such as QR codes and barcodes.

[0486] A "server" is a computer system for storing, processing, and communicating data.

[0487] "Dietary Restriction Information" refers to data regarding dietary restrictions registered by a user, including a list of allergens and ingredients to avoid.

[0488] "Augmented reality" is a technology that displays computer-generated information overlaid on the real world.

[0489] "Visual display means" refers to devices or software that use displays or projectors to provide information in a form that can be visually confirmed by the user.

[0490] This invention is a system that allows customers with specific dietary restrictions to enjoy eating out safely. This system is composed of a means for providing information on the customer's dietary restrictions and information on the ingredients of dishes served at restaurants as QR codes, smart glasses or a smartphone for reading the QR codes, a server that processes the read information, and an augmented reality function that visually displays the results.

[0491] System Overview

[0492] First, users install a dedicated application on their smart glasses or smartphone and register their dietary restrictions, including allergies and a list of ingredients they want to avoid.

[0493] Scanning QR codes

[0494] The restaurant menu includes a QR code containing the ingredient information for each dish. Customers use the camera in their smart glasses to scan the QR code, which encodes the dish's menu ID.

[0495] Server-side processing

[0496] When the QR code is scanned, the smart glasses send the menu ID and the user's dietary restriction information to the server. The server retrieves detailed ingredient information for the corresponding dish from a database based on the received menu ID. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information and determine whether the dish meets the user's dietary restrictions.

[0497] Augmented reality results display

[0498] The results are displayed on the user's smart glasses in a visually easy-to-understand format. For example, a message such as "This dish can be eaten" or "This dish should not be eaten" is displayed, along with the reason for the decision. This result display allows the user to safely select a dish.

[0499] Hardware and software used

[0500] Hardware: Smart glasses (camera, AR-enabled display), server, smartphone

[0501] Software: QR code reader, AR display software, database management system, generative AI model

[0502] Specific examples

[0503] For example, if a user scans the QR code for a dish called "Pad Thai," the server retrieves the ingredient information for "Pad Thai," and assumes that the dish contains peanuts. If the generated AI checks the peanut content and determines that the user has a peanut allergy, it will determine that "this dish should not be eaten" and display the result on the smart glasses. The user can then select a dish based on this visual display.

[0504] Prompt Sentence Examples

[0505] User ID: user123

[0506] QR code data: {"menu_id": "th001"}

[0507] User's dietary restrictions: {"allergies": ["shrimp"]}

[0508] Based on this, the server compares the ingredient information with the dietary restriction information and returns the results, allowing users to enjoy eating out safely.

[0509] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0510] Step 1:

[0511] Registering dietary restrictions

[0512] Users use smart glasses or a smartphone to launch a dedicated application and enter their dietary restrictions, including allergies and food items they want to avoid, which are then sent to a server and stored.

[0513] Input: User's dietary restrictions (e.g., allergy information)

[0514] Output: Dietary restrictions stored on the server

[0515] How it works: A user uses an application to provide input, which is received by the server and stored in a database.

[0516] Step 2:

[0517] Scan the menu QR code

[0518] At a restaurant, the user scans the QR code on the menu with the camera in the smart glasses, which retrieves the QR code data that encodes the dish's ingredients.

[0519] Input: Menu QR code

[0520] Output: QR code data (menu ID)

[0521] How it works: The camera in the smart glasses reads the QR code and captures the data.

[0522] Step 3:

[0523] Send QR code data and dietary restriction information

[0524] The smart glasses send the scanned QR code data and the user's pre-registered dietary restriction information to the server.

[0525] Input: QR code data, user's dietary restrictions

[0526] Output: Data sent to the server

[0527] How it works: The smart glasses send data over the network to a server.

[0528] Step 4:

[0529] Get menu ingredient information

[0530] The server analyzes the menu ID based on the received QR code data and retrieves detailed ingredient information for the corresponding dish from the database.

[0531] Input: QR code data (menu ID)

[0532] Output: Food ingredients information

[0533] How it works: The server queries the database and retrieves the ingredients for the corresponding dish.

[0534] Step 5:

[0535] Matching with dietary restrictions

[0536] The server compares the ingredient information of the dish with the user's dietary restrictions and uses an AI model to determine whether the user can eat the dish.

[0537] Input: Ingredients of the dish, user's dietary restrictions

[0538] Output: Judgment result on whether it is edible or not

[0539] How it works: The server's generative AI model performs data calculations and matches ingredient information with dietary restriction information.

[0540] Step 6:

[0541] Notification of results

[0542] The server sends the results to the smart glasses, which then display them in the user's field of vision using augmented reality.

[0543] Input: Judgment result

[0544] Output: Visual results displayed on an augmented reality display

[0545] How it works: The server sends the results to the smart glasses, which then use augmented reality to display the results, such as "This dish is okay to eat" or "This dish should be avoided."

[0546] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0547] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and further improves the user experience by combining it with an emotion engine that recognizes the user's emotions. This system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[0548] overview

[0549] The overall system flow begins when a user uses a smartphone app to scan a QR code attached to a restaurant menu. The QR code contains detailed information about the ingredients of the menu item. The app sends this information to a server, which then compares it with the user's pre-registered dietary restrictions to determine whether a particular menu item is safe. Furthermore, by combining this with an emotion engine, the system provides advice based on the user's emotional state.

[0550] Scan and send QR codes

[0551] The user launches the smartphone app and scans the menu's QR code using the QR code reader. The device then obtains the menu ID and sends it along with the user's dietary restrictions to the server. The emotion engine then recognizes the user's current emotional state and sends that information to the server.

[0552] Server-side processing

[0553] The server receives the menu ID, the user's dietary restriction information, and emotional state information sent from the device. First, it retrieves the ingredient information of the corresponding menu from the database using the menu ID. Then, it uses the generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information. This process determines whether a particular menu is suitable for the user.

[0554] The role of the emotional engine

[0555] The emotion engine uses facial expression and voice recognition technology to evaluate the user's emotional state. For example, if the user is feeling stressed or in a hurry, the server can provide a quicker judgment result. The emotion engine can also provide positive feedback or advice to relax based on the user's emotional state.

[0556] Notification of the results

[0557] After the server has completed its compatibility assessment, it sends the results back to the device, which then analyzes the results and displays them to the user in an appropriate format, along with advice based on the emotional state obtained by the emotion engine.

[0558] Specific examples

[0559] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0560] As described above, the present invention allows users to receive appropriate advice based on their emotional state at the time, as well as whether a meal meets their dietary restrictions, thereby making the dining out experience safer and more fulfilling for the user.

[0561] The processing flow will be explained below.

[0562] Step 1:

[0563] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[0564] Step 2:

[0565] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[0566] Step 3:

[0567] The device's emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state. For example, if the user is smiling, it is judged to be "happiness," and if they have a stern expression, it is judged to be "stress."

[0568] Step 4:

[0569] The terminal compiles the menu ID it reads, the user's emotional state, and pre-set dietary restriction information (e.g., allergy information or religious restrictions) and creates a request to the server.

[0570] Step 5:

[0571] The device sends a request to the server, which includes the menu ID, the user's dietary restrictions, and their emotional state.

[0572] Step 6:

[0573] The server receives the request, analyzes the request, extracts the menu ID, and retrieves detailed ingredient information for the corresponding menu from the database.

[0574] Step 7:

[0575] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restrictions. For example, if a user has a peanut allergy, it can check whether the menu contains peanuts.

[0576] Step 8:

[0577] The server generates a menu suitability evaluation result, which includes a judgment such as "This menu is OK to eat" or "This menu should not be eaten" and the reason for the judgment.

[0578] Step 9:

[0579] Based on the evaluation results, the server also provides advice tailored to the user's emotional state. For example, if the user is feeling stressed, the server suggests an alternative menu to help them relax.

[0580] Step 10:

[0581] The server sends the evaluation results and advice from the emotion engine back to the device in a standard format such as JSON.

[0582] Step 11:

[0583] The device analyzes the evaluation results and advice received from the server, and then displays them in an easy-to-read format for the user.

[0584] Step 12:

[0585] The user checks the evaluation results on the device screen. For example, a message such as "You should not eat this menu item because it contains peanuts. However, we recommend this salad menu item."

[0586] Through the above steps, the user can easily and safely check a menu that suits their own dietary restrictions, and at the same time receive appropriate advice that corresponds to their current emotional state.

[0587] Example 2

[0588] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0589] In conventional restaurants, customers with specific dietary restrictions have limited means of checking the ingredient information on the menu, making it difficult for them to determine whether or not they can eat the food. Furthermore, since advice is not provided that takes into account the customer's emotional state, their dining out experience tends to be limited. The goal is to solve these problems.

[0590] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0591] In this invention, the server includes a means for checking the ingredient information of the menu to determine whether it is safe to eat, a means for recognizing the emotional state of the user and providing advice based on that information, and a means for notifying the user of the judgment result and advice, thereby enabling the user to safely select a menu and receive advice according to their emotional state at the time.

[0592] "Menu ingredient information" is data that describes the food ingredients and allergen information contained in each menu item offered at a restaurant.

[0593] "Dietary restriction information" refers to information about foods or ingredients that you are allergic to or that you need to avoid for health reasons.

[0594] A "QR code" is a two-dimensional code that stores data such as menu ingredient information and can be read by devices such as smartphones.

[0595] A "server" is a computer system that receives, analyzes, and transmits data over a network.

[0596] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their current emotional state.

[0597] "Generated artificial intelligence (AI)" is a collection of machine learning models and algorithms used for data analysis and pattern recognition, which in this invention serves to match menu ingredient information with dietary restriction information.

[0598] "Advice" refers to advice or recommendations provided by the system to the user, including feedback based on emotional state in particular.

[0599] "Notification means" refers to a function for communicating the assessment results and advice to the user, and includes screen displays and voice notifications on the smartphone app.

[0600] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and improves the user's dining out experience by combining it with an emotion engine that recognizes the user's emotions. The system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[0601] When a user launches the smartphone app, the QR code reading function becomes available. When the user uses the app's QR code reading function to scan a QR code on a restaurant menu, the device acquires this information. The QR code contains detailed information about the ingredients of the menu item. The device then sends the menu ID obtained as a result of the scan, any dietary restrictions set by the user, and the user's emotional state analyzed by the emotion engine to the server.

[0602] The server receives the menu ID, dietary restriction information, and emotional state information sent from the device. First, the server uses the menu ID to retrieve the ingredient information of the corresponding menu from the database. Then, it uses the generated AI model to match the menu ingredient information with the dietary restriction information. This matching process determines whether a particular menu is suitable for the user. Furthermore, the server analyzes the user's emotional state retrieved by the emotion engine and generates positive advice or relaxation recommendations based on that.

[0603] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated AI model. For example, if the Pad Thai contains peanuts, the server determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, the device provides positive advice such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0604] Throughout the entire system, users can receive appropriate advice based on their emotional state as well as whether the food meets their dietary restrictions, making their dining out experience safer and more fulfilling.

[0605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0606] Step 1:

[0607] The user launches the smartphone app.

[0608] Input: The app is launched by user action.

[0609] Output: The app home screen will be displayed and the QR code reading function will be available.

[0610] Specific operation: The user launches the app and taps the "Scan QR code" button that appears on the home screen.

[0611] Step 2:

[0612] The device scans the QR code and obtains the menu ID.

[0613] Input: QR code attached to restaurant menu.

[0614] Output: The menu ID obtained from the QR code is stored in temporary memory.

[0615] Specific operation: The device activates the built-in camera and scans the QR code. The app displays the message "Menu ID acquired."

[0616] Step 3:

[0617] The device acquires dietary restriction information and emotional state and transmits them to the server.

[0618] Input: User's dietary restriction information and emotional state information.

[0619] Output: Send the acquired information to the server.

[0620] Specific operation: The device reads the user's dietary restriction information (such as peanut allergies) from memory, and the emotion engine uses the camera and microphone to analyze the user's emotions from their facial expressions and voice. This information is then sent to the server.

[0621] Step 4:

[0622] The server retrieves the menu ingredient information from a database.

[0623] Input: Menu ID sent from the terminal.

[0624] Output: Ingredient information for the corresponding menu.

[0625] Specific operation: The server queries the database using the menu ID and retrieves the ingredient information for the corresponding menu item.

[0626] Step 5:

[0627] The server uses the generated AI model to match menu ingredient information with dietary restriction information.

[0628] Input: Menu ingredient information and user dietary restriction information.

[0629] Output: A decision on whether the menu is suitable for the user.

[0630] How it works: The server runs an AI model to analyze the menu ingredient information and dietary restriction information and determine whether there are any matching ingredients. For example, if the ingredient information includes peanuts, it will evaluate the food as "not suitable."

[0631] Step 6:

[0632] The server uses an emotion engine to generate advice based on the user's emotional state.

[0633] Input: User's emotional state information.

[0634] Output: Advice based on the user's emotional state.

[0635] Specific operation: The server generates advice based on the analysis results of the emotion engine according to the user's emotional state. For example, if the user is feeling stressed, it generates a message to relax.

[0636] Step 7:

[0637] The server returns the judgment result and advice to the terminal.

[0638] Input: Judgment results from the AI ​​model and advice from the emotion engine.

[0639] Output: The judgment result and advice are sent to the terminal.

[0640] Specific operation: The server sends JSON data containing the judgment result and advice to the terminal via HTTP(S).

[0641] Step 8:

[0642] The terminal displays the judgment result and advice to the user.

[0643] Input: Verification result and advice sent from the server.

[0644] Output: Display in a format suitable for the user.

[0645] Specific operation: The device analyzes the received JSON data and displays a message on the user's smartphone screen such as, "Please avoid this menu item as it contains peanuts. We recommend this salad menu item."

[0646] (Application example 2)

[0647] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] When choosing a menu at a restaurant, it is difficult for users with specific dietary restrictions to select an appropriate menu that takes into account their emotional state in addition to their dietary restrictions. Conventional systems only consider dietary restrictions, and therefore lack appropriate advice and suggestions based on the user's emotional state. As a result, users often feel stressed and anxious, resulting in a poor dining out experience. This project aims to solve this problem.

[0649] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's emotional state to the server, means for the server to compare the menu ingredient information, the user's dietary restriction information, and emotional state information and use a generative AI model to suggest an appropriate menu, and means for notifying the user of advice based on the judgment results and emotions. This makes it possible to suggest menus that comprehensively take into account the user's dietary restriction information and emotional state, thereby providing a safer and more comfortable dining out experience.

[0650] Key Word Definitions

[0651] "Specific dietary restrictions" refers to specific ingredients or foods that a user should avoid due to allergies or health conditions.

[0652] "Menu ingredient information" refers to data that indicates all ingredients and their details contained in each menu item served at a restaurant.

[0653] "Code" refers to symbols, barcodes, and especially QR codes, that convert information, including menu ingredient information, into a form that can be read by a smartphone or other device.

[0654] "Emotional state" refers to the psychological state, such as stress or happiness, that the user is feeling when selecting a menu, and is analyzed from facial expressions and voice.

[0655] A "generative AI model" is a machine learning method that uses data to suggest menus that are appropriate for the user, and in particular uses artificial intelligence technologies such as neural networks.

[0656] A "server" is a central processing unit that processes data sent from a client terminal and provides necessary information.

[0657] "Advice" refers to recommendations about what meals are suitable or should be avoided based on the user's dietary restrictions and emotional state.

[0658] "Method of reading" refers to the method of obtaining information from a QR code using a smartphone camera or a dedicated scanner.

[0659] "Means of notification" refers to the functions and methods for conveying information to users through devices such as smartphones and tablets.

[0660] "Matching" refers to the process by which the server compares the menu ingredient information with the user's dietary restrictions and / or emotional state information to confirm a match.

[0661] MODE FOR CARRYING OUT THE INVENTION

[0662] The system for implementing this invention is composed of a smartphone app, a server, an emotion engine, and a QR code provided at a restaurant. A specific embodiment of the system is shown below.

[0663] Hardware and Software Configuration

[0664] Hardware

[0665] Smartphone: A device that allows users to use applications.

[0666] QR Code Reader: A function to read QR codes using a smartphone camera.

[0667] Emotion recognition system: Uses the smartphone's camera and microphone to analyze the user's facial expressions and voice.

[0668] software

[0669] Smartphone app: Application that works on iOS and Android.

[0670] Emotion engine: An API for analyzing the user's emotional state, such as Google Cloud's Face API or Microsoft Azure's Emotion API.

[0671] Server-side programs: Web servers such as Apache or NGINX, and back-end programs using Python or Node.js.

[0672] Generative AI model: A machine learning model using TensorFlow, etc., used to make menu suggestions based on the analysis results.

[0673] Process Overview

[0674] Smartphone app

[0675] 1. The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code.

[0676] 2. The smartphone app obtains the menu ID from the scanned QR code and sends it to the server.

[0677] 3. At the same time, the smartphone's camera and microphone capture the user's facial expressions and voice and send them to the emotion engine.

[0678] Server Side

[0679] 1. The server retrieves ingredient information from the database based on the received menu ID.

[0680] 2. The user's dietary restriction information is also obtained from a pre-registered database.

[0681] 3. The emotional state analyzed by the emotion engine is obtained and data is sent to the generative AI model based on this.

[0682] 4. The generative AI model collates ingredient information, dietary restriction information, and emotional state information to generate optimal menu suggestions and emotion-based advice.

[0683] 5. The server sends the generated suggestions and advice back to the smartphone app.

[0684] User Experience

[0685] 1. Shows whether the menu the user scans is suitable for their dietary restrictions and how the menu selection affects the user's emotional state.

[0686] 2. If necessary, we will also provide positive advice and suggest menus that will promote relaxation.

[0687] Specific examples

[0688] For example, consider the case where a user scans a QR code for a "Pad Thai" menu. The user scans the QR code with a smartphone app, and the device sends the "Pad Thai" menu ID and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0689] Prompt Sentence Examples

[0690] Use the following prompt for the generative AI model:

[0691] A user scanned the QR code of a menu item selected at a restaurant. The user's dietary restrictions are: {'Allergies': 'Peanuts'}. The user's current emotional state is: {'Emotion': 'Stressed'}. Generate menu suggestions and advice. Menu ID: '12345'. Ingredients: {'Peanuts': True, ...}.

[0692] Based on this prompt, appropriate menu suggestions and sentiment-based advice can be generated.

[0693] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0694] Program processing steps

[0695] Step 1:

[0696] The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code. The input is the QR code, and the output is the menu ID. This menu ID is sent to the server in the next step.

[0697] Step 2:

[0698] The device sends the acquired menu ID and the user's dietary restriction information to the server. At the same time, the device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine. The input is the menu ID, dietary restriction information, and emotion data, which are then processed in the next step.

[0699] Step 3:

[0700] The server retrieves the ingredient information of the corresponding menu from the database based on the received menu ID. The input is the menu ID, and the output is ingredient information. This ingredient information is used in the next step.

[0701] Step 4:

[0702] The server retrieves the user's dietary restriction information from the database. The input is the user ID, and the output is the dietary restriction information. This information is then collated in the next step.

[0703] Step 5:

[0704] The server receives the user's emotional state information from the emotion engine. The input is the captured facial and voice data, and the output is the emotional state information, which is then sent to the generative AI model in the next step.

[0705] Step 6:

[0706] The server inputs ingredient information, dietary restriction information, and emotional state information into a generative AI model to generate appropriate menu suggestions and emotional advice. The inputs are the above three pieces of information, and the output is a suggested menu and advice. This generative AI model is built using TensorFlow and other tools.

[0707] Step 7:

[0708] The server returns the generated suggestion menu and advice to the terminal. The input is the suggestion menu and advice, and the output is displayed on the terminal.

[0709] Step 8:

[0710] The device displays the received suggested menu and advice to the user. The input is the suggested menu and advice, and the output is displayed on the user's smartphone screen. In concrete terms, the user can select a menu based on the displayed information.

[0711] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0712] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0713] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0714] [Third embodiment]

[0715] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0716] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0717] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0718] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0719] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0720] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0721] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0722] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0723] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0724] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0725] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0726] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0727] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat certain foods based on the ingredient information included in the menus offered by restaurants when dining out. This system is configured using the user's smartphone, a server, and QR codes provided by restaurants.

[0728] overview

[0729] First, users install a dedicated application on their smartphone. This application has the function of registering the user's dietary restrictions in advance. Next, each menu item offered by a restaurant is affixed with a QR code containing information about the ingredients of the menu item. When users dine, they can easily check whether they can eat the item by scanning this QR code with the smartphone app.

[0730] Scan and send QR codes

[0731] When a user scans a QR code using a smartphone app, the device transmits the menu ID and the user's dietary restriction information obtained from the QR code to the server. At this time, the QR code contains detailed ingredient information for each menu item, so the menu ID can be obtained by decoding it.

[0732] Server-side processing

[0733] The server receives the menu ID and the user's dietary restriction information sent from the device. The server first retrieves detailed ingredient information for the menu item from the database. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information. For example, if a menu item contains peanuts, the server will determine that a user with a peanut allergy should not eat that menu item.

[0734] Notification of the results

[0735] After the server has completed its compatibility assessment, it sends the results back to the device. The device receives the results and displays them to the user. The results include a message such as "This menu item can be eaten" or "This menu item should not be eaten," along with the reason for the decision.

[0736] Specific examples

[0737] For example, consider a scenario where a user scans the QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) to the server. The server retrieves the ingredient information for "Pad Thai" from a database and checks whether it contains peanuts. The generated AI detects the presence of peanuts, compares it with the dietary restriction information, determines that "this menu item should not be eaten," and sends the result back to the device. The device displays this result to the user, who can then change their selection with peace of mind.

[0738] As a result, we can provide a system that ensures the safety of eating out for people with dietary restrictions and supports easy menu selection. This system can efficiently manage and compare user dietary restriction information and menu ingredient information, achieving high reliability and speed.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[0742] Step 2:

[0743] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[0744] Step 3:

[0745] The menu ID read by the terminal is combined with the user's pre-set dietary restriction information (for example, allergy information or religious restrictions).

[0746] Step 4:

[0747] The device creates a request to the server, which includes the menu ID and the user's dietary restrictions.

[0748] Step 5:

[0749] The request created by the device is sent to the server. When the server receives the request, it first analyzes the contents of the request.

[0750] Step 6:

[0751] The server uses the menu ID to retrieve detailed ingredient information for the corresponding menu from the database, including each ingredient and cooking method.

[0752] Step 7:

[0753] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restriction information, and the AI ​​evaluates whether the menu contains ingredients that are incompatible with the user's dietary restrictions.

[0754] Step 8:

[0755] The server generates a suitability evaluation result, which includes a judgment such as "This menu item is OK to eat" or "This menu item should not be eaten" and the reason for the judgment.

[0756] Step 9:

[0757] The server returns the evaluation results to the device, which are sent in a standard format such as JSON.

[0758] Step 10:

[0759] The terminal analyzes the evaluation results received from the server and displays them in an easy-to-read format for the user.

[0760] Step 11:

[0761] The user checks the evaluation results on the device screen. For example, "This menu item should not be eaten. Reason: It contains peanuts" is displayed.

[0762] By following the steps above, the user can easily and safely check menus that meet their own dietary restrictions.

[0763] Example 1

[0764] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0765] To enable customers with specific dietary restrictions to quickly and accurately determine whether they can eat a certain food when dining out based on the ingredient information included in the menu. Currently, it is time-consuming to check the ingredient information individually, and there is a risk of accidentally ingesting something, so there is a need for a system that solves this problem.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0767] In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's dietary restriction information to the server, means for the server to use generated artificial intelligence to compare the menu ingredient information with the user's dietary restriction information to determine whether the food is permissible to eat, and means for notifying the user of the determination result and reason. This allows users to easily check the ingredient information of menus offered when dining out and select meals that suit their own dietary restrictions.

[0768] "Users with specific dietary restrictions" are users who cannot consume certain foods or ingredients due to allergies, religious restrictions, or health reasons.

[0769] "Codes containing menu ingredient information" refer to codes such as QR codes or barcodes that encode and include detailed ingredient information for menu items offered by restaurants.

[0770] "Means for reading" refers to the functionality or equipment for scanning the code and deciphering its contents using a camera and dedicated application installed on a device such as a smartphone or tablet.

[0771] "Means for sending to server" refers to communication means or protocols for transferring the read menu ingredient information and the user's dietary restriction information to a remote server via the Internet or the like.

[0772] "Generated artificial intelligence" refers to AI technologies such as machine learning models and deep learning models that are generated by programs to perform specific data processing and analysis.

[0773] The "means for determining" refers to the algorithms and processing logic that use a generative AI model within the server to compare menu ingredient information with the user's dietary restriction information and evaluate their suitability.

[0774] "Means of notification" refers to means for providing information to users, such as a function that displays the judgment results and the reasons for them on the user's device, or by means of push notifications or in-app messages.

[0775] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on the ingredient information of the menu items offered at restaurants. The system is configured using the user's smartphone, a server, and QR codes provided by the restaurant.

[0776] First, users install a dedicated application on their smartphone. This application has a function to register their dietary restrictions in advance. Specifically, users can input information about certain ingredients that they cannot consume due to allergies, religious restrictions, or health reasons.

[0777] Next, each menu item offered by a restaurant is affixed with a QR code containing the menu's ingredient information. The QR code encodes the menu ID and detailed ingredient information. When dining out, users can simply scan this QR code with a smartphone app to easily check whether they can eat the item.

[0778] When a user scans a QR code using a smartphone app, the device extracts the menu ID from the QR code and sends it to the server along with the user's dietary restriction information. The specific processing performed by the device includes a camera with QR code reading functionality and an application. The read data is then sent to the server via the Internet using protocols such as HTTP.

[0779] The server receives the menu ID and the user's dietary restriction information sent from the terminal. The server first retrieves detailed ingredient information for the menu item from the database. Next, it compares the ingredient information retrieved using the generative AI model with the user's dietary restriction information. An example of a prompt for the generative AI model is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[0780] The server analyzes the output of the generative AI model and determines whether the menu is suitable for the user. For example, if the ingredient information includes peanuts, the server generates a result that says "This menu should not be eaten" for a user with a peanut allergy.

[0781] The server then returns the judgment result (and reason) to the terminal. The judgment result is sent as an HTTP response, and the terminal receives the response from the server and analyzes its contents. Finally, the terminal displays the judgment result to the user. For example, a message such as "You can eat this menu item" or "You should not eat this menu item" along with the reason for the decision is displayed. This allows the user to make a meal selection with peace of mind.

[0782] For example, if a user scans the QR code for a menu item called "Pad Thai," the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) from the QR code to the server. The server retrieves the ingredient information for "Pad Thai" from the database and determines suitability using a generative AI model. As a result, the device sends a message back to the device saying, "This menu item contains peanuts and is not suitable for you," and the device displays that information to the user.

[0783] The purpose of this system is to enable people with dietary restrictions to select menu items with peace of mind when eating out. The system configuration effectively combines the user's device, server, and QR codes provided by restaurants, achieving high reliability and speed.

[0784] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0785] Step 1:

[0786] Users install a dedicated application on their smartphone and register their dietary restrictions.

[0787] Specifically, users download the app from the application store, install it, and then launch it. After launching the app, they can enter and save information about certain foods and ingredients they cannot consume due to allergies, religious restrictions, or health reasons.

[0788] Input: Any dietary restrictions the user has (e.g., peanut allergy).

[0789] Output: A saved profile of the user's dietary restrictions.

[0790] Step 2:

[0791] Users visit a restaurant and scan the QR code on the menu.

[0792] Specifically, the user launches a dedicated app and uses the smartphone camera to read the QR code.

[0793] Input: QR code containing menu ingredient information.

[0794] Output: Menu ID extracted from QR code.

[0795] Step 3:

[0796] The device sends the menu ID extracted from the QR code and pre-registered dietary restriction information to the server.

[0797] Specifically, the menu ID is extracted using the QR code reader function and sent to the server along with the registered dietary restriction information as an HTTP request.

[0798] Input: Menu ID, user's dietary restrictions.

[0799] Output: HTTP request to the server.

[0800] Step 4:

[0801] The server analyzes the received menu ID and dietary restriction information and retrieves the ingredient information for the corresponding menu from the database.

[0802] Specifically, the server parses the HTTP request body, extracts the menu ID and dietary restriction information, and then generates an SQL query to retrieve detailed ingredient information for the menu from the database.

[0803] Input: Menu ID.

[0804] Output: Ingredient information for the corresponding menu.

[0805] Step 5:

[0806] The server uses a generative AI model to match the acquired ingredient information with the user's dietary restrictions.

[0807] Specifically, it generates an appropriate prompt for the generative AI model and starts the matching process. An example of a prompt is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[0808] Input: Ingredient information, dietary restriction information.

[0809] Output: The relevance result.

[0810] Step 6:

[0811] The server analyzes the results of the suitability determination and the reasons for it, and returns them to the user's terminal.

[0812] Specifically, the server formats the determination result and the reason for it as an HTTP response and sends it to the user's terminal.

[0813] Input: The result of the decision and the reason for it.

[0814] Output: HTTP response to the user's device.

[0815] Step 7:

[0816] The terminal receives the HTTP response from the server and displays the contents to the user.

[0817] Specifically, the device analyzes the HTTP response and extracts the judgment result and reason. The result is displayed to the user as a pop-up notification or in-app message. For example, a message such as "This menu item can be eaten" or "This menu item should not be eaten" is displayed, along with the reason.

[0818] Input: The HTTP response from the server.

[0819] Output: The result and reason for the decision that is displayed to the user.

[0820] (Application example 1)

[0821] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0822] There is a need for a system that allows customers with specific dietary restrictions to easily and accurately check the ingredient information of dishes served when dining out, so that they can eat safely. With conventional systems, checking menu information is cumbersome, which can lead to errors and delays in judgment. Furthermore, there is a challenge in improving user convenience by displaying information in a visually easy-to-understand manner.

[0823] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0824] In this invention, the server includes means for reading a code containing information on ingredients of a dish, means for transmitting the read information on ingredients of the dish to the server, means for the server to compare the information on ingredients of the dish with the dietary restriction information of the user to determine whether the dish is edible, means for notifying the user of the determination result, and means for visually displaying the result using an augmented reality function. This allows users to instantly check the suitability of a menu for their dietary restrictions simply by scanning the QR code, and to visually confirm the result using augmented reality in an easy-to-understand manner.

[0825] "Specific dietary restrictions" are dietary rules for individuals who are required to avoid consuming certain ingredients or foodstuffs due to allergies, illness, religious reasons, etc.

[0826] "User" refers to an individual who uses the system to identify dietary restrictions at a food establishment.

[0827] "Restaurant" means a store or establishment that serves food and beverages.

[0828] "Food" refers to the food and menu served at a restaurant.

[0829] "Ingredient information" is detailed data about the specific ingredients, additives, and nutritional components contained in a dish.

[0830] "Means for reading codes" refers to devices or software that optically decode coded information such as QR codes and barcodes.

[0831] A "server" is a computer system for storing, processing, and communicating data.

[0832] "Dietary Restriction Information" refers to data regarding dietary restrictions registered by a user, including a list of allergens and ingredients to avoid.

[0833] "Augmented reality" is a technology that displays computer-generated information overlaid on the real world.

[0834] "Visual display means" refers to devices or software that use displays or projectors to provide information in a form that can be visually confirmed by the user.

[0835] This invention is a system that allows customers with specific dietary restrictions to enjoy eating out safely. This system is composed of a means for providing information on the customer's dietary restrictions and information on the ingredients of dishes served at restaurants as QR codes, smart glasses or a smartphone for reading the QR codes, a server that processes the read information, and an augmented reality function that visually displays the results.

[0836] System Overview

[0837] First, users install a dedicated application on their smart glasses or smartphone and register their dietary restrictions, including allergies and a list of ingredients they want to avoid.

[0838] Scanning QR codes

[0839] The restaurant menu includes a QR code containing the ingredient information for each dish. Customers use the camera in their smart glasses to scan the QR code, which encodes the dish's menu ID.

[0840] Server-side processing

[0841] When the QR code is scanned, the smart glasses send the menu ID and the user's dietary restriction information to the server. The server retrieves detailed ingredient information for the corresponding dish from a database based on the received menu ID. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information and determine whether the dish meets the user's dietary restrictions.

[0842] Augmented reality results display

[0843] The results are displayed on the user's smart glasses in a visually easy-to-understand format. For example, a message such as "This dish can be eaten" or "This dish should not be eaten" is displayed, along with the reason for the decision. This result display allows the user to safely select a dish.

[0844] Hardware and software used

[0845] Hardware: Smart glasses (camera, AR-enabled display), server, smartphone

[0846] Software: QR code reader, AR display software, database management system, generative AI model

[0847] Specific examples

[0848] For example, if a user scans the QR code for a dish called "Pad Thai," the server retrieves the ingredient information for "Pad Thai," and assumes that the dish contains peanuts. If the generated AI checks the peanut content and determines that the user has a peanut allergy, it will determine that "this dish should not be eaten" and display the result on the smart glasses. The user can then select a dish based on this visual display.

[0849] Prompt Sentence Examples

[0850] User ID: user123

[0851] QR code data: {"menu_id": "th001"}

[0852] User's dietary restrictions: {"allergies": ["shrimp"]}

[0853] Based on this, the server compares the ingredient information with the dietary restriction information and returns the results, allowing users to enjoy eating out safely.

[0854] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0855] Step 1:

[0856] Registering dietary restrictions

[0857] Users use smart glasses or a smartphone to launch a dedicated application and enter their dietary restrictions, including allergies and food items they want to avoid, which are then sent to a server and stored.

[0858] Input: User's dietary restrictions (e.g., allergy information)

[0859] Output: Dietary restrictions stored on the server

[0860] How it works: A user uses an application to provide input, which is received by the server and stored in a database.

[0861] Step 2:

[0862] Scan the menu QR code

[0863] At a restaurant, the user scans the QR code on the menu with the camera in the smart glasses, which retrieves the QR code data that encodes the dish's ingredients.

[0864] Input: Menu QR code

[0865] Output: QR code data (menu ID)

[0866] How it works: The camera in the smart glasses reads the QR code and captures the data.

[0867] Step 3:

[0868] Send QR code data and dietary restriction information

[0869] The smart glasses send the scanned QR code data and the user's pre-registered dietary restriction information to the server.

[0870] Input: QR code data, user's dietary restrictions

[0871] Output: Data sent to the server

[0872] How it works: The smart glasses send data over the network to a server.

[0873] Step 4:

[0874] Get menu ingredient information

[0875] The server analyzes the menu ID based on the received QR code data and retrieves detailed ingredient information for the corresponding dish from the database.

[0876] Input: QR code data (menu ID)

[0877] Output: Food ingredients information

[0878] How it works: The server queries the database and retrieves the ingredients for the corresponding dish.

[0879] Step 5:

[0880] Matching with dietary restrictions

[0881] The server compares the ingredient information of the dish with the user's dietary restrictions and uses an AI model to determine whether the user can eat the dish.

[0882] Input: Ingredients of the dish, user's dietary restrictions

[0883] Output: Judgment result on whether it is edible or not

[0884] How it works: The server's generative AI model performs data calculations and matches ingredient information with dietary restriction information.

[0885] Step 6:

[0886] Notification of results

[0887] The server sends the results to the smart glasses, which then display them in the user's field of vision using augmented reality.

[0888] Input: Judgment result

[0889] Output: Visual results displayed on an augmented reality display

[0890] How it works: The server sends the results to the smart glasses, which then use augmented reality to display the results, such as "This dish is okay to eat" or "This dish should be avoided."

[0891] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0892] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and further improves the user experience by combining it with an emotion engine that recognizes the user's emotions. This system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[0893] overview

[0894] The overall system flow begins when a user uses a smartphone app to scan a QR code attached to a restaurant menu. The QR code contains detailed information about the ingredients of the menu item. The app sends this information to a server, which then compares it with the user's pre-registered dietary restrictions to determine whether a particular menu item is safe. Furthermore, by combining this with an emotion engine, the system provides advice based on the user's emotional state.

[0895] Scan and send QR codes

[0896] The user launches the smartphone app and scans the menu's QR code using the QR code reader. The device then obtains the menu ID and sends it along with the user's dietary restrictions to the server. The emotion engine then recognizes the user's current emotional state and sends that information to the server.

[0897] Server-side processing

[0898] The server receives the menu ID, the user's dietary restriction information, and emotional state information sent from the device. First, it retrieves the ingredient information of the corresponding menu from the database using the menu ID. Then, it uses the generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information. This process determines whether a particular menu is suitable for the user.

[0899] The role of the emotional engine

[0900] The emotion engine uses facial expression and voice recognition technology to evaluate the user's emotional state. For example, if the user is feeling stressed or in a hurry, the server can provide a quicker judgment result. The emotion engine can also provide positive feedback or advice to relax based on the user's emotional state.

[0901] Notification of the results

[0902] After the server has completed its compatibility assessment, it sends the results back to the device, which then analyzes the results and displays them to the user in an appropriate format, along with advice based on the emotional state obtained by the emotion engine.

[0903] Specific examples

[0904] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0905] As described above, the present invention allows users to receive appropriate advice based on their emotional state at the time, as well as whether a meal meets their dietary restrictions, thereby making the dining out experience safer and more fulfilling for the user.

[0906] The processing flow will be explained below.

[0907] Step 1:

[0908] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[0909] Step 2:

[0910] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[0911] Step 3:

[0912] The device's emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state. For example, if the user is smiling, it is judged to be "happiness," and if they have a stern expression, it is judged to be "stress."

[0913] Step 4:

[0914] The terminal compiles the menu ID it reads, the user's emotional state, and pre-set dietary restriction information (e.g., allergy information or religious restrictions) and creates a request to the server.

[0915] Step 5:

[0916] The device sends a request to the server, which includes the menu ID, the user's dietary restrictions, and their emotional state.

[0917] Step 6:

[0918] The server receives the request, analyzes the request, extracts the menu ID, and retrieves detailed ingredient information for the corresponding menu from the database.

[0919] Step 7:

[0920] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restrictions. For example, if a user has a peanut allergy, it can check whether the menu contains peanuts.

[0921] Step 8:

[0922] The server generates a menu suitability evaluation result, which includes a judgment such as "This menu is OK to eat" or "This menu should not be eaten" and the reason for the judgment.

[0923] Step 9:

[0924] Based on the evaluation results, the server also provides advice tailored to the user's emotional state. For example, if the user is feeling stressed, the server suggests an alternative menu to help them relax.

[0925] Step 10:

[0926] The server sends the evaluation results and advice from the emotion engine back to the device in a standard format such as JSON.

[0927] Step 11:

[0928] The device analyzes the evaluation results and advice received from the server, and then displays them in an easy-to-read format for the user.

[0929] Step 12:

[0930] The user checks the evaluation results on the device screen. For example, a message such as "You should not eat this menu item because it contains peanuts. However, we recommend this salad menu item."

[0931] Through the above steps, the user can easily and safely check a menu that suits their own dietary restrictions, and at the same time receive appropriate advice that corresponds to their current emotional state.

[0932] Example 2

[0933] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0934] In conventional restaurants, customers with specific dietary restrictions have limited means of checking the ingredient information on the menu, making it difficult for them to determine whether or not they can eat the food. Furthermore, since advice is not provided that takes into account the customer's emotional state, their dining out experience tends to be limited. The goal is to solve these problems.

[0935] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0936] In this invention, the server includes a means for checking the ingredient information of the menu to determine whether it is safe to eat, a means for recognizing the emotional state of the user and providing advice based on that information, and a means for notifying the user of the judgment result and advice, thereby enabling the user to safely select a menu and receive advice according to their emotional state at the time.

[0937] "Menu ingredient information" is data that describes the food ingredients and allergen information contained in each menu item offered at a restaurant.

[0938] "Dietary restriction information" refers to information about foods or ingredients that you are allergic to or that you need to avoid for health reasons.

[0939] A "QR code" is a two-dimensional code that stores data such as menu ingredient information and can be read by devices such as smartphones.

[0940] A "server" is a computer system that receives, analyzes, and transmits data over a network.

[0941] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their current emotional state.

[0942] "Generated artificial intelligence (AI)" is a collection of machine learning models and algorithms used for data analysis and pattern recognition, which in this invention serves to match menu ingredient information with dietary restriction information.

[0943] "Advice" refers to advice or recommendations provided by the system to the user, including feedback based on emotional state in particular.

[0944] "Notification means" refers to a function for communicating the assessment results and advice to the user, and includes screen displays and voice notifications on the smartphone app.

[0945] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and improves the user's dining out experience by combining it with an emotion engine that recognizes the user's emotions. The system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[0946] When a user launches the smartphone app, the QR code reading function becomes available. When the user uses the app's QR code reading function to scan a QR code on a restaurant menu, the device acquires this information. The QR code contains detailed information about the ingredients of the menu item. The device then sends the menu ID obtained as a result of the scan, any dietary restrictions set by the user, and the user's emotional state analyzed by the emotion engine to the server.

[0947] The server receives the menu ID, dietary restriction information, and emotional state information sent from the device. First, the server uses the menu ID to retrieve the ingredient information of the corresponding menu from the database. Then, it uses the generated AI model to match the menu ingredient information with the dietary restriction information. This matching process determines whether a particular menu is suitable for the user. Furthermore, the server analyzes the user's emotional state retrieved by the emotion engine and generates positive advice or relaxation recommendations based on that.

[0948] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated AI model. For example, if the Pad Thai contains peanuts, the server determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, the device provides positive advice such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[0949] Throughout the entire system, users can receive appropriate advice based on their emotional state as well as whether the food meets their dietary restrictions, making their dining out experience safer and more fulfilling.

[0950] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] The user launches the smartphone app.

[0953] Input: The app is launched by user action.

[0954] Output: The app home screen will be displayed and the QR code reading function will be available.

[0955] Specific operation: The user launches the app and taps the "Scan QR code" button that appears on the home screen.

[0956] Step 2:

[0957] The device scans the QR code and obtains the menu ID.

[0958] Input: QR code attached to restaurant menu.

[0959] Output: The menu ID obtained from the QR code is stored in temporary memory.

[0960] Specific operation: The device activates the built-in camera and scans the QR code. The app displays the message "Menu ID acquired."

[0961] Step 3:

[0962] The device acquires dietary restriction information and emotional state and transmits them to the server.

[0963] Input: User's dietary restriction information and emotional state information.

[0964] Output: Send the acquired information to the server.

[0965] Specific operation: The device reads the user's dietary restriction information (such as peanut allergies) from memory, and the emotion engine uses the camera and microphone to analyze the user's emotions from their facial expressions and voice. This information is then sent to the server.

[0966] Step 4:

[0967] The server retrieves the menu ingredient information from a database.

[0968] Input: Menu ID sent from the terminal.

[0969] Output: Ingredient information for the corresponding menu.

[0970] Specific operation: The server queries the database using the menu ID and retrieves the ingredient information for the corresponding menu item.

[0971] Step 5:

[0972] The server uses the generated AI model to match menu ingredient information with dietary restriction information.

[0973] Input: Menu ingredient information and user dietary restriction information.

[0974] Output: A decision on whether the menu is suitable for the user.

[0975] How it works: The server runs an AI model to analyze the menu ingredient information and dietary restriction information and determine whether there are any matching ingredients. For example, if the ingredient information includes peanuts, it will evaluate the food as "not suitable."

[0976] Step 6:

[0977] The server uses an emotion engine to generate advice based on the user's emotional state.

[0978] Input: User's emotional state information.

[0979] Output: Advice based on the user's emotional state.

[0980] Specific operation: The server generates advice based on the analysis results of the emotion engine according to the user's emotional state. For example, if the user is feeling stressed, it generates a message to relax.

[0981] Step 7:

[0982] The server returns the judgment result and advice to the terminal.

[0983] Input: Judgment results from the AI ​​model and advice from the emotion engine.

[0984] Output: The judgment result and advice are sent to the terminal.

[0985] Specific operation: The server sends JSON data containing the judgment result and advice to the terminal via HTTP(S).

[0986] Step 8:

[0987] The terminal displays the judgment result and advice to the user.

[0988] Input: Verification result and advice sent from the server.

[0989] Output: Display in a format suitable for the user.

[0990] Specific operation: The device analyzes the received JSON data and displays a message on the user's smartphone screen such as, "Please avoid this menu item as it contains peanuts. We recommend this salad menu item."

[0991] (Application example 2)

[0992] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0993] When choosing a menu at a restaurant, it is difficult for users with specific dietary restrictions to select an appropriate menu that takes into account their emotional state in addition to their dietary restrictions. Conventional systems only consider dietary restrictions, and therefore lack appropriate advice and suggestions based on the user's emotional state. As a result, users often feel stressed and anxious, resulting in a poor dining out experience. This project aims to solve this problem.

[0994] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's emotional state to the server, means for the server to compare the menu ingredient information, the user's dietary restriction information, and emotional state information and use a generative AI model to suggest an appropriate menu, and means for notifying the user of advice based on the judgment results and emotions. This makes it possible to suggest menus that comprehensively take into account the user's dietary restriction information and emotional state, thereby providing a safer and more comfortable dining out experience.

[0995] Key Word Definitions

[0996] "Specific dietary restrictions" refers to specific ingredients or foods that a user should avoid due to allergies or health conditions.

[0997] "Menu ingredient information" refers to data that indicates all ingredients and their details contained in each menu item served at a restaurant.

[0998] "Code" refers to symbols, barcodes, and especially QR codes, that convert information, including menu ingredient information, into a form that can be read by a smartphone or other device.

[0999] "Emotional state" refers to the psychological state, such as stress or happiness, that the user is feeling when selecting a menu, and is analyzed from facial expressions and voice.

[1000] A "generative AI model" is a machine learning method that uses data to suggest menus that are appropriate for the user, and in particular uses artificial intelligence technologies such as neural networks.

[1001] A "server" is a central processing unit that processes data sent from a client terminal and provides necessary information.

[1002] "Advice" refers to recommendations about what meals are suitable or should be avoided based on the user's dietary restrictions and emotional state.

[1003] "Method of reading" refers to the method of obtaining information from a QR code using a smartphone camera or a dedicated scanner.

[1004] "Means of notification" refers to the functions and methods for conveying information to users through devices such as smartphones and tablets.

[1005] "Matching" refers to the process by which the server compares the menu ingredient information with the user's dietary restrictions and / or emotional state information to confirm a match.

[1006] MODE FOR CARRYING OUT THE INVENTION

[1007] The system for implementing this invention is composed of a smartphone app, a server, an emotion engine, and a QR code provided at a restaurant. A specific embodiment of the system is shown below.

[1008] Hardware and Software Configuration

[1009] Hardware

[1010] Smartphone: A device that allows users to use applications.

[1011] QR Code Reader: A function to read QR codes using a smartphone camera.

[1012] Emotion recognition system: Uses the smartphone's camera and microphone to analyze the user's facial expressions and voice.

[1013] software

[1014] Smartphone app: Application that works on iOS and Android.

[1015] Emotion engine: An API for analyzing the user's emotional state, such as Google Cloud's Face API or Microsoft Azure's Emotion API.

[1016] Server-side programs: Web servers such as Apache or NGINX, and back-end programs using Python or Node.js.

[1017] Generative AI model: A machine learning model using TensorFlow, etc., used to make menu suggestions based on the analysis results.

[1018] Process Overview

[1019] Smartphone app

[1020] 1. The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code.

[1021] 2. The smartphone app obtains the menu ID from the scanned QR code and sends it to the server.

[1022] 3. At the same time, the smartphone's camera and microphone capture the user's facial expressions and voice and send them to the emotion engine.

[1023] Server Side

[1024] 1. The server retrieves ingredient information from the database based on the received menu ID.

[1025] 2. The user's dietary restriction information is also obtained from a pre-registered database.

[1026] 3. The emotional state analyzed by the emotion engine is obtained and data is sent to the generative AI model based on this.

[1027] 4. The generative AI model collates ingredient information, dietary restriction information, and emotional state information to generate optimal menu suggestions and emotion-based advice.

[1028] 5. The server sends the generated suggestions and advice back to the smartphone app.

[1029] User Experience

[1030] 1. Shows whether the menu the user scans is suitable for their dietary restrictions and how the menu selection affects the user's emotional state.

[1031] 2. If necessary, we will also provide positive advice and suggest menus that will promote relaxation.

[1032] Specific examples

[1033] For example, consider the case where a user scans a QR code for a "Pad Thai" menu. The user scans the QR code with a smartphone app, and the device sends the "Pad Thai" menu ID and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[1034] Prompt Sentence Examples

[1035] Use the following prompt for the generative AI model:

[1036] A user scanned the QR code of a menu item selected at a restaurant. The user's dietary restrictions are: {'Allergies': 'Peanuts'}. The user's current emotional state is: {'Emotion': 'Stressed'}. Generate menu suggestions and advice. Menu ID: '12345'. Ingredients: {'Peanuts': True, ...}.

[1037] Based on this prompt, appropriate menu suggestions and sentiment-based advice can be generated.

[1038] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1039] Program processing steps

[1040] Step 1:

[1041] The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code. The input is the QR code, and the output is the menu ID. This menu ID is sent to the server in the next step.

[1042] Step 2:

[1043] The device sends the acquired menu ID and the user's dietary restriction information to the server. At the same time, the device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine. The input is the menu ID, dietary restriction information, and emotion data, which are then processed in the next step.

[1044] Step 3:

[1045] The server retrieves the ingredient information of the corresponding menu from the database based on the received menu ID. The input is the menu ID, and the output is ingredient information. This ingredient information is used in the next step.

[1046] Step 4:

[1047] The server retrieves the user's dietary restriction information from the database. The input is the user ID, and the output is the dietary restriction information. This information is then collated in the next step.

[1048] Step 5:

[1049] The server receives the user's emotional state information from the emotion engine. The input is the captured facial and voice data, and the output is the emotional state information, which is then sent to the generative AI model in the next step.

[1050] Step 6:

[1051] The server inputs ingredient information, dietary restriction information, and emotional state information into a generative AI model to generate appropriate menu suggestions and emotional advice. The inputs are the above three pieces of information, and the output is a suggested menu and advice. This generative AI model is built using TensorFlow and other tools.

[1052] Step 7:

[1053] The server returns the generated suggestion menu and advice to the terminal. The input is the suggestion menu and advice, and the output is displayed on the terminal.

[1054] Step 8:

[1055] The device displays the received suggested menu and advice to the user. The input is the suggested menu and advice, and the output is displayed on the user's smartphone screen. In concrete terms, the user can select a menu based on the displayed information.

[1056] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1057] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1058] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1059] [Fourth embodiment]

[1060] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1061] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1062] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1063] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1064] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1065] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1066] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1067] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1068] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1069] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1070] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1071] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1072] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1073] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat certain foods based on the ingredient information included in the menus offered by restaurants when dining out. This system is configured using the user's smartphone, a server, and QR codes provided by restaurants.

[1074] overview

[1075] First, users install a dedicated application on their smartphone. This application has the function of registering the user's dietary restrictions in advance. Next, each menu item offered by a restaurant is affixed with a QR code containing information about the ingredients of the menu item. When users dine, they can easily check whether they can eat the item by scanning this QR code with the smartphone app.

[1076] Scan and send QR codes

[1077] When a user scans a QR code using a smartphone app, the device transmits the menu ID and the user's dietary restriction information obtained from the QR code to the server. At this time, the QR code contains detailed ingredient information for each menu item, so the menu ID can be obtained by decoding it.

[1078] Server-side processing

[1079] The server receives the menu ID and the user's dietary restriction information sent from the device. The server first retrieves detailed ingredient information for the menu item from the database. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information. For example, if a menu item contains peanuts, the server will determine that a user with a peanut allergy should not eat that menu item.

[1080] Notification of the results

[1081] After the server has completed its compatibility assessment, it sends the results back to the device. The device receives the results and displays them to the user. The results include a message such as "This menu item can be eaten" or "This menu item should not be eaten," along with the reason for the decision.

[1082] Specific examples

[1083] For example, consider a scenario where a user scans the QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) to the server. The server retrieves the ingredient information for "Pad Thai" from a database and checks whether it contains peanuts. The generated AI detects the presence of peanuts, compares it with the dietary restriction information, determines that "this menu item should not be eaten," and sends the result back to the device. The device displays this result to the user, who can then change their selection with peace of mind.

[1084] As a result, we can provide a system that ensures the safety of eating out for people with dietary restrictions and supports easy menu selection. This system can efficiently manage and compare user dietary restriction information and menu ingredient information, achieving high reliability and speed.

[1085] The processing flow will be explained below.

[1086] Step 1:

[1087] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[1088] Step 2:

[1089] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[1090] Step 3:

[1091] The menu ID read by the terminal is combined with the user's pre-set dietary restriction information (for example, allergy information or religious restrictions).

[1092] Step 4:

[1093] The device creates a request to the server, which includes the menu ID and the user's dietary restrictions.

[1094] Step 5:

[1095] The request created by the device is sent to the server. When the server receives the request, it first analyzes the contents of the request.

[1096] Step 6:

[1097] The server uses the menu ID to retrieve detailed ingredient information for the corresponding menu from the database, including each ingredient and cooking method.

[1098] Step 7:

[1099] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restriction information, and the AI ​​evaluates whether the menu contains ingredients that are incompatible with the user's dietary restrictions.

[1100] Step 8:

[1101] The server generates a suitability evaluation result, which includes a judgment such as "This menu item is OK to eat" or "This menu item should not be eaten" and the reason for the judgment.

[1102] Step 9:

[1103] The server returns the evaluation results to the device, which are sent in a standard format such as JSON.

[1104] Step 10:

[1105] The terminal analyzes the evaluation results received from the server and displays them in an easy-to-read format for the user.

[1106] Step 11:

[1107] The user checks the evaluation results on the device screen. For example, "This menu item should not be eaten. Reason: It contains peanuts" is displayed.

[1108] By following the steps above, the user can easily and safely check menus that meet their own dietary restrictions.

[1109] Example 1

[1110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1111] To enable customers with specific dietary restrictions to quickly and accurately determine whether they can eat a certain food when dining out based on the ingredient information included in the menu. Currently, it is time-consuming to check the ingredient information individually, and there is a risk of accidentally ingesting something, so there is a need for a system that solves this problem.

[1112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1113] In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's dietary restriction information to the server, means for the server to use generated artificial intelligence to compare the menu ingredient information with the user's dietary restriction information to determine whether the food is permissible to eat, and means for notifying the user of the determination result and reason. This allows users to easily check the ingredient information of menus offered when dining out and select meals that suit their own dietary restrictions.

[1114] "Users with specific dietary restrictions" are users who cannot consume certain foods or ingredients due to allergies, religious restrictions, or health reasons.

[1115] "Codes containing menu ingredient information" refer to codes such as QR codes or barcodes that encode and include detailed ingredient information for menu items offered by restaurants.

[1116] "Means for reading" refers to the functionality or equipment for scanning the code and deciphering its contents using a camera and dedicated application installed on a device such as a smartphone or tablet.

[1117] "Means for sending to server" refers to communication means or protocols for transferring the read menu ingredient information and the user's dietary restriction information to a remote server via the Internet or the like.

[1118] "Generated artificial intelligence" refers to AI technologies such as machine learning models and deep learning models that are generated by programs to perform specific data processing and analysis.

[1119] The "means for determining" refers to the algorithms and processing logic that use a generative AI model within the server to compare menu ingredient information with the user's dietary restriction information and evaluate their suitability.

[1120] "Means of notification" refers to means for providing information to users, such as a function that displays the judgment results and the reasons for them on the user's device, or by means of push notifications or in-app messages.

[1121] This invention is a system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on the ingredient information of the menu items offered at restaurants. The system is configured using the user's smartphone, a server, and QR codes provided by the restaurant.

[1122] First, users install a dedicated application on their smartphone. This application has a function to register their dietary restrictions in advance. Specifically, users can input information about certain ingredients that they cannot consume due to allergies, religious restrictions, or health reasons.

[1123] Next, each menu item offered by a restaurant is affixed with a QR code containing the menu's ingredient information. The QR code encodes the menu ID and detailed ingredient information. When dining out, users can simply scan this QR code with a smartphone app to easily check whether they can eat the item.

[1124] When a user scans a QR code using a smartphone app, the device extracts the menu ID from the QR code and sends it to the server along with the user's dietary restriction information. The specific processing performed by the device includes a camera with QR code reading functionality and an application. The read data is then sent to the server via the Internet using protocols such as HTTP.

[1125] The server receives the menu ID and the user's dietary restriction information sent from the terminal. The server first retrieves detailed ingredient information for the menu item from the database. Next, it compares the ingredient information retrieved using the generative AI model with the user's dietary restriction information. An example of a prompt for the generative AI model is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[1126] The server analyzes the output of the generative AI model and determines whether the menu is suitable for the user. For example, if the ingredient information includes peanuts, the server generates a result that says "This menu should not be eaten" for a user with a peanut allergy.

[1127] The server then returns the judgment result (and reason) to the terminal. The judgment result is sent as an HTTP response, and the terminal receives the response from the server and analyzes its contents. Finally, the terminal displays the judgment result to the user. For example, a message such as "You can eat this menu item" or "You should not eat this menu item" along with the reason for the decision is displayed. This allows the user to make a meal selection with peace of mind.

[1128] For example, if a user scans the QR code for a menu item called "Pad Thai," the device sends the menu ID "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy information) from the QR code to the server. The server retrieves the ingredient information for "Pad Thai" from the database and determines suitability using a generative AI model. As a result, the device sends a message back to the device saying, "This menu item contains peanuts and is not suitable for you," and the device displays that information to the user.

[1129] The purpose of this system is to enable people with dietary restrictions to select menu items with peace of mind when eating out. The system configuration effectively combines the user's device, server, and QR codes provided by restaurants, achieving high reliability and speed.

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] Users install a dedicated application on their smartphone and register their dietary restrictions.

[1133] Specifically, users download the app from the application store, install it, and then launch it. After launching the app, they can enter and save information about certain foods and ingredients they cannot consume due to allergies, religious restrictions, or health reasons.

[1134] Input: Any dietary restrictions the user has (e.g., peanut allergy).

[1135] Output: A saved profile of the user's dietary restrictions.

[1136] Step 2:

[1137] Users visit a restaurant and scan the QR code on the menu.

[1138] Specifically, the user launches a dedicated app and uses the smartphone camera to read the QR code.

[1139] Input: QR code containing menu ingredient information.

[1140] Output: Menu ID extracted from QR code.

[1141] Step 3:

[1142] The device sends the menu ID extracted from the QR code and pre-registered dietary restriction information to the server.

[1143] Specifically, the menu ID is extracted using the QR code reader function and sent to the server along with the registered dietary restriction information as an HTTP request.

[1144] Input: Menu ID, user's dietary restrictions.

[1145] Output: HTTP request to the server.

[1146] Step 4:

[1147] The server analyzes the received menu ID and dietary restriction information and retrieves the ingredient information for the corresponding menu from the database.

[1148] Specifically, the server parses the HTTP request body, extracts the menu ID and dietary restriction information, and then generates an SQL query to retrieve detailed ingredient information for the menu from the database.

[1149] Input: Menu ID.

[1150] Output: Ingredient information for the corresponding menu.

[1151] Step 5:

[1152] The server uses a generative AI model to match the acquired ingredient information with the user's dietary restrictions.

[1153] Specifically, it generates an appropriate prompt for the generative AI model and starts the matching process. An example of a prompt is, "User XYZ's dietary restriction information is a peanut allergy. Please determine whether this user can eat the menu with menu ID 1234."

[1154] Input: Ingredient information, dietary restriction information.

[1155] Output: The relevance result.

[1156] Step 6:

[1157] The server analyzes the results of the suitability determination and the reasons for it, and returns them to the user's terminal.

[1158] Specifically, the server formats the determination result and the reason for it as an HTTP response and sends it to the user's terminal.

[1159] Input: The result of the decision and the reason for it.

[1160] Output: HTTP response to the user's device.

[1161] Step 7:

[1162] The terminal receives the HTTP response from the server and displays the contents to the user.

[1163] Specifically, the device analyzes the HTTP response and extracts the judgment result and reason. The result is displayed to the user as a pop-up notification or in-app message. For example, a message such as "This menu item can be eaten" or "This menu item should not be eaten" is displayed, along with the reason.

[1164] Input: The HTTP response from the server.

[1165] Output: The result and reason for the decision that is displayed to the user.

[1166] (Application example 1)

[1167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1168] There is a need for a system that allows customers with specific dietary restrictions to easily and accurately check the ingredient information of dishes served when dining out, so that they can eat safely. With conventional systems, checking menu information is cumbersome, which can lead to errors and delays in judgment. Furthermore, there is a challenge in improving user convenience by displaying information in a visually easy-to-understand manner.

[1169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1170] In this invention, the server includes means for reading a code containing information on ingredients of a dish, means for transmitting the read information on ingredients of the dish to the server, means for the server to compare the information on ingredients of the dish with the dietary restriction information of the user to determine whether the dish is edible, means for notifying the user of the determination result, and means for visually displaying the result using an augmented reality function. This allows users to instantly check the suitability of a menu for their dietary restrictions simply by scanning the QR code, and to visually confirm the result using augmented reality in an easy-to-understand manner.

[1171] "Specific dietary restrictions" are dietary rules for individuals who are required to avoid consuming certain ingredients or foodstuffs due to allergies, illness, religious reasons, etc.

[1172] "User" refers to an individual who uses the system to identify dietary restrictions at a food establishment.

[1173] "Restaurant" means a store or establishment that serves food and beverages.

[1174] "Food" refers to the food and menu served at a restaurant.

[1175] "Ingredient information" is detailed data about the specific ingredients, additives, and nutritional components contained in a dish.

[1176] "Means for reading codes" refers to devices or software that optically decode coded information such as QR codes and barcodes.

[1177] A "server" is a computer system for storing, processing, and communicating data.

[1178] "Dietary Restriction Information" refers to data regarding dietary restrictions registered by a user, including a list of allergens and ingredients to avoid.

[1179] "Augmented reality" is a technology that displays computer-generated information overlaid on the real world.

[1180] "Visual display means" refers to devices or software that use displays or projectors to provide information in a form that can be visually confirmed by the user.

[1181] This invention is a system that allows customers with specific dietary restrictions to enjoy eating out safely. This system is composed of a means for providing information on the customer's dietary restrictions and information on the ingredients of dishes served at restaurants as QR codes, smart glasses or a smartphone for reading the QR codes, a server that processes the read information, and an augmented reality function that visually displays the results.

[1182] System Overview

[1183] First, users install a dedicated application on their smart glasses or smartphone and register their dietary restrictions, including allergies and a list of ingredients they want to avoid.

[1184] Scanning QR codes

[1185] The restaurant menu includes a QR code containing the ingredient information for each dish. Customers use the camera in their smart glasses to scan the QR code, which encodes the dish's menu ID.

[1186] Server-side processing

[1187] When the QR code is scanned, the smart glasses send the menu ID and the user's dietary restriction information to the server. The server retrieves detailed ingredient information for the corresponding dish from a database based on the received menu ID. It then uses the generated AI to compare the retrieved ingredient information with the user's dietary restriction information and determine whether the dish meets the user's dietary restrictions.

[1188] Augmented reality results display

[1189] The results are displayed on the user's smart glasses in a visually easy-to-understand format. For example, a message such as "This dish can be eaten" or "This dish should not be eaten" is displayed, along with the reason for the decision. This result display allows the user to safely select a dish.

[1190] Hardware and software used

[1191] Hardware: Smart glasses (camera, AR-enabled display), server, smartphone

[1192] Software: QR code reader, AR display software, database management system, generative AI model

[1193] Specific examples

[1194] For example, if a user scans the QR code for a dish called "Pad Thai," the server retrieves the ingredient information for "Pad Thai," and assumes that the dish contains peanuts. If the generated AI checks the peanut content and determines that the user has a peanut allergy, it will determine that "this dish should not be eaten" and display the result on the smart glasses. The user can then select a dish based on this visual display.

[1195] Prompt Sentence Examples

[1196] User ID: user123

[1197] QR code data: {"menu_id": "th001"}

[1198] User's dietary restrictions: {"allergies": ["shrimp"]}

[1199] Based on this, the server compares the ingredient information with the dietary restriction information and returns the results, allowing users to enjoy eating out safely.

[1200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1201] Step 1:

[1202] Registering dietary restrictions

[1203] Users use smart glasses or a smartphone to launch a dedicated application and enter their dietary restrictions, including allergies and food items they want to avoid, which are then sent to a server and stored.

[1204] Input: User's dietary restrictions (e.g., allergy information)

[1205] Output: Dietary restrictions stored on the server

[1206] How it works: A user uses an application to provide input, which is received by the server and stored in a database.

[1207] Step 2:

[1208] Scan the menu QR code

[1209] At a restaurant, the user scans the QR code on the menu with the camera in the smart glasses, which retrieves the QR code data that encodes the dish's ingredients.

[1210] Input: Menu QR code

[1211] Output: QR code data (menu ID)

[1212] How it works: The camera in the smart glasses reads the QR code and captures the data.

[1213] Step 3:

[1214] Send QR code data and dietary restriction information

[1215] The smart glasses send the scanned QR code data and the user's pre-registered dietary restriction information to the server.

[1216] Input: QR code data, user's dietary restrictions

[1217] Output: Data sent to the server

[1218] How it works: The smart glasses send data over the network to a server.

[1219] Step 4:

[1220] Get menu ingredient information

[1221] The server analyzes the menu ID based on the received QR code data and retrieves detailed ingredient information for the corresponding dish from the database.

[1222] Input: QR code data (menu ID)

[1223] Output: Food ingredients information

[1224] How it works: The server queries the database and retrieves the ingredients for the corresponding dish.

[1225] Step 5:

[1226] Matching with dietary restrictions

[1227] The server compares the ingredient information of the dish with the user's dietary restrictions and uses an AI model to determine whether the user can eat the dish.

[1228] Input: Ingredients of the dish, user's dietary restrictions

[1229] Output: Judgment result on whether it is edible or not

[1230] How it works: The server's generative AI model performs data calculations and matches ingredient information with dietary restriction information.

[1231] Step 6:

[1232] Notification of results

[1233] The server sends the results to the smart glasses, which then display them in the user's field of vision using augmented reality.

[1234] Input: Judgment result

[1235] Output: Visual results displayed on an augmented reality display

[1236] How it works: The server sends the results to the smart glasses, which then use augmented reality to display the results, such as "This dish is okay to eat" or "This dish should be avoided."

[1237] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1238] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and further improves the user experience by combining it with an emotion engine that recognizes the user's emotions. This system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[1239] overview

[1240] The overall system flow begins when a user uses a smartphone app to scan a QR code attached to a restaurant menu. The QR code contains detailed information about the ingredients of the menu item. The app sends this information to a server, which then compares it with the user's pre-registered dietary restrictions to determine whether a particular menu item is safe. Furthermore, by combining this with an emotion engine, the system provides advice based on the user's emotional state.

[1241] Scan and send QR codes

[1242] The user launches the smartphone app and scans the menu's QR code using the QR code reader. The device then obtains the menu ID and sends it along with the user's dietary restrictions to the server. The emotion engine then recognizes the user's current emotional state and sends that information to the server.

[1243] Server-side processing

[1244] The server receives the menu ID, the user's dietary restriction information, and emotional state information sent from the device. First, it retrieves the ingredient information of the corresponding menu from the database using the menu ID. Then, it uses the generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information. This process determines whether a particular menu is suitable for the user.

[1245] The role of the emotional engine

[1246] The emotion engine uses facial expression and voice recognition technology to evaluate the user's emotional state. For example, if the user is feeling stressed or in a hurry, the server can provide a quicker judgment result. The emotion engine can also provide positive feedback or advice to relax based on the user's emotional state.

[1247] Notification of the results

[1248] After the server has completed its compatibility assessment, it sends the results back to the device, which then analyzes the results and displays them to the user in an appropriate format, along with advice based on the emotional state obtained by the emotion engine.

[1249] Specific examples

[1250] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[1251] As described above, the present invention allows users to receive appropriate advice based on their emotional state at the time, as well as whether a meal meets their dietary restrictions, thereby making the dining out experience safer and more fulfilling for the user.

[1252] The processing flow will be explained below.

[1253] Step 1:

[1254] The user launches the smartphone app and selects the QR code reading function, which activates the device's camera and captures the QR code.

[1255] Step 2:

[1256] The device app decodes the captured QR code and obtains the menu ID, which includes the menu's ingredient information. This decoding process is performed using an internal library.

[1257] Step 3:

[1258] The device's emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state. For example, if the user is smiling, it is judged to be "happiness," and if they have a stern expression, it is judged to be "stress."

[1259] Step 4:

[1260] The terminal compiles the menu ID it reads, the user's emotional state, and pre-set dietary restriction information (e.g., allergy information or religious restrictions) and creates a request to the server.

[1261] Step 5:

[1262] The device sends a request to the server, which includes the menu ID, the user's dietary restrictions, and their emotional state.

[1263] Step 6:

[1264] The server receives the request, analyzes the request, extracts the menu ID, and retrieves detailed ingredient information for the corresponding menu from the database.

[1265] Step 7:

[1266] The server uses the generated AI to compare the acquired menu ingredient information with the user's dietary restrictions. For example, if a user has a peanut allergy, it can check whether the menu contains peanuts.

[1267] Step 8:

[1268] The server generates a menu suitability evaluation result, which includes a judgment such as "This menu is OK to eat" or "This menu should not be eaten" and the reason for the judgment.

[1269] Step 9:

[1270] Based on the evaluation results, the server also provides advice tailored to the user's emotional state. For example, if the user is feeling stressed, the server suggests an alternative menu to help them relax.

[1271] Step 10:

[1272] The server sends the evaluation results and advice from the emotion engine back to the device in a standard format such as JSON.

[1273] Step 11:

[1274] The device analyzes the evaluation results and advice received from the server, and then displays them in an easy-to-read format for the user.

[1275] Step 12:

[1276] The user checks the evaluation results on the device screen. For example, a message such as "You should not eat this menu item because it contains peanuts. However, we recommend this salad menu item."

[1277] Through the above steps, the user can easily and safely check a menu that suits their own dietary restrictions, and at the same time receive appropriate advice that corresponds to their current emotional state.

[1278] Example 2

[1279] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1280] In conventional restaurants, customers with specific dietary restrictions have limited means of checking the ingredient information on the menu, making it difficult for them to determine whether or not they can eat the food. Furthermore, since advice is not provided that takes into account the customer's emotional state, their dining out experience tends to be limited. The goal is to solve these problems.

[1281] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1282] In this invention, the server includes a means for checking the ingredient information of the menu to determine whether it is safe to eat, a means for recognizing the emotional state of the user and providing advice based on that information, and a means for notifying the user of the judgment result and advice, thereby enabling the user to safely select a menu and receive advice according to their emotional state at the time.

[1283] "Menu ingredient information" is data that describes the food ingredients and allergen information contained in each menu item offered at a restaurant.

[1284] "Dietary restriction information" refers to information about foods or ingredients that you are allergic to or that you need to avoid for health reasons.

[1285] A "QR code" is a two-dimensional code that stores data such as menu ingredient information and can be read by devices such as smartphones.

[1286] A "server" is a computer system that receives, analyzes, and transmits data over a network.

[1287] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their current emotional state.

[1288] "Generated artificial intelligence (AI)" is a collection of machine learning models and algorithms used for data analysis and pattern recognition, which in this invention serves to match menu ingredient information with dietary restriction information.

[1289] "Advice" refers to advice or recommendations provided by the system to the user, including feedback based on emotional state in particular.

[1290] "Notification means" refers to a function for communicating the assessment results and advice to the user, and includes screen displays and voice notifications on the smartphone app.

[1291] This invention is a system that enables users with specific dietary restrictions to safely select menu items offered at restaurants, and improves the user's dining out experience by combining it with an emotion engine that recognizes the user's emotions. The system consists of a dedicated smartphone app, a server, an emotion engine, and a QR code provided by the restaurant.

[1292] When a user launches the smartphone app, the QR code reading function becomes available. When the user uses the app's QR code reading function to scan a QR code on a restaurant menu, the device acquires this information. The QR code contains detailed information about the ingredients of the menu item. The device then sends the menu ID obtained as a result of the scan, any dietary restrictions set by the user, and the user's emotional state analyzed by the emotion engine to the server.

[1293] The server receives the menu ID, dietary restriction information, and emotional state information sent from the device. First, the server uses the menu ID to retrieve the ingredient information of the corresponding menu from the database. Then, it uses the generated AI model to match the menu ingredient information with the dietary restriction information. This matching process determines whether a particular menu is suitable for the user. Furthermore, the server analyzes the user's emotional state retrieved by the emotion engine and generates positive advice or relaxation recommendations based on that.

[1294] For example, consider the case where a user scans a QR code for a menu item called "Pad Thai." The user scans the QR code with a smartphone app, and the device sends the menu ID for "Pad Thai" and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated AI model. For example, if the Pad Thai contains peanuts, the server determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, the device provides positive advice such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[1295] Throughout the entire system, users can receive appropriate advice based on their emotional state as well as whether the food meets their dietary restrictions, making their dining out experience safer and more fulfilling.

[1296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1297] Step 1:

[1298] The user launches the smartphone app.

[1299] Input: The app is launched by user action.

[1300] Output: The app home screen will be displayed and the QR code reading function will be available.

[1301] Specific operation: The user launches the app and taps the "Scan QR code" button that appears on the home screen.

[1302] Step 2:

[1303] The device scans the QR code and obtains the menu ID.

[1304] Input: QR code attached to restaurant menu.

[1305] Output: The menu ID obtained from the QR code is stored in temporary memory.

[1306] Specific operation: The device activates the built-in camera and scans the QR code. The app displays the message "Menu ID acquired."

[1307] Step 3:

[1308] The device acquires dietary restriction information and emotional state and transmits them to the server.

[1309] Input: User's dietary restriction information and emotional state information.

[1310] Output: Send the acquired information to the server.

[1311] Specific operation: The device reads the user's dietary restriction information (such as peanut allergies) from memory, and the emotion engine uses the camera and microphone to analyze the user's emotions from their facial expressions and voice. This information is then sent to the server.

[1312] Step 4:

[1313] The server retrieves the menu ingredient information from a database.

[1314] Input: Menu ID sent from the terminal.

[1315] Output: Ingredient information for the corresponding menu.

[1316] Specific operation: The server queries the database using the menu ID and retrieves the ingredient information for the corresponding menu item.

[1317] Step 5:

[1318] The server uses the generated AI model to match menu ingredient information with dietary restriction information.

[1319] Input: Menu ingredient information and user dietary restriction information.

[1320] Output: A decision on whether the menu is suitable for the user.

[1321] How it works: The server runs an AI model to analyze the menu ingredient information and dietary restriction information and determine whether there are any matching ingredients. For example, if the ingredient information includes peanuts, it will evaluate the food as "not suitable."

[1322] Step 6:

[1323] The server uses an emotion engine to generate advice based on the user's emotional state.

[1324] Input: User's emotional state information.

[1325] Output: Advice based on the user's emotional state.

[1326] Specific operation: The server generates advice based on the analysis results of the emotion engine according to the user's emotional state. For example, if the user is feeling stressed, it generates a message to relax.

[1327] Step 7:

[1328] The server returns the judgment result and advice to the terminal.

[1329] Input: Judgment results from the AI ​​model and advice from the emotion engine.

[1330] Output: The judgment result and advice are sent to the terminal.

[1331] Specific operation: The server sends JSON data containing the judgment result and advice to the terminal via HTTP(S).

[1332] Step 8:

[1333] The terminal displays the judgment result and advice to the user.

[1334] Input: Verification result and advice sent from the server.

[1335] Output: Display in a format suitable for the user.

[1336] Specific operation: The device analyzes the received JSON data and displays a message on the user's smartphone screen such as, "Please avoid this menu item as it contains peanuts. We recommend this salad menu item."

[1337] (Application example 2)

[1338] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1339] When choosing a menu at a restaurant, it is difficult for users with specific dietary restrictions to select an appropriate menu that takes into account their emotional state in addition to their dietary restrictions. Conventional systems only consider dietary restrictions, and therefore lack appropriate advice and suggestions based on the user's emotional state. As a result, users often feel stressed and anxious, resulting in a poor dining out experience. This project aims to solve this problem.

[1340] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code containing menu ingredient information, means for transmitting the read menu ingredient information and the user's emotional state to the server, means for the server to compare the menu ingredient information, the user's dietary restriction information, and emotional state information and use a generative AI model to suggest an appropriate menu, and means for notifying the user of advice based on the judgment results and emotions. This makes it possible to suggest menus that comprehensively take into account the user's dietary restriction information and emotional state, thereby providing a safer and more comfortable dining out experience.

[1341] Key Word Definitions

[1342] "Specific dietary restrictions" refers to specific ingredients or foods that a user should avoid due to allergies or health conditions.

[1343] "Menu ingredient information" refers to data that indicates all ingredients and their details contained in each menu item served at a restaurant.

[1344] "Code" refers to symbols, barcodes, and especially QR codes, that convert information, including menu ingredient information, into a form that can be read by a smartphone or other device.

[1345] "Emotional state" refers to the psychological state, such as stress or happiness, that the user is feeling when selecting a menu, and is analyzed from facial expressions and voice.

[1346] A "generative AI model" is a machine learning method that uses data to suggest menus that are appropriate for the user, and in particular uses artificial intelligence technologies such as neural networks.

[1347] A "server" is a central processing unit that processes data sent from a client terminal and provides necessary information.

[1348] "Advice" refers to recommendations about what meals are suitable or should be avoided based on the user's dietary restrictions and emotional state.

[1349] "Method of reading" refers to the method of obtaining information from a QR code using a smartphone camera or a dedicated scanner.

[1350] "Means of notification" refers to the functions and methods for conveying information to users through devices such as smartphones and tablets.

[1351] "Matching" refers to the process by which the server compares the menu ingredient information with the user's dietary restrictions and / or emotional state information to confirm a match.

[1352] MODE FOR CARRYING OUT THE INVENTION

[1353] The system for implementing this invention is composed of a smartphone app, a server, an emotion engine, and a QR code provided at a restaurant. A specific embodiment of the system is shown below.

[1354] Hardware and Software Configuration

[1355] Hardware

[1356] Smartphone: A device that allows users to use applications.

[1357] QR Code Reader: A function to read QR codes using a smartphone camera.

[1358] Emotion recognition system: Uses the smartphone's camera and microphone to analyze the user's facial expressions and voice.

[1359] software

[1360] Smartphone app: Application that works on iOS and Android.

[1361] Emotion engine: An API for analyzing the user's emotional state, such as Google Cloud's Face API or Microsoft Azure's Emotion API.

[1362] Server-side programs: Web servers such as Apache or NGINX, and back-end programs using Python or Node.js.

[1363] Generative AI model: A machine learning model using TensorFlow, etc., used to make menu suggestions based on the analysis results.

[1364] Process Overview

[1365] Smartphone app

[1366] 1. The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code.

[1367] 2. The smartphone app obtains the menu ID from the scanned QR code and sends it to the server.

[1368] 3. At the same time, the smartphone's camera and microphone capture the user's facial expressions and voice and send them to the emotion engine.

[1369] Server Side

[1370] 1. The server retrieves ingredient information from the database based on the received menu ID.

[1371] 2. The user's dietary restriction information is also obtained from a pre-registered database.

[1372] 3. The emotional state analyzed by the emotion engine is obtained and data is sent to the generative AI model based on this.

[1373] 4. The generative AI model collates ingredient information, dietary restriction information, and emotional state information to generate optimal menu suggestions and emotion-based advice.

[1374] 5. The server sends the generated suggestions and advice back to the smartphone app.

[1375] User Experience

[1376] 1. Shows whether the menu the user scans is suitable for their dietary restrictions and how the menu selection affects the user's emotional state.

[1377] 2. If necessary, we will also provide positive advice and suggest menus that will promote relaxation.

[1378] Specific examples

[1379] For example, consider the case where a user scans a QR code for a "Pad Thai" menu. The user scans the QR code with a smartphone app, and the device sends the "Pad Thai" menu ID and the user's dietary restriction information (e.g., peanut allergy) to the server. At the same time, the emotion engine recognizes the user's emotional state and sends it to the server. The server retrieves the Pad Thai's ingredient information from the database and evaluates its suitability using the generated artificial intelligence. For example, if it contains peanuts, it determines that "this menu item should not be eaten." The result and advice based on the emotion engine's evaluation are sent back to the device, which displays it to the user. If the user's emotional state is stressed, positive advice can also be provided, such as "Avoid this menu item because it contains peanuts. We recommend this salad menu item."

[1380] Prompt Sentence Examples

[1381] Use the following prompt for the generative AI model:

[1382] A user scanned the QR code of a menu item selected at a restaurant. The user's dietary restrictions are: {'Allergies': 'Peanuts'}. The user's current emotional state is: {'Emotion': 'Stressed'}. Generate menu suggestions and advice. Menu ID: '12345'. Ingredients: {'Peanuts': True, ...}.

[1383] Based on this prompt, appropriate menu suggestions and sentiment-based advice can be generated.

[1384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1385] Program processing steps

[1386] Step 1:

[1387] The user launches the smartphone app and uses the QR code reader to scan the restaurant menu QR code. The input is the QR code, and the output is the menu ID. This menu ID is sent to the server in the next step.

[1388] Step 2:

[1389] The device sends the acquired menu ID and the user's dietary restriction information to the server. At the same time, the device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine. The input is the menu ID, dietary restriction information, and emotion data, which are then processed in the next step.

[1390] Step 3:

[1391] The server retrieves the ingredient information of the corresponding menu from the database based on the received menu ID. The input is the menu ID, and the output is ingredient information. This ingredient information is used in the next step.

[1392] Step 4:

[1393] The server retrieves the user's dietary restriction information from the database. The input is the user ID, and the output is the dietary restriction information. This information is then collated in the next step.

[1394] Step 5:

[1395] The server receives the user's emotional state information from the emotion engine. The input is the captured facial and voice data, and the output is the emotional state information, which is then sent to the generative AI model in the next step.

[1396] Step 6:

[1397] The server inputs ingredient information, dietary restriction information, and emotional state information into a generative AI model to generate appropriate menu suggestions and emotional advice. The inputs are the above three pieces of information, and the output is a suggested menu and advice. This generative AI model is built using TensorFlow and other tools.

[1398] Step 7:

[1399] The server returns the generated suggestion menu and advice to the terminal. The input is the suggestion menu and advice, and the output is displayed on the terminal.

[1400] Step 8:

[1401] The device displays the received suggested menu and advice to the user. The input is the suggested menu and advice, and the output is displayed on the user's smartphone screen. In concrete terms, the user can select a menu based on the displayed information.

[1402] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1403] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1404] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1405] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1406] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1407] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1408] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1409] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1410] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1411] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1412] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1413] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1414] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1415] 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.

[1416] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1417] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1418] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1419] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1420] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1421] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1422] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1423] The following is further disclosed regarding the above embodiment.

[1424] (Claim 1)

[1425] A system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on ingredient information included in the menus offered by restaurants,

[1426] means for reading a code containing ingredient information from a menu;

[1427] means for transmitting the read menu ingredient information to a server;

[1428] A means for the server to compare the menu ingredient information with the user's dietary restriction information and determine whether or not the menu is permissible;

[1429] a means for notifying the user of the result of the determination;

[1430] A system including:

[1431] (Claim 2)

[1432] 2. The system according to claim 1, wherein the means for transmitting the read menu ingredient information to the server also transmits the user's dietary restriction information to the server.

[1433] (Claim 3)

[1434] 10. The system of claim 1, wherein the server uses generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information.

[1435] "Example 1"

[1436] (Claim 1)

[1437] A system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on ingredient information included in the menus offered by restaurants,

[1438] means for reading a code containing ingredient information from a menu;

[1439] means for transmitting the read menu ingredient information and the user's dietary restriction information to a server;

[1440] The server uses the generated artificial intelligence to compare the menu ingredient information with the user's dietary restriction information to determine whether the food is edible;

[1441] A means for notifying the user of the decision and the reason for the decision;

[1442] A system including:

[1443] (Claim 2)

[1444] The system according to claim 1, characterized in that the read menu ingredient information and the user's dietary restriction information are transmitted to a server.

[1445] (Claim 3)

[1446] 10. The system of claim 1, wherein the server uses generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information.

[1447] "Application Example 1"

[1448] (Claim 1)

[1449] A system for enabling a user with specific dietary restrictions to determine whether or not they can eat food from information on ingredients contained in the food provided by a restaurant,

[1450] means for reading a code containing information about ingredients of a dish;

[1451] means for transmitting the read cooking ingredient information to a server;

[1452] The server compares the information on ingredients of the food with the user's dietary restriction information to determine whether the food is edible or not.

[1453] a means for notifying the user of the result of the determination;

[1454] a means for visually displaying the results based on an augmented reality function;

[1455] A system including:

[1456] (Claim 2)

[1457] 2. The system according to claim 1, wherein the means for transmitting the read dish ingredient information to the server also transmits the user's dietary restriction information to the server.

[1458] (Claim 3)

[1459] The system according to claim 1, characterized in that the server uses the generated artificial intelligence to compare the dish ingredient information with the user's dietary restriction information.

[1460] "Example 2: Combining Emotion Engines"

[1461] (Claim 1)

[1462] A system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on ingredient information included in the menus offered by restaurants,

[1463] means for reading a code containing ingredient information from a menu;

[1464] means for transmitting the read menu ingredient information and the user's dietary restriction information to a server;

[1465] A means for the server to compare the menu ingredient information with the user's dietary restriction information and determine whether or not the menu is permissible;

[1466] a means for the server to recognize the user's emotional state and provide advice based on that information;

[1467] a means for notifying the user of the judgment results and advice;

[1468] A system including:

[1469] (Claim 2)

[1470] 2. The system according to claim 1, wherein the means for transmitting the read menu ingredient information and the user's dietary restriction information to the server also transmits the user's emotional state information to the server.

[1471] (Claim 3)

[1472] 10. The system of claim 1, wherein the server uses generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information.

[1473] "Application example 2 when combining emotion engines"

[1474] Claims

[1475] (Claim 1)

[1476] A system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on ingredient information included in the menus offered by restaurants,

[1477] means for reading a code containing ingredient information from a menu;

[1478] means for transmitting the read menu ingredient information and the user's emotional state to a server;

[1479] a means for the server to use a generative AI model to compare the menu ingredient information with the user's dietary restriction information and emotional state information to suggest appropriate menu items;

[1480] A means for notifying the user of advice based on the judgment result and emotion;

[1481] A system including:

[1482] (Claim 2)

[1483] 2. The system of claim 1, further comprising: an emotion engine for evaluating the user's emotional state; and transmitting the read menu ingredient information and the user's emotional state information to the server.

[1484] (Claim 3)

[1485] The system of claim 1, characterized in that the server uses the generated artificial intelligence model to make appropriate menu suggestions and emotion-based advice based on menu ingredient information, dietary restriction information, and emotional state information. [Explanation of symbols]

[1486] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system that allows users with specific dietary restrictions to determine whether they can eat the food they are eating based on ingredient information included in the menus offered by restaurants, means for reading a code containing ingredient information from a menu; means for transmitting the read menu ingredient information to a server; A means for the server to compare the menu ingredient information with the user's dietary restriction information and determine whether or not the menu is permissible; a means for notifying the user of the result of the determination; A system including:

2. 2. The system according to claim 1, wherein the means for transmitting the read menu ingredient information to the server also transmits the user's dietary restriction information to the server.

3. 10. The system of claim 1, wherein the server uses generated artificial intelligence to match the menu ingredient information with the user's dietary restriction information.

Images