System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting and storing dietary restrictions and allergy information of a user, a means for collecting menu information of a restaurant and storing it in a database, a means for evaluating the safety degree of a menu on the basis of food materials and allergy information, a means for recommending a restaurant with a high safety degree in a corresponding area in response to a retrieval request of the user, and a means for collecting and analyzing feedback of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's world, it is difficult for people with dietary restrictions or allergies to enjoy eating out safely. In particular, when restaurant menu information is incomplete, it is not easy to confirm the safety of food in advance. Therefore, there is a need for a reliable means for finding restaurants that accommodate dietary restrictions or allergies. The present invention aims to solve these problems and provide an information system that allows users to enjoy eating out safely. [Means for solving the problem]

[0005] The present invention provides a system that includes means for inputting and saving a user's dietary restrictions and allergy information, means for collecting restaurant menu information and storing it in a database, means for evaluating the safety of menu items based on ingredients and allergy information, means for recommending safe restaurants in the area in response to a user's search request, and means for collecting and analyzing user feedback, thereby enabling users to obtain reliable restaurant information and enjoy meals with peace of mind.

[0006] "User" refers to an individual who provides dietary restriction or allergy information and uses the system.

[0007] "Dietary restrictions" refers to an individual's dietary restrictions that require them to avoid certain ingredients or cooking methods.

[0008] "Allergy Information" refers to information regarding an individual's allergic reactions to specific foods or ingredients.

[0009] "Menu information" refers to detailed information about the food served by a restaurant, such as the name, ingredients, and cooking method.

[0010] "Database" refers to an electronic system for storing and managing collected menu information and user dietary restrictions and allergy information.

[0011] "Safety level" refers to an index used to evaluate the risk level for dietary restrictions and allergies.

[0012] "Restaurant" means a food establishment that serves meals.

[0013] "Feedback" refers to information provided to the system by a user regarding their evaluation and impressions of the restaurant they visited.

[0014] "API" stands for Application Program Interface and refers to a standardized means of exchanging information between different software programs.

[0015] "Scraping technology" refers to technology for automatically extracting data from websites.

[0016] "Search request" refers to a request that a user submits to the system to search for specific information. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[0039] Explanation of program processing (in natural language)

[0040] 1. Enter and save user information

[0041] User's device

[0042] Users launch the application and enter their dietary restrictions and allergies on an initial setup screen, including, for example, nut allergies or gluten-free requirements.

[0043] The user may also turn on location services to provide current location information.

[0044] server

[0045] Information sent by the user is encrypted and sent to the server.

[0046] The server uses this information to create an individual profile for the user and stores it in a database.

[0047] 2. Collection and analysis of restaurant information

[0048] server

[0049] The server collects restaurant menu information in real time through the API of food applications and websites, or uses scraping technology to extract the required data if the API is not available.

[0050] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[0051] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[0052] 3. Restaurant Safety Rating

[0053] server

[0054] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[0055] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[0056] 4. Providing Recommendations

[0057] User's device

[0058] Within the application, users can search for restaurants near their current location.

[0059] server

[0060] It receives a user's search request and retrieves relevant restaurant information from a database, including restaurants that cater to specific dietary restrictions or allergies.

[0061] The extracted restaurant list is sorted in descending order of safety score.

[0062] The server generates the final recommendation list and sends it to the user.

[0063] User's device

[0064] Users can view a list of recommended restaurants in the application, including the restaurant name, address, and safety information for each menu item.

[0065] 5. Using User Feedback

[0066] User's device

[0067] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[0068] server

[0069] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[0070] Specific examples

[0071] 1. Parsing menu information by the server

[0072] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[0073] 2. User Recommendations

[0074] If User B has a nut allergy and wants a gluten-free meal, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe.

[0075] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying eating out with peace of mind.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] User's device

[0079] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[0080] Step 2:

[0081] User's device

[0082] The user enables location services and allows the use of current location information.

[0083] This information is encrypted and sent to the server.

[0084] Step 3:

[0085] server

[0086] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[0087] Step 4:

[0088] server

[0089] We use APIs from food applications and websites to collect restaurant menu information in real time, and if APIs are not available, we use scraping technology to extract the required data.

[0090] Step 5:

[0091] server

[0092] The collected menu information is stored in a database along with detailed information such as the names of the dishes included, ingredients, and cooking methods.

[0093] Step 6:

[0094] server

[0095] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[0096] Step 7:

[0097] server

[0098] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[0099] Step 8:

[0100] User's device

[0101] Within the application, users can search for restaurants near their current location.

[0102] Step 9:

[0103] User's device

[0104] A search request is sent to the server.

[0105] Step 10:

[0106] server

[0107] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[0108] Step 11:

[0109] server

[0110] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed to generate a recommendation list.

[0111] Step 12:

[0112] server

[0113] The recommendation list is sent to the user's terminal.

[0114] Step 13:

[0115] User's device

[0116] The recommendation list is displayed on the user's device, where the user can view detailed menu information and safety level breakdowns.

[0117] Step 14:

[0118] User's device

[0119] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[0120] Step 15:

[0121] server

[0122] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[0123] Example 1

[0124] 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."

[0125] Conventional restaurant recommendation systems have difficulty finding suitable restaurants because they are unable to fully consider users' dietary restrictions and allergy information. Furthermore, due to insufficient safety assessment, users with allergies are unable to enjoy eating out with peace of mind. Furthermore, the lack of a mechanism for effectively utilizing user feedback makes it difficult to improve the accuracy of the system.

[0126] 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.

[0127] In this invention, the server includes means for inputting and saving a user's dietary restrictions and allergy information, means for collecting restaurant menu information and storing it in a database, means for analyzing the collected menu information and evaluating the safety of the menu based on the ingredients and allergy information, means for recommending restaurants that meet the search request based on the user's profile information and current location, and means for collecting and analyzing user feedback to improve the accuracy of the system, thereby enabling efficient recommendation of safe restaurants that meet the user's individual dietary restrictions and allergy information.

[0128] "User dietary restrictions" refer to requirements that individual users must meet to avoid certain dishes or ingredients, such as allergies or restrictions on certain ingredients, dietary restrictions based on religion or belief, or intake restrictions based on health conditions or doctor's instructions.

[0129] "Allergy information" is information indicating whether the user has an allergy to a specific food ingredient or seasoning. Specifically, it includes allergies to nuts, gluten, dairy products, shellfish, etc.

[0130] "Menu information" refers to data such as the names, ingredients, cooking methods, prices, and nutritional information of the dishes and drinks served by the restaurant.

[0131] A "database" is a system that stores and manages various types of information in an organized manner, and enables searching and updating. Specific examples include SQL databases and NoSQL databases.

[0132] A "safety rating" is a numerical or ranking indicator of how safe a menu item is for a particular user, based on the ingredients and allergy information contained in the menu item.

[0133] A "search request" is a request for information search made by a user to the system, and appropriate data is extracted based on this request.

[0134] "Recommendation" means that the system provides appropriate options based on the user's requirements and conditions.

[0135] "Feedback" refers to opinions and evaluations provided by users regarding the quality and satisfaction of the services or products they have used.

[0136] "Analysis" is the act of analyzing data in detail to extract information and discover its meaning and trends.

[0137] "Profile information" is a collection of information about an individual user, such as the user's personal information, dietary restrictions and allergy information, and past usage history.

[0138] This invention is a system that recommends restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[0139] Entering and saving user information

[0140] User's device

[0141] A user launches an application and authenticates at the login screen using an email address and password or a social login (e.g., Google or social media). After authentication, the user enters dietary restrictions and allergy information on the initial setup screen. The information is entered as input fields, with specific restrictions such as nut allergies or gluten-free options selected using checkboxes or input boxes. Turning on location services allows the user to provide their current location to the application.

[0142] server

[0143] Data sent by users is encrypted using SSL / TLS and sent to the server. The server receives the encrypted data and decrypts it using an algorithm such as AES before storing it in the database. Based on the decrypted information, a unique user ID is generated and the user's profile is saved in the database. The saved profile information is later used for restaurant recommendations.

[0144] Collection and analysis of restaurant information

[0145] server

[0146] Restaurant information is collected using APIs (e.g., public APIs) from food applications and food review sites. If APIs are not available, the necessary data is obtained using scraping techniques using Python's BeautifulSoup or Selenium. The collected data is stored in a temporary database and cleaned, including filling in missing data and removing duplicate data. After the data is cleaned, the menu information is analyzed and ingredients and cooking methods are extracted using NLP (natural language processing) techniques.

[0147] Restaurant Safety Rating

[0148] server

[0149] Based on the user's profile information, the analyzed menu information is compared and a safety score is assigned. For example, if a user profile has a "nut allergy" and there is a menu item that "contains nuts," the safety score for that menu item will be set low. The safety scores for all menu items are tallied to calculate a safety score for the entire restaurant. The score is saved in a database and will be used the next time the restaurant is evaluated.

[0150] Providing recommendation information

[0151] User's device

[0152] The user opens a search screen within the application to search for nearby restaurants. The search criteria include filter options such as "nut-free" and "gluten-free." When the user presses the search button, a search request is sent to the server.

[0153] server

[0154] The server receives the request, searches the database based on the user's current location and profile criteria, extracts restaurant information that matches the criteria, sorts them by safety score, and generates a final recommendation list that is sent back to the user's device in JSON format.

[0155] User's device

[0156] The user can then review the list of recommendations they receive, which includes the restaurant name, address, and safety information for the menu. For some restaurants, the user can also view ratings and reviews.

[0157] Using User Feedback

[0158] User's device

[0159] Users provide feedback about the restaurant they visited. Feedback is in text input format and includes items such as "food safety" and "quality of service." When they press the "send feedback" button, the feedback is sent to the server.

[0160] server

[0161] The collected feedback is stored in a database and categorised using text analysis, which will be used to improve the accuracy of future safety assessments and restaurant recommendation algorithms.

[0162] Specific examples

[0163] Parsing menu information by the server

[0164] For example, if the server collects menu information from a restaurant and determines that the menu item "grilled chicken" does not contain nuts or gluten, the menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[0165] User Recommendations

[0166] If a user has a nut allergy and wants gluten-free food, when he searches for restaurants near his current location, the server will prioritize recommendations of restaurants that are nut-free and offer gluten-free menu items. For example, a restaurant's "grilled chicken" is included in the recommendation list because it has a high safety rating.

[0167] Prompt Sentence Examples

[0168] "If I have a nut allergy and gluten-free diet, can you recommend some restaurants near my location?"

[0169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0170] Step 1:

[0171] User's device

[0172] A user launches an application and authenticates at a login screen using an email address and password or a social login (e.g., Google or social media).

[0173] Enter your email address, password, or social login details

[0174] Output: Authentication token

[0175] What happens: An authentication token is generated and the user is authenticated.

[0176] Step 2:

[0177] User's device

[0178] After authentication, the user enters information about dietary restrictions and allergies on the initial setup screen.

[0179] Input: Dietary restrictions (e.g., nut allergies, gluten-free), location information

[0180] Output: User's dietary restrictions and location

[0181] Specific operation: Information entered on the device is checked for integrity and then sent to the server.

[0182] Step 3:

[0183] server

[0184] Data sent by the user is encrypted using SSL / TLS and sent to the server.

[0185] Input: Encrypted user data

[0186] Output: Decrypted user data

[0187] Specific operation: The server decrypts the received data using the AES algorithm and stores it in the database along with the user ID.

[0188] Step 4:

[0189] server

[0190] The server periodically collects restaurant menu information using APIs of food applications and food review sites, or uses scraping techniques if APIs are not available.

[0191] Input: Restaurant information obtained from API or data collected by scraping

[0192] Output: Cleaned restaurant menu information

[0193] What happens: The server cleans the data, filling in missing data and removing duplicates.

[0194] Step 5:

[0195] server

[0196] The cleaned data is analyzed and each restaurant's menu information is classified into ingredients and cooking methods using NLP technology.

[0197] Input: Cleaned menu information

[0198] Output: Categorized ingredients and cooking methods

[0199] Specific operation: Runs NLP algorithms to extract ingredients and cooking methods from menu information and saves them in a database.

[0200] Step 6:

[0201] server

[0202] A safety score is calculated based on the user's profile information and analyzed menu information.

[0203] Input: User profile information, menu information

[0204] Output: Safety score

[0205] Specific operation: The ingredient information of each menu item is compared with the user's allergy information, a safety score is calculated, and the result is saved in the database.

[0206] Step 7:

[0207] User's device

[0208] Users can search for nearby restaurants within the application, using filter options to find the right search criteria.

[0209] Input: Search request (current location and dietary restrictions)

[0210] Output: Search result list

[0211] What happens next: A search request is sent to the server.

[0212] Step 8:

[0213] server

[0214] The server receives the user's search request and extracts restaurant information that meets the search criteria.

[0215] Input: Search request, restaurant information in database

[0216] Output: Recommendation list

[0217] What it does: Extracts relevant restaurant information from a database and generates a prioritized list based on safety scores.

[0218] Step 9:

[0219] User's device

[0220] The user checks the list of recommended restaurants displayed on the device, along with safety information for each restaurant.

[0221] Input: Recommendation list sent from the server

[0222] Output: The displayed list of restaurants

[0223] What happens: The application receives the recommendation list and displays it to the user.

[0224] Step 10:

[0225] User's device

[0226] Users provide feedback about the restaurants they visit.

[0227] Input: User feedback (e.g. text data)

[0228] Output: Feedback data to be sent

[0229] What it does: Collects feedback and sends it to the server.

[0230] Step 11:

[0231] server

[0232] The server analyzes the received feedback, stores it in a database, and uses it for analysis to improve the accuracy of the system.

[0233] Input: User feedback

[0234] Output: Analysis results and updated evaluation data

[0235] Specific operation: The feedback content is classified using text analysis, and the feedback information is reflected in future safety assessments and recommendation algorithms.

[0236] (Application example 1)

[0237] 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."

[0238] Conventional systems only recommend restaurants that can provide safe meals based on a user's dietary restrictions and allergies, but lack systems that support delivery services. This limits the options available to users for obtaining safe meals at home or at work. Furthermore, the lack of an integrated method for efficiently recommending delivery services that meet a user's dietary restrictions makes it difficult to improve the user experience.

[0239] 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.

[0240] In this invention, the server includes a means for inputting and saving a user's dietary restriction and allergy information, a means for collecting and storing menu information from restaurants and delivery services in a database, a means for evaluating the safety of menu items based on ingredients and allergy information, a means for recommending restaurants or delivery services with a high safety rating in the area in response to a user's search request, and a means for collecting and analyzing user feedback. This allows users to easily select safe meals and enjoy meals at any location, such as their home or workplace. Furthermore, by utilizing user feedback to improve the accuracy of recommendations in future periods, the system can be continuously improved.

[0241] "Means for inputting and saving information about a user's dietary restrictions and allergies" is a function that allows a user to input information about their own dietary restrictions and allergies and save that information within the system.

[0242] The "means for collecting restaurant menu information and storing it in a database" is a function for collecting menu information from external restaurants and storing it in a database.

[0243] The "means for evaluating the safety of menu items based on ingredient and allergy information" is a function that compares the collected ingredient information of menu items with the user's allergy information to evaluate how safe each menu item is for the user.

[0244] "Means for recommending safe restaurants in the area in response to a user's search request" is a function that receives a search request from a user and recommends safe restaurants in the area based on that information.

[0245] The "means for collecting and analyzing user feedback" is a function that collects feedback information provided by users and analyzes it to help improve the system.

[0246] The "means for recommending delivery services" is a function that recommends delivery services that can provide safe meals based on the user's dietary restrictions and allergy information.

[0247] "Use the user's location information to search for restaurants or delivery services near the current location" is a function that uses the user's current location information to search for safe restaurants and delivery services in the vicinity.

[0248] "Collecting menu information from restaurants and delivery services using APIs or scraping technology" refers to a method of collecting menu information from restaurants and delivery services externally using APIs or scraping technology.

[0249] This invention relates to a system that recommends restaurants and delivery services that can provide safe meals based on a user's dietary restrictions and allergy information. The system operates using a server, user terminals, databases, APIs, scraping technology, etc.

[0250] The server does the following:

[0251] 1. Enter and save your user information:

[0252] Users enter their dietary restrictions and allergy information using a device such as a smartphone. This information, along with the user's location information, is sent to a server, encrypted, and then stored in a database.

[0253] 2. Collection of restaurant and delivery service menu information:

[0254] The server uses external APIs and scraping technology to collect menu information from restaurants and delivery services, and stores the collected information in a database.

[0255] 3. Menu Safety Rating:

[0256] The server compares the collected information on ingredients in the menu with the user's allergy information and executes an algorithm to evaluate the safety of the food. The results of this evaluation are stored in a database.

[0257] 4. Providing Recommendations:

[0258] When a user searches for restaurants or delivery services near their current location, the server prioritizes and recommends safe options that meet the user's dietary restrictions. This information is displayed on the user's device.

[0259] 5. Collecting and Analyzing User Feedback:

[0260] Users provide feedback about the restaurants they visit and the delivery services they use, which is sent to the server and analyzed to help improve the system's recommendations.

[0261] Examples:

[0262] When a user with a nut allergy searches for gluten-free meals near their current location, the server compares the collected menu information with the user's allergy information and recommends restaurants and delivery services that offer nut-free and gluten-free menus, allowing the user to choose a safe meal.

[0263] Example prompt sentence:

[0264] When a user searches for "nut allergy" and "gluten-free," the server provides a list of recommended restaurants and delivery services. For example, it might recommend "restaurants that serve gluten-free grilled chicken that doesn't contain nuts."

[0265] The specific hardware and software used includes a smartphone, a server, an SQLite database, Flask (a web server framework), and scraping technology, as well as using APIs to obtain external data.

[0266] By using generative AI models and prompt sentences, it is possible to further improve the analysis of user feedback and the recommendation algorithm, thus realizing a system that increases the reliability and convenience of providing users with safe and comfortable meals.

[0267] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0268] Step 1:

[0269] The user enters their dietary restrictions and allergy information and sends it from their device to the server. The user uses a smartphone app to enter information such as nut allergies and gluten-free eating, and turns on location services. This information is encrypted and sent to the server. The input data is dietary restrictions, allergy information, and location information, and the output is encrypted user profile data.

[0270] Step 2:

[0271] The server stores the received user information in a database. The server decrypts the received encrypted data and stores the user's dietary restrictions, allergy information, and location information in a database. The input data is the encrypted user profile data, and the output is the user profile stored in the database.

[0272] Step 3:

[0273] The server uses API or scraping technology to collect menu information from restaurants and delivery services. It obtains menu information in real time using APIs provided by external services or collects menu information through web scraping. The input data is the API response or scraping results, and the output is the collected menu information.

[0274] Step 4:

[0275] The server stores the collected menu information in a database. The server automatically stores the acquired menu information in a database for subsequent processing. The input data is the collected menu information, and the output is the menu information stored in the database.

[0276] Step 5:

[0277] The server evaluates the safety of the menu items. It matches the collected menu information with the user's allergy information and runs a safety evaluation algorithm. For example, a menu item containing nuts will be evaluated as less safe for a user with a nut allergy. The input data are the user profile and menu information, and the output is a safety evaluation score for each menu item.

[0278] Step 6:

[0279] The server recommends safe restaurants and delivery services in the area in response to a user's search request. When a user searches for safe meals near their current location, the server generates a recommendation list based on the safety scores of the corresponding menu items from the database. The input data is the user's search request and current location information, and the output is a recommended list of safe restaurants and delivery services.

[0280] Step 7:

[0281] The user selects a restaurant or delivery service from the recommended list and visits or orders. The user selects a restaurant or delivery service that provides safe meals from the provided recommended list and uses it. The input data is the recommended list, and the output is information about the selected restaurant or delivery service.

[0282] Step 8:

[0283] Users provide feedback and send it to the server. Feedback about the safety of food and the quality of service is input from the terminal and sent to the server. This feedback is saved as learning data for the system and used to improve the accuracy of recommendations in the future. The input data is the user's feedback, and the output is the feedback information saved in the database.

[0284] In this way, the present invention realizes a system that efficiently recommends restaurants and delivery services that offer safe meals based on the user's dietary restrictions and allergy information.

[0285] 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.

[0286] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine for recognizing the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface available on the user's device.

[0287] Explanation of program processing (in natural language)

[0288] 1. Enter and save user information

[0289] User's device

[0290] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[0291] The user may also turn on location services to provide current location information.

[0292] server

[0293] The information sent by the user is encrypted before being sent to the server.

[0294] The server uses this information to create an individual profile for the user and stores it in a database.

[0295] 2. Collection and analysis of restaurant information

[0296] server

[0297] Use APIs from food applications and websites to collect restaurant menu information in real time, or use scraping techniques to extract the required data if APIs are not available.

[0298] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[0299] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[0300] 3. Restaurant Safety Rating

[0301] server

[0302] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[0303] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[0304] 4. Providing Recommendations

[0305] User's device

[0306] Within the application, users can search for restaurants near their current location.

[0307] User's device

[0308] Emotional data is collected using the built-in camera and microphone to understand the user's current emotional state.

[0309] server

[0310] It receives a user's search request and emotion data and extracts relevant restaurants from the database.

[0311] The system generates a list of appropriate restaurants based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants.

[0312] The extracted restaurant list is optimized taking into account the safety score and sent to the user.

[0313] User's device

[0314] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[0315] 5. Using User Feedback

[0316] User's device

[0317] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[0318] server

[0319] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[0320] Specific examples

[0321] 1. Parsing menu information by the server

[0322] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[0323] 2. User Recommendations

[0324] If User B has a nut allergy, wants a gluten-free diet, and has recently been stressed, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe and has a quiet, relaxing environment.

[0325] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying dining out with peace of mind. In addition, by utilizing the user's emotional information, more personalized recommendations become possible.

[0326] The processing flow will be explained below.

[0327] Step 1:

[0328] User's device

[0329] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[0330] Step 2:

[0331] User's device

[0332] The user enables location services and allows the use of current location information.

[0333] This information is encrypted and sent to the server.

[0334] Step 3:

[0335] server

[0336] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[0337] Step 4:

[0338] server

[0339] Use APIs to collect restaurant menu information from food applications and websites in real time, or use scraping techniques to extract the required data if APIs are not available.

[0340] Step 5:

[0341] server

[0342] The collected menu information is stored in a database along with detailed dish names, ingredients, cooking methods, etc.

[0343] Step 6:

[0344] server

[0345] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[0346] Step 7:

[0347] server

[0348] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[0349] Step 8:

[0350] User's device

[0351] Within the application, users can search for restaurants near their current location.

[0352] Step 9:

[0353] User's device

[0354] A search request is sent to the server.

[0355] Step 10:

[0356] server

[0357] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[0358] Step 11:

[0359] server

[0360] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed.

[0361] Step 12:

[0362] server

[0363] To take into account the user's emotional state, the emotion engine analyzes emotional data obtained from the user's device, including data collected through the built-in camera and microphone.

[0364] Step 13:

[0365] server

[0366] Optimize the recommendation list based on the user's emotional state: for example, prioritize quiet, relaxing restaurants if the user is feeling stressed.

[0367] Step 14:

[0368] server

[0369] A final recommendation list is generated and sent to the user's terminal.

[0370] Step 15:

[0371] User's device

[0372] The recommendation list is displayed on the user's device, where they can see detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[0373] Step 16:

[0374] User's device

[0375] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[0376] Step 17:

[0377] server

[0378] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[0379] Example 2

[0380] 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."

[0381] To enjoy eating out with peace of mind, users need to find safe restaurants based on dietary restrictions and allergies. However, this process is cumbersome, and it can be difficult to obtain appropriate information. Furthermore, because the user's emotional state also influences the selection of an appropriate restaurant, recommendations that take into account the user's current emotional state, rather than just ingredient information, are needed. Furthermore, it is a challenge to continuously improve recommendation accuracy by effectively utilizing user feedback.

[0382] 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.

[0383] In this invention, the server includes means for inputting and saving a user's dietary restriction and allergy information, means for collecting restaurant menu information and storing it in a database, means for evaluating the safety of menu items based on ingredient and allergy information, means for recommending safe restaurants in the area in response to a user's search request, means for collecting user emotional information to improve recommendation accuracy, and means for collecting and analyzing user feedback. This allows users to not only find safe restaurants that meet their dietary restrictions and allergies, but also receive recommendations that suit their emotional state at the time, allowing them to enjoy eating out with peace of mind. Furthermore, collecting and analyzing feedback information enables continuous improvement in recommendation accuracy.

[0384] "User's dietary restrictions and allergy information" refers to information about a user's inability to or restrictions on the intake of certain ingredients, such as nut allergies, gluten-free diets, or vegan diets.

[0385] "Storage means" refers to devices or software that record the entered information in a safe and appropriate location and make it available for retrieval when necessary.

[0386] "Restaurant menu information" refers to information about the dishes served at each restaurant, their ingredients, cooking methods, etc.

[0387] "Means of collection" refers to devices or software that obtain necessary information from outside using APIs, scraping technology, etc.

[0388] A "database" refers to a data repository that stores collected information systematically and allows it to be searched and used as needed.

[0389] "Methods for evaluating the safety of menu items based on ingredient and allergy information" refers to the process or algorithm that analyzes the ingredients contained in each menu item and compares them with the user's allergy information to quantify the safety of the menu item.

[0390] "User search request" means an input or instruction a User makes using a Device to search for specific information.

[0391] The "relevant area" refers to the location information of the device currently being operated by the user or the area specified by the user.

[0392] A "safe restaurant" refers to a restaurant that has been rated as safe in light of the user's dietary restrictions and allergy information.

[0393] "Recommendation means" refers to the system's functions and algorithms that extract restaurants that meet the criteria from the database and present them to the user.

[0394] "User's emotional information" is data that represents the user's current psychological state and mood, and refers to information acquired from facial expressions, tone of voice, speech content, and the like.

[0395] "Means to improve recommendation accuracy" refers to algorithms and system functions that incorporate users' emotional information to suggest more appropriate restaurants.

[0396] "Means for collecting and analyzing feedback" refers to the process or system for storing user-provided opinions and ratings in a database and analyzing that data.

[0397] This invention is a system that recommends restaurants that can provide safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine that recognizes the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface that can be used on the user's device.

[0398] Entering and saving user information

[0399] User's device

[0400] Users launch the smartphone application and enter their dietary restrictions and allergies on the initial setup screen. For example, they can enter information such as nut allergies, gluten-free diets, or vegan diets. Next, they authorize the use of location services, which then uses the GPS function to collect their current location.

[0401] server

[0402] The server receives the dietary restriction information and current location information sent by the user. The received data is encrypted and stored securely. A user profile is created in the database and the entered information is stored.

[0403] Collection and analysis of restaurant information

[0404] server

[0405] The server collects restaurant menu information in real time from external food applications and websites using APIs. If APIs are not available, it uses web scraping technology to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to parse the ingredient list for each menu item and match it with the user's allergy information.

[0406] Specific working example:

[0407] The server confirms that there is a dish called "grilled chicken" on the menu and that it is nut- and gluten-free.

[0408] Restaurant Safety Rating

[0409] server

[0410] The server compares the user's profile information stored in the database with the restaurant's menu information, checking whether the ingredients in each menu item match the user's allergy information. It then scores the safety of the menu items, rating, for example, "dishes containing nuts" as low safety for users with nut allergies. The safety score for the entire restaurant is then tallied and stored in the database.

[0411] Specific working example:

[0412] The server rates the safety of "grilled chicken" at 95 out of 100, and takes into account the restaurant's overall safety rating even if other high-risk menu items are present.

[0413] Providing recommendation information

[0414] User's device

[0415] The user opens the application and searches for nearby restaurants. The application collects the user's emotional data (e.g., facial expressions, tone of voice) using the device's built-in camera and microphone.

[0416] server

[0417] The server receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize quiet and relaxing restaurants. Finally, it sends the user an optimized restaurant list that also takes safety scores into account.

[0418] User's device

[0419] A list of recommended restaurants will be displayed on the user's device, where the user can view detailed menu information, safety ratings, and special recommendation reasons based on sentiment.

[0420] Specific working example:

[0421] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[0422] Using User Feedback

[0423] User's device

[0424] Users enter feedback about the restaurants they visit in the application, including, for example, food safety, quality of service, and reactions to specific ingredients.

[0425] server

[0426] The server stores the received feedback in a database and analyzes the aggregated feedback data to use as training data for the system, thereby improving the accuracy of future safety assessments and restaurant recommendations.

[0427] Specific working example:

[0428] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[0429] Prompt Sentence Examples

[0430] Below are some example prompts to input to a generative AI model:

[0431] "Please suggest restaurants to a user who has a nut allergy and wants gluten-free food, and has been feeling stressed lately. The user's current location is around Tokyo Station."

[0432] Based on this prompt, the system can recommend an appropriate restaurant.

[0433] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0434] Step 1: Enter and save user information

[0435] User's device

[0436] 1. The user launches the application on their smartphone.

[0437] 2. On the initial setup screen, enter your dietary restrictions (e.g., nut allergies, gluten-free, vegan, etc.) and allergy information. This will be your input data.

[0438] 3. Next, allow location services to collect your location using the GPS function.

[0439] 4. This information is sent to the server, and the output is encrypted user information and current location information.

[0440] server

[0441] 5. The server receives the dietary restriction information and current location information sent by the user. The input is the encrypted user information and current location information.

[0442] 6. Decrypt and securely store the received data.

[0443] 7. Create a user profile in the database and store the entered information. The output is a user profile stored in the database.

[0444] Step 2: Collect and analyze restaurant information

[0445] server

[0446] 1. The server collects restaurant menu information from external food applications and websites using APIs. If APIs are not available, scraping techniques are used. The input is the menu information collected via APIs.

[0447] 2. Automatically store the collected menu information in a database. The output is restaurant menu information stored in a database.

[0448] 3. The server uses an analysis algorithm to analyze the ingredient list for each menu item and compares the entered menu information with the user's allergy information. The input is the user's allergy information stored in the database and the restaurant's menu information.

[0449] 4. The safety of the menu is evaluated by comparing ingredients with allergy information and stored in a database. The output is the safety of the evaluated menu.

[0450] Specific operation example

[0451] The server confirms that the dish "grilled chicken" is nut- and gluten-free and rates it "highly safe."

[0452] Step 3: Restaurant Safety Assessment

[0453] server

[0454] 1. Compare the user profile information stored in the database with restaurant menu information. The input data is the user profile information and restaurant menu information.

[0455] 2. Check whether the ingredients in each menu match the user's allergy information.

[0456] 3. Score the safety of the menu items, for example, "dishes containing nuts" are rated as less safe for users with nut allergies. The output is a safety score for each menu item.

[0457] 4. The safety score of the entire restaurant is calculated and stored in a database. The output is the overall safety score of the restaurant.

[0458] Specific operation example

[0459] The server rates the safety of the "grilled chicken" at 95 out of 100, which determines the restaurant's overall safety rating.

[0460] Step 4: Provide a recommendation

[0461] User's device

[0462] 1. A user opens the application and searches for nearby restaurants. The input is the user's search request.

[0463] 2. Collect user emotion data using the device's built-in camera and microphone. The input is the collected emotion data.

[0464] 3. This information is sent to the server, and the output is an encrypted search request and sentiment data.

[0465] server

[0466] 4. The server receives the user's search request and emotion data. The input is the encrypted search request and emotion data.

[0467] 5. Extract relevant restaurants from the database. The input is the user's search request.

[0468] 6. Generate an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, prioritize quiet and relaxing restaurants. The output is a restaurant list optimized for the user's emotional state.

[0469] 7. The search results are optimized taking into account the safety score and sent to the user.

[0470] User's device

[0471] 8. The recommended restaurant list is displayed. The user can see detailed menu information, safety ratings, and special recommendation reasons based on sentiment. The output is the displayed restaurant list.

[0472] Specific operation example

[0473] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[0474] Step 5: Use user feedback

[0475] User's device

[0476] 1. A user enters feedback about a restaurant they visited in the application. The input is feedback information.

[0477] 2. Send the feedback to the server. The output is the encrypted feedback information.

[0478] server

[0479] 3. The server stores the received feedback in a database and analyzes the aggregated feedback data. The input is the encrypted feedback information.

[0480] 4. The feedback data is used as training data for the system, which will improve the accuracy of future safety assessments and restaurant recommendations. The output is the analyzed feedback information.

[0481] Specific operation example

[0482] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[0483] (Application example 2)

[0484] 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."

[0485] Conventional food recommendation systems are limited to recommending restaurants based on a user's dietary restrictions and allergies, and do not consider individual emotional states. This makes it difficult to recommend restaurants and menus that suit a user's physical and mental state. To solve this problem, there is a need for more personalized recommendations that utilize the user's emotional information.

[0486] 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 recognizing user emotional information to improve recommendation accuracy, means for recommending restaurants with high safety ratings in the relevant area in response to the user's search request, and means for collecting and analyzing user feedback. This makes it possible to recommend restaurants and menus that are personalized according to the user's emotional state.

[0487] "User's dietary restrictions and allergy information" refers to information that indicates when a user needs to avoid certain ingredients or when a user's food choices are restricted based on their health condition.

[0488] "Menu information" refers to detailed information such as the name, ingredients, cooking method, and price of the dishes served at a restaurant.

[0489] A "database" is a set of data storage mechanisms for efficiently storing, managing, and retrieving collected information.

[0490] "Safety level" is a score that evaluates how low a risk a menu item poses to a user's dietary restrictions or allergy information.

[0491] "Emotional information" is data that indicates the user's psychological state and emotions, and is information collected from the user's facial expressions, tone of voice, and the like.

[0492] "Recommendation accuracy" refers to the system's ability to select restaurants and menus that best suit the user's conditions and emotions.

[0493] A "search request" is a request made by a user to the system for specific information.

[0494] "Feedback" refers to users providing ratings and comments about restaurants and menus they have actually visited.

[0495] This invention is a system that recommends appropriate restaurants and menus based on a user's dietary restrictions, allergy information, and emotional information. The system operates as follows.

[0496] 1. Enter and save user information

[0497] User device:

[0498] Users launch the app and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, or vegan diets. They can also turn on location services to provide their current location.

[0499] server:

[0500] The information sent by the user is encrypted and sent to the server, which uses this information to create an individual profile for the user and stores it in a database.

[0501] 2. Collection and analysis of restaurant information

[0502] server:

[0503] Restaurant menu information is collected in real time from food applications and websites using APIs. If APIs are not available, the necessary data is extracted using scraping technology. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[0504] 3. Restaurant Safety Rating

[0505] server:

[0506] The system compares the user's profile information with the menu information stored in the database and scores the safety of each menu item. For example, a menu item containing nuts is rated as less safe for a user with a nut allergy. The safety score for the entire restaurant is then aggregated and stored in the database. This information is used later in the recommendation process.

[0507] 4. Providing Recommendations

[0508] User device:

[0509] Within the application, users can search for restaurants near their current location, and the app also uses the built-in camera and microphone to collect emotional data to recognize the user's current emotional state.

[0510] server:

[0511] The system receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates a list of appropriate restaurants taking into account the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants. The extracted restaurant list is then optimized, taking into account the safety score, and sent to the user.

[0512] User device:

[0513] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[0514] 5. Using User Feedback

[0515] User device:

[0516] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[0517] server:

[0518] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[0519] Specific examples

[0520] Parsing menu information by the server

[0521] For example, the server collects menu information from a restaurant and analyzes the ingredients in each menu item. If it determines that a menu item called "grilled chicken" does not contain nuts or gluten, it will rate this menu as safe for customers with nut allergies or who are gluten-free.

[0522] User Recommendations

[0523] If a user has a nut allergy, wants to eat gluten-free, and has recently been stressed, they can search for restaurants near their current location and the system will recommend restaurants that offer gluten-free menus, are nut-free, and have a quiet, relaxing environment.

[0524] Prompt Sentence Examples

[0525] "Recommend restaurants that offer safe meals based on dietary restrictions and allergies. If the user is stressed, prioritize quiet and relaxing restaurants. For example, if the user is vegan and has a nut allergy, generate a list of restaurants based on this information. Don't forget to take emotional information into account."

[0526] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0527] Step 1:

[0528] User device:

[0529] The user starts the application and enters their dietary restrictions and allergy information on the initial setup screen. The entered information may include, for example, nut allergies, gluten-free diets, or vegan diets. This information is sent to the server as input data.

[0530] Step 2:

[0531] server:

[0532] The dietary restrictions and allergy information submitted by the user is encrypted and sent to the server. The server receives this information, creates an individual profile for the user, and stores it in a database. The input data is output as saved profile information.

[0533] Step 3:

[0534] server:

[0535] The server uses APIs from food applications and websites to collect restaurant menu information in real time. If APIs are not available, scraping techniques are used to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is stored in a database. The input data is restaurant information, and the output data is the saved menu information.

[0536] Step 4:

[0537] server:

[0538] The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment. The input data is the menu information and the user's profile information, and the output data is a safety score for each menu item. Specifically, the server checks whether the allergens match the ingredient list.

[0539] Step 5:

[0540] User device:

[0541] A user searches for restaurants near their current location within the application. The device obtains the current location information and sends a search request to the server. The input data is the search request, and the output data is a list of search results from the server.

[0542] Step 6:

[0543] User device:

[0544] To understand the user's current emotional state, emotional data is collected using the built-in camera and microphone. The device then transmits the collected emotional data to a server. The input data is emotional state information, and the output data is a notification of completion of transmission to the server.

[0545] Step 7:

[0546] server:

[0547] It receives the user's search request and emotional data and extracts relevant restaurants from the database. It generates a list of suitable restaurants taking into account the user's emotional state. The input data is the search request and emotional state information, and the output data is an optimized recommendation list. Specifically, the emotion engine analyzes the emotional data and matches the restaurant's environmental information with the emotional state.

[0548] Step 8:

[0549] User device:

[0550] The recommendation list is displayed on the user's device, where the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.The input data is the recommendation list sent from the server, and the output data is the displayed restaurant information.

[0551] Step 9:

[0552] User device:

[0553] A user provides feedback about a restaurant they visited. The input data is the feedback information, and the output data is a notification that the feedback has been sent.

[0554] Step 10:

[0555] server:

[0556] The collected feedback is stored in a database and used as training data for the system. The input data is the user's feedback information, and the output data is the updated training data. Specifically, the feedback information is analyzed and used to improve the recommendation algorithm next time.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Second embodiment]

[0561] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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."

[0573] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[0574] Explanation of program processing (in natural language)

[0575] 1. Enter and save user information

[0576] User's device

[0577] Users launch the application and enter their dietary restrictions and allergies on an initial setup screen, including, for example, nut allergies or gluten-free requirements.

[0578] The user may also turn on location services to provide current location information.

[0579] server

[0580] Information sent by the user is encrypted and sent to the server.

[0581] The server uses this information to create an individual profile for the user and stores it in a database.

[0582] 2. Collection and analysis of restaurant information

[0583] server

[0584] The server collects restaurant menu information in real time through the API of food applications and websites, or uses scraping technology to extract the required data if the API is not available.

[0585] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[0586] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[0587] 3. Restaurant Safety Rating

[0588] server

[0589] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[0590] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[0591] 4. Providing Recommendations

[0592] User's device

[0593] Within the application, users can search for restaurants near their current location.

[0594] server

[0595] It receives a user's search request and retrieves relevant restaurant information from a database, including restaurants that cater to specific dietary restrictions or allergies.

[0596] The extracted restaurant list is sorted in descending order of safety score.

[0597] The server generates the final recommendation list and sends it to the user.

[0598] User's device

[0599] Users can view a list of recommended restaurants in the application, including the restaurant name, address, and safety information for each menu item.

[0600] 5. Using User Feedback

[0601] User's device

[0602] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[0603] server

[0604] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[0605] Specific examples

[0606] 1. Parsing menu information by the server

[0607] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[0608] 2. User Recommendations

[0609] If User B has a nut allergy and wants a gluten-free meal, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe.

[0610] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying eating out with peace of mind.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] User's device

[0614] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[0615] Step 2:

[0616] User's device

[0617] The user enables location services and allows the use of current location information.

[0618] This information is encrypted and sent to the server.

[0619] Step 3:

[0620] server

[0621] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[0622] Step 4:

[0623] server

[0624] We use APIs from food applications and websites to collect restaurant menu information in real time, and if APIs are not available, we use scraping technology to extract the required data.

[0625] Step 5:

[0626] server

[0627] The collected menu information is stored in a database along with detailed information such as the names of the dishes included, ingredients, and cooking methods.

[0628] Step 6:

[0629] server

[0630] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[0631] Step 7:

[0632] server

[0633] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[0634] Step 8:

[0635] User's device

[0636] Within the application, users can search for restaurants near their current location.

[0637] Step 9:

[0638] User's device

[0639] A search request is sent to the server.

[0640] Step 10:

[0641] server

[0642] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[0643] Step 11:

[0644] server

[0645] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed to generate a recommendation list.

[0646] Step 12:

[0647] server

[0648] The recommendation list is sent to the user's terminal.

[0649] Step 13:

[0650] User's device

[0651] The recommendation list is displayed on the user's device, where the user can view detailed menu information and safety level breakdowns.

[0652] Step 14:

[0653] User's device

[0654] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[0655] Step 15:

[0656] server

[0657] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[0658] Example 1

[0659] 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."

[0660] Conventional restaurant recommendation systems have difficulty finding suitable restaurants because they are unable to fully consider users' dietary restrictions and allergy information. Furthermore, due to insufficient safety assessment, users with allergies are unable to enjoy eating out with peace of mind. Furthermore, the lack of a mechanism for effectively utilizing user feedback makes it difficult to improve the accuracy of the system.

[0661] 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.

[0662] In this invention, the server includes means for inputting and saving a user's dietary restrictions and allergy information, means for collecting restaurant menu information and storing it in a database, means for analyzing the collected menu information and evaluating the safety of the menu based on the ingredients and allergy information, means for recommending restaurants that meet the search request based on the user's profile information and current location, and means for collecting and analyzing user feedback to improve the accuracy of the system, thereby enabling efficient recommendation of safe restaurants that meet the user's individual dietary restrictions and allergy information.

[0663] "User dietary restrictions" refer to requirements that individual users must meet to avoid certain dishes or ingredients, such as allergies or restrictions on certain ingredients, dietary restrictions based on religion or belief, or intake restrictions based on health conditions or doctor's instructions.

[0664] "Allergy information" is information indicating whether the user has an allergy to a specific food ingredient or seasoning. Specifically, it includes allergies to nuts, gluten, dairy products, shellfish, etc.

[0665] "Menu information" refers to data such as the names, ingredients, cooking methods, prices, and nutritional information of the dishes and drinks served by the restaurant.

[0666] A "database" is a system that stores and manages various types of information in an organized manner, and enables searching and updating. Specific examples include SQL databases and NoSQL databases.

[0667] A "safety rating" is a numerical or ranking indicator of how safe a menu item is for a particular user, based on the ingredients and allergy information contained in the menu item.

[0668] A "search request" is a request for information search made by a user to the system, and appropriate data is extracted based on this request.

[0669] "Recommendation" means that the system provides appropriate options based on the user's requirements and conditions.

[0670] "Feedback" refers to opinions and evaluations provided by users regarding the quality and satisfaction of the services or products they have used.

[0671] "Analysis" is the act of analyzing data in detail to extract information and discover its meaning and trends.

[0672] "Profile information" is a collection of information about an individual user, such as the user's personal information, dietary restrictions and allergy information, and past usage history.

[0673] This invention is a system that recommends restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[0674] Entering and saving user information

[0675] User's device

[0676] A user launches an application and authenticates at the login screen using an email address and password or a social login (e.g., Google or social media). After authentication, the user enters dietary restrictions and allergy information on the initial setup screen. The information is entered as input fields, with specific restrictions such as nut allergies or gluten-free options selected using checkboxes or input boxes. Turning on location services allows the user to provide their current location to the application.

[0677] server

[0678] Data sent by users is encrypted using SSL / TLS and sent to the server. The server receives the encrypted data and decrypts it using an algorithm such as AES before storing it in the database. Based on the decrypted information, a unique user ID is generated and the user's profile is saved in the database. The saved profile information is later used for restaurant recommendations.

[0679] Collection and analysis of restaurant information

[0680] server

[0681] Restaurant information is collected using APIs (e.g., public APIs) from food applications and food review sites. If APIs are not available, the necessary data is obtained using scraping techniques using Python's BeautifulSoup or Selenium. The collected data is stored in a temporary database and cleaned, including filling in missing data and removing duplicate data. After the data is cleaned, the menu information is analyzed and ingredients and cooking methods are extracted using NLP (natural language processing) techniques.

[0682] Restaurant Safety Rating

[0683] server

[0684] Based on the user's profile information, the analyzed menu information is compared and a safety score is assigned. For example, if a user profile has a "nut allergy" and there is a menu item that "contains nuts," the safety score for that menu item will be set low. The safety scores for all menu items are tallied to calculate a safety score for the entire restaurant. The score is saved in a database and will be used the next time the restaurant is evaluated.

[0685] Providing recommendation information

[0686] User's device

[0687] The user opens a search screen within the application to search for nearby restaurants. The search criteria include filter options such as "nut-free" and "gluten-free." When the user presses the search button, a search request is sent to the server.

[0688] server

[0689] The server receives the request, searches the database based on the user's current location and profile criteria, extracts restaurant information that matches the criteria, sorts them by safety score, and generates a final recommendation list that is sent back to the user's device in JSON format.

[0690] User's device

[0691] The user can then review the list of recommendations they receive, which includes the restaurant name, address, and safety information for the menu. For some restaurants, the user can also view ratings and reviews.

[0692] Using User Feedback

[0693] User's device

[0694] Users provide feedback about the restaurant they visited. Feedback is in text input format and includes items such as "food safety" and "quality of service." When they press the "send feedback" button, the feedback is sent to the server.

[0695] server

[0696] The collected feedback is stored in a database and categorised using text analysis, which will be used to improve the accuracy of future safety assessments and restaurant recommendation algorithms.

[0697] Specific examples

[0698] Parsing menu information by the server

[0699] For example, if the server collects menu information from a restaurant and determines that the menu item "grilled chicken" does not contain nuts or gluten, the menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[0700] User Recommendations

[0701] If a user has a nut allergy and wants gluten-free food, when he searches for restaurants near his current location, the server will prioritize recommendations of restaurants that are nut-free and offer gluten-free menu items. For example, a restaurant's "grilled chicken" is included in the recommendation list because it has a high safety rating.

[0702] Prompt Sentence Examples

[0703] "If I have a nut allergy and gluten-free diet, can you recommend some restaurants near my location?"

[0704] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0705] Step 1:

[0706] User's device

[0707] A user launches an application and authenticates at a login screen using an email address and password or a social login (e.g., Google or social media).

[0708] Enter your email address, password, or social login details

[0709] Output: Authentication token

[0710] What happens: An authentication token is generated and the user is authenticated.

[0711] Step 2:

[0712] User's device

[0713] After authentication, the user enters information about dietary restrictions and allergies on the initial setup screen.

[0714] Input: Dietary restrictions (e.g., nut allergies, gluten-free), location information

[0715] Output: User's dietary restrictions and location

[0716] Specific operation: Information entered on the device is checked for integrity and then sent to the server.

[0717] Step 3:

[0718] server

[0719] Data sent by the user is encrypted using SSL / TLS and sent to the server.

[0720] Input: Encrypted user data

[0721] Output: Decrypted user data

[0722] Specific operation: The server decrypts the received data using the AES algorithm and stores it in the database along with the user ID.

[0723] Step 4:

[0724] server

[0725] The server periodically collects restaurant menu information using APIs of food applications and food review sites, or uses scraping techniques if APIs are not available.

[0726] Input: Restaurant information obtained from API or data collected by scraping

[0727] Output: Cleaned restaurant menu information

[0728] What happens: The server cleans the data, filling in missing data and removing duplicates.

[0729] Step 5:

[0730] server

[0731] The cleaned data is analyzed and each restaurant's menu information is classified into ingredients and cooking methods using NLP technology.

[0732] Input: Cleaned menu information

[0733] Output: Categorized ingredients and cooking methods

[0734] Specific operation: Runs NLP algorithms to extract ingredients and cooking methods from menu information and saves them in a database.

[0735] Step 6:

[0736] server

[0737] A safety score is calculated based on the user's profile information and analyzed menu information.

[0738] Input: User profile information, menu information

[0739] Output: Safety score

[0740] Specific operation: The ingredient information of each menu item is compared with the user's allergy information, a safety score is calculated, and the result is saved in the database.

[0741] Step 7:

[0742] User's device

[0743] Users can search for nearby restaurants within the application, using filter options to find the right search criteria.

[0744] Input: Search request (current location and dietary restrictions)

[0745] Output: Search result list

[0746] What happens next: A search request is sent to the server.

[0747] Step 8:

[0748] server

[0749] The server receives the user's search request and extracts restaurant information that meets the search criteria.

[0750] Input: Search request, restaurant information in database

[0751] Output: Recommendation list

[0752] What it does: Extracts relevant restaurant information from a database and generates a prioritized list based on safety scores.

[0753] Step 9:

[0754] User's device

[0755] The user checks the list of recommended restaurants displayed on the device, along with safety information for each restaurant.

[0756] Input: Recommendation list sent from the server

[0757] Output: The displayed list of restaurants

[0758] What happens: The application receives the recommendation list and displays it to the user.

[0759] Step 10:

[0760] User's device

[0761] Users provide feedback about the restaurants they visit.

[0762] Input: User feedback (e.g. text data)

[0763] Output: Feedback data to be sent

[0764] What it does: Collects feedback and sends it to the server.

[0765] Step 11:

[0766] server

[0767] The server analyzes the received feedback, stores it in a database, and uses it for analysis to improve the accuracy of the system.

[0768] Input: User feedback

[0769] Output: Analysis results and updated evaluation data

[0770] Specific operation: The feedback content is classified using text analysis, and the feedback information is reflected in future safety assessments and recommendation algorithms.

[0771] (Application example 1)

[0772] 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."

[0773] Conventional systems only recommend restaurants that can provide safe meals based on a user's dietary restrictions and allergies, but lack systems that support delivery services. This limits the options available to users for obtaining safe meals at home or at work. Furthermore, the lack of an integrated method for efficiently recommending delivery services that meet a user's dietary restrictions makes it difficult to improve the user experience.

[0774] 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.

[0775] In this invention, the server includes a means for inputting and saving a user's dietary restriction and allergy information, a means for collecting and storing menu information from restaurants and delivery services in a database, a means for evaluating the safety of menu items based on ingredients and allergy information, a means for recommending restaurants or delivery services with a high safety rating in the area in response to a user's search request, and a means for collecting and analyzing user feedback. This allows users to easily select safe meals and enjoy meals at any location, such as their home or workplace. Furthermore, by utilizing user feedback to improve the accuracy of recommendations in future periods, the system can be continuously improved.

[0776] "Means for inputting and saving information about a user's dietary restrictions and allergies" is a function that allows a user to input information about their own dietary restrictions and allergies and save that information within the system.

[0777] The "means for collecting restaurant menu information and storing it in a database" is a function for collecting menu information from external restaurants and storing it in a database.

[0778] The "means for evaluating the safety of menu items based on ingredient and allergy information" is a function that compares the collected ingredient information of menu items with the user's allergy information to evaluate how safe each menu item is for the user.

[0779] "Means for recommending safe restaurants in the area in response to a user's search request" is a function that receives a search request from a user and recommends safe restaurants in the area based on that information.

[0780] The "means for collecting and analyzing user feedback" is a function that collects feedback information provided by users and analyzes it to help improve the system.

[0781] The "means for recommending delivery services" is a function that recommends delivery services that can provide safe meals based on the user's dietary restrictions and allergy information.

[0782] "Use the user's location information to search for restaurants or delivery services near the current location" is a function that uses the user's current location information to search for safe restaurants and delivery services in the vicinity.

[0783] "Collecting menu information from restaurants and delivery services using APIs or scraping technology" refers to a method of collecting menu information from restaurants and delivery services externally using APIs or scraping technology.

[0784] This invention relates to a system that recommends restaurants and delivery services that can provide safe meals based on a user's dietary restrictions and allergy information. The system operates using a server, user terminals, databases, APIs, scraping technology, etc.

[0785] The server does the following:

[0786] 1. Enter and save your user information:

[0787] Users enter their dietary restrictions and allergy information using a device such as a smartphone. This information, along with the user's location information, is sent to a server, encrypted, and then stored in a database.

[0788] 2. Collection of restaurant and delivery service menu information:

[0789] The server uses external APIs and scraping technology to collect menu information from restaurants and delivery services, and stores the collected information in a database.

[0790] 3. Menu Safety Rating:

[0791] The server compares the collected information on ingredients in the menu with the user's allergy information and executes an algorithm to evaluate the safety of the food. The results of this evaluation are stored in a database.

[0792] 4. Providing Recommendations:

[0793] When a user searches for restaurants or delivery services near their current location, the server prioritizes and recommends safe options that meet the user's dietary restrictions. This information is displayed on the user's device.

[0794] 5. Collecting and Analyzing User Feedback:

[0795] Users provide feedback about the restaurants they visit and the delivery services they use, which is sent to the server and analyzed to help improve the system's recommendations.

[0796] Examples:

[0797] When a user with a nut allergy searches for gluten-free meals near their current location, the server compares the collected menu information with the user's allergy information and recommends restaurants and delivery services that offer nut-free and gluten-free menus, allowing the user to choose a safe meal.

[0798] Example prompt sentence:

[0799] When a user searches for "nut allergy" and "gluten-free," the server provides a list of recommended restaurants and delivery services. For example, it might recommend "restaurants that serve gluten-free grilled chicken that doesn't contain nuts."

[0800] The specific hardware and software used includes a smartphone, a server, an SQLite database, Flask (a web server framework), and scraping technology, as well as using APIs to obtain external data.

[0801] By using generative AI models and prompt sentences, it is possible to further improve the analysis of user feedback and the recommendation algorithm, thus realizing a system that increases the reliability and convenience of providing users with safe and comfortable meals.

[0802] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0803] Step 1:

[0804] The user enters their dietary restrictions and allergy information and sends it from their device to the server. The user uses a smartphone app to enter information such as nut allergies and gluten-free eating, and turns on location services. This information is encrypted and sent to the server. The input data is dietary restrictions, allergy information, and location information, and the output is encrypted user profile data.

[0805] Step 2:

[0806] The server stores the received user information in a database. The server decrypts the received encrypted data and stores the user's dietary restrictions, allergy information, and location information in a database. The input data is the encrypted user profile data, and the output is the user profile stored in the database.

[0807] Step 3:

[0808] The server uses API or scraping technology to collect menu information from restaurants and delivery services. It obtains menu information in real time using APIs provided by external services or collects menu information through web scraping. The input data is the API response or scraping results, and the output is the collected menu information.

[0809] Step 4:

[0810] The server stores the collected menu information in a database. The server automatically stores the acquired menu information in a database for subsequent processing. The input data is the collected menu information, and the output is the menu information stored in the database.

[0811] Step 5:

[0812] The server evaluates the safety of the menu items. It matches the collected menu information with the user's allergy information and runs a safety evaluation algorithm. For example, a menu item containing nuts will be evaluated as less safe for a user with a nut allergy. The input data are the user profile and menu information, and the output is a safety evaluation score for each menu item.

[0813] Step 6:

[0814] The server recommends safe restaurants and delivery services in the area in response to a user's search request. When a user searches for safe meals near their current location, the server generates a recommendation list based on the safety scores of the corresponding menu items from the database. The input data is the user's search request and current location information, and the output is a recommended list of safe restaurants and delivery services.

[0815] Step 7:

[0816] The user selects a restaurant or delivery service from the recommended list and visits or orders. The user selects a restaurant or delivery service that provides safe meals from the provided recommended list and uses it. The input data is the recommended list, and the output is information about the selected restaurant or delivery service.

[0817] Step 8:

[0818] Users provide feedback and send it to the server. Feedback about the safety of food and the quality of service is input from the terminal and sent to the server. This feedback is saved as learning data for the system and used to improve the accuracy of recommendations in the future. The input data is the user's feedback, and the output is the feedback information saved in the database.

[0819] In this way, the present invention realizes a system that efficiently recommends restaurants and delivery services that offer safe meals based on the user's dietary restrictions and allergy information.

[0820] 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.

[0821] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine for recognizing the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface available on the user's device.

[0822] Explanation of program processing (in natural language)

[0823] 1. Enter and save user information

[0824] User's device

[0825] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[0826] The user may also turn on location services to provide current location information.

[0827] server

[0828] The information sent by the user is encrypted before being sent to the server.

[0829] The server uses this information to create an individual profile for the user and stores it in a database.

[0830] 2. Collection and analysis of restaurant information

[0831] server

[0832] Use APIs from food applications and websites to collect restaurant menu information in real time, or use scraping techniques to extract the required data if APIs are not available.

[0833] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[0834] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[0835] 3. Restaurant Safety Rating

[0836] server

[0837] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[0838] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[0839] 4. Providing Recommendations

[0840] User's device

[0841] Within the application, users can search for restaurants near their current location.

[0842] User's device

[0843] Emotional data is collected using the built-in camera and microphone to understand the user's current emotional state.

[0844] server

[0845] It receives a user's search request and emotion data and extracts relevant restaurants from the database.

[0846] The system generates a list of appropriate restaurants based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants.

[0847] The extracted restaurant list is optimized taking into account the safety score and sent to the user.

[0848] User's device

[0849] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[0850] 5. Using User Feedback

[0851] User's device

[0852] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[0853] server

[0854] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[0855] Specific examples

[0856] 1. Parsing menu information by the server

[0857] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[0858] 2. User Recommendations

[0859] If User B has a nut allergy, wants a gluten-free diet, and has recently been stressed, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe and has a quiet, relaxing environment.

[0860] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying dining out with peace of mind. In addition, by utilizing the user's emotional information, more personalized recommendations become possible.

[0861] The processing flow will be explained below.

[0862] Step 1:

[0863] User's device

[0864] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[0865] Step 2:

[0866] User's device

[0867] The user enables location services and allows the use of current location information.

[0868] This information is encrypted and sent to the server.

[0869] Step 3:

[0870] server

[0871] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[0872] Step 4:

[0873] server

[0874] Use APIs to collect restaurant menu information from food applications and websites in real time, or use scraping techniques to extract the required data if APIs are not available.

[0875] Step 5:

[0876] server

[0877] The collected menu information is stored in a database along with detailed dish names, ingredients, cooking methods, etc.

[0878] Step 6:

[0879] server

[0880] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[0881] Step 7:

[0882] server

[0883] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[0884] Step 8:

[0885] User's device

[0886] Within the application, users can search for restaurants near their current location.

[0887] Step 9:

[0888] User's device

[0889] A search request is sent to the server.

[0890] Step 10:

[0891] server

[0892] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[0893] Step 11:

[0894] server

[0895] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed.

[0896] Step 12:

[0897] server

[0898] To take into account the user's emotional state, the emotion engine analyzes emotional data obtained from the user's device, including data collected through the built-in camera and microphone.

[0899] Step 13:

[0900] server

[0901] Optimize the recommendation list based on the user's emotional state: for example, prioritize quiet, relaxing restaurants if the user is feeling stressed.

[0902] Step 14:

[0903] server

[0904] A final recommendation list is generated and sent to the user's terminal.

[0905] Step 15:

[0906] User's device

[0907] The recommendation list is displayed on the user's device, where they can see detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[0908] Step 16:

[0909] User's device

[0910] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[0911] Step 17:

[0912] server

[0913] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[0914] Example 2

[0915] 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."

[0916] To enjoy eating out with peace of mind, users need to find safe restaurants based on dietary restrictions and allergies. However, this process is cumbersome, and it can be difficult to obtain appropriate information. Furthermore, because the user's emotional state also influences the selection of an appropriate restaurant, recommendations that take into account the user's current emotional state, rather than just ingredient information, are needed. Furthermore, it is a challenge to continuously improve recommendation accuracy by effectively utilizing user feedback.

[0917] 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.

[0918] In this invention, the server includes means for inputting and saving a user's dietary restriction and allergy information, means for collecting restaurant menu information and storing it in a database, means for evaluating the safety of menu items based on ingredient and allergy information, means for recommending safe restaurants in the area in response to a user's search request, means for collecting user emotional information to improve recommendation accuracy, and means for collecting and analyzing user feedback. This allows users to not only find safe restaurants that meet their dietary restrictions and allergies, but also receive recommendations that suit their emotional state at the time, allowing them to enjoy eating out with peace of mind. Furthermore, collecting and analyzing feedback information enables continuous improvement in recommendation accuracy.

[0919] "User's dietary restrictions and allergy information" refers to information about a user's inability to or restrictions on the intake of certain ingredients, such as nut allergies, gluten-free diets, or vegan diets.

[0920] "Storage means" refers to devices or software that record the entered information in a safe and appropriate location and make it available for retrieval when necessary.

[0921] "Restaurant menu information" refers to information about the dishes served at each restaurant, their ingredients, cooking methods, etc.

[0922] "Means of collection" refers to devices or software that obtain necessary information from outside using APIs, scraping technology, etc.

[0923] A "database" refers to a data repository that stores collected information systematically and allows it to be searched and used as needed.

[0924] "Methods for evaluating the safety of menu items based on ingredient and allergy information" refers to the process or algorithm that analyzes the ingredients contained in each menu item and compares them with the user's allergy information to quantify the safety of the menu item.

[0925] "User search request" means an input or instruction a User makes using a Device to search for specific information.

[0926] The "relevant area" refers to the location information of the device currently being operated by the user or the area specified by the user.

[0927] A "safe restaurant" refers to a restaurant that has been rated as safe in light of the user's dietary restrictions and allergy information.

[0928] "Recommendation means" refers to the system's functions and algorithms that extract restaurants that meet the criteria from the database and present them to the user.

[0929] "User's emotional information" is data that represents the user's current psychological state and mood, and refers to information acquired from facial expressions, tone of voice, speech content, and the like.

[0930] "Means to improve recommendation accuracy" refers to algorithms and system functions that incorporate users' emotional information to suggest more appropriate restaurants.

[0931] "Means for collecting and analyzing feedback" refers to the process or system for storing user-provided opinions and ratings in a database and analyzing that data.

[0932] This invention is a system that recommends restaurants that can provide safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine that recognizes the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface that can be used on the user's device.

[0933] Entering and saving user information

[0934] User's device

[0935] Users launch the smartphone application and enter their dietary restrictions and allergies on the initial setup screen. For example, they can enter information such as nut allergies, gluten-free diets, or vegan diets. Next, they authorize the use of location services, which then uses the GPS function to collect their current location.

[0936] server

[0937] The server receives the dietary restriction information and current location information sent by the user. The received data is encrypted and stored securely. A user profile is created in the database and the entered information is stored.

[0938] Collection and analysis of restaurant information

[0939] server

[0940] The server collects restaurant menu information in real time from external food applications and websites using APIs. If APIs are not available, it uses web scraping technology to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to parse the ingredient list for each menu item and match it with the user's allergy information.

[0941] Specific working example:

[0942] The server confirms that there is a dish called "grilled chicken" on the menu and that it is nut- and gluten-free.

[0943] Restaurant Safety Rating

[0944] server

[0945] The server compares the user's profile information stored in the database with the restaurant's menu information, checking whether the ingredients in each menu item match the user's allergy information. It then scores the safety of the menu items, rating, for example, "dishes containing nuts" as low safety for users with nut allergies. The safety score for the entire restaurant is then tallied and stored in the database.

[0946] Specific working example:

[0947] The server rates the safety of "grilled chicken" at 95 out of 100, and takes into account the restaurant's overall safety rating even if other high-risk menu items are present.

[0948] Providing recommendation information

[0949] User's device

[0950] The user opens the application and searches for nearby restaurants. The application collects the user's emotional data (e.g., facial expressions, tone of voice) using the device's built-in camera and microphone.

[0951] server

[0952] The server receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize quiet and relaxing restaurants. Finally, it sends the user an optimized restaurant list that also takes safety scores into account.

[0953] User's device

[0954] A list of recommended restaurants will be displayed on the user's device, where the user can view detailed menu information, safety ratings, and special recommendation reasons based on sentiment.

[0955] Specific working example:

[0956] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[0957] Using User Feedback

[0958] User's device

[0959] Users enter feedback about the restaurants they visit in the application, including, for example, food safety, quality of service, and reactions to specific ingredients.

[0960] server

[0961] The server stores the received feedback in a database and analyzes the aggregated feedback data to use as training data for the system, thereby improving the accuracy of future safety assessments and restaurant recommendations.

[0962] Specific working example:

[0963] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[0964] Prompt Sentence Examples

[0965] Below are some example prompts to input to a generative AI model:

[0966] "Please suggest restaurants to a user who has a nut allergy and wants gluten-free food, and has been feeling stressed lately. The user's current location is around Tokyo Station."

[0967] Based on this prompt, the system can recommend an appropriate restaurant.

[0968] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0969] Step 1: Enter and save user information

[0970] User's device

[0971] 1. The user launches the application on their smartphone.

[0972] 2. On the initial setup screen, enter your dietary restrictions (e.g., nut allergies, gluten-free, vegan, etc.) and allergy information. This will be your input data.

[0973] 3. Next, allow location services to collect your location using the GPS function.

[0974] 4. This information is sent to the server, and the output is encrypted user information and current location information.

[0975] server

[0976] 5. The server receives the dietary restriction information and current location information sent by the user. The input is the encrypted user information and current location information.

[0977] 6. Decrypt and securely store the received data.

[0978] 7. Create a user profile in the database and store the entered information. The output is a user profile stored in the database.

[0979] Step 2: Collect and analyze restaurant information

[0980] server

[0981] 1. The server collects restaurant menu information from external food applications and websites using APIs. If APIs are not available, scraping techniques are used. The input is the menu information collected via APIs.

[0982] 2. Automatically store the collected menu information in a database. The output is restaurant menu information stored in a database.

[0983] 3. The server uses an analysis algorithm to analyze the ingredient list for each menu item and compares the entered menu information with the user's allergy information. The input is the user's allergy information stored in the database and the restaurant's menu information.

[0984] 4. The safety of the menu is evaluated by comparing ingredients with allergy information and stored in a database. The output is the safety of the evaluated menu.

[0985] Specific operation example

[0986] The server confirms that the dish "grilled chicken" is nut- and gluten-free and rates it "highly safe."

[0987] Step 3: Restaurant Safety Assessment

[0988] server

[0989] 1. Compare the user profile information stored in the database with restaurant menu information. The input data is the user profile information and restaurant menu information.

[0990] 2. Check whether the ingredients in each menu match the user's allergy information.

[0991] 3. Score the safety of the menu items, for example, "dishes containing nuts" are rated as less safe for users with nut allergies. The output is a safety score for each menu item.

[0992] 4. The safety score of the entire restaurant is calculated and stored in a database. The output is the overall safety score of the restaurant.

[0993] Specific operation example

[0994] The server rates the safety of the "grilled chicken" at 95 out of 100, which determines the restaurant's overall safety rating.

[0995] Step 4: Provide a recommendation

[0996] User's device

[0997] 1. A user opens the application and searches for nearby restaurants. The input is the user's search request.

[0998] 2. Collect user emotion data using the device's built-in camera and microphone. The input is the collected emotion data.

[0999] 3. This information is sent to the server, and the output is an encrypted search request and sentiment data.

[1000] server

[1001] 4. The server receives the user's search request and emotion data. The input is the encrypted search request and emotion data.

[1002] 5. Extract relevant restaurants from the database. The input is the user's search request.

[1003] 6. Generate an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, prioritize quiet and relaxing restaurants. The output is a restaurant list optimized for the user's emotional state.

[1004] 7. The search results are optimized taking into account the safety score and sent to the user.

[1005] User's device

[1006] 8. The recommended restaurant list is displayed. The user can see detailed menu information, safety ratings, and special recommendation reasons based on sentiment. The output is the displayed restaurant list.

[1007] Specific operation example

[1008] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[1009] Step 5: Use user feedback

[1010] User's device

[1011] 1. A user enters feedback about a restaurant they visited in the application. The input is feedback information.

[1012] 2. Send the feedback to the server. The output is the encrypted feedback information.

[1013] server

[1014] 3. The server stores the received feedback in a database and analyzes the aggregated feedback data. The input is the encrypted feedback information.

[1015] 4. The feedback data is used as training data for the system, which will improve the accuracy of future safety assessments and restaurant recommendations. The output is the analyzed feedback information.

[1016] Specific operation example

[1017] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[1018] (Application example 2)

[1019] 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."

[1020] Conventional food recommendation systems are limited to recommending restaurants based on a user's dietary restrictions and allergies, and do not consider individual emotional states. This makes it difficult to recommend restaurants and menus that suit a user's physical and mental state. To solve this problem, there is a need for more personalized recommendations that utilize the user's emotional information.

[1021] 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 recognizing user emotional information to improve recommendation accuracy, means for recommending restaurants with high safety ratings in the relevant area in response to the user's search request, and means for collecting and analyzing user feedback. This makes it possible to recommend restaurants and menus that are personalized according to the user's emotional state.

[1022] "User's dietary restrictions and allergy information" refers to information that indicates when a user needs to avoid certain ingredients or when a user's food choices are restricted based on their health condition.

[1023] "Menu information" refers to detailed information such as the name, ingredients, cooking method, and price of the dishes served at a restaurant.

[1024] A "database" is a set of data storage mechanisms for efficiently storing, managing, and retrieving collected information.

[1025] "Safety level" is a score that evaluates how low a risk a menu item poses to a user's dietary restrictions or allergy information.

[1026] "Emotional information" is data that indicates the user's psychological state and emotions, and is information collected from the user's facial expressions, tone of voice, and the like.

[1027] "Recommendation accuracy" refers to the system's ability to select restaurants and menus that best suit the user's conditions and emotions.

[1028] A "search request" is a request made by a user to the system for specific information.

[1029] "Feedback" refers to users providing ratings and comments about restaurants and menus they have actually visited.

[1030] This invention is a system that recommends appropriate restaurants and menus based on a user's dietary restrictions, allergy information, and emotional information. The system operates as follows.

[1031] 1. Enter and save user information

[1032] User device:

[1033] Users launch the app and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, or vegan diets. They can also turn on location services to provide their current location.

[1034] server:

[1035] The information sent by the user is encrypted and sent to the server, which uses this information to create an individual profile for the user and stores it in a database.

[1036] 2. Collection and analysis of restaurant information

[1037] server:

[1038] Restaurant menu information is collected in real time from food applications and websites using APIs. If APIs are not available, the necessary data is extracted using scraping technology. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[1039] 3. Restaurant Safety Rating

[1040] server:

[1041] The system compares the user's profile information with the menu information stored in the database and scores the safety of each menu item. For example, a menu item containing nuts is rated as less safe for a user with a nut allergy. The safety score for the entire restaurant is then aggregated and stored in the database. This information is used later in the recommendation process.

[1042] 4. Providing Recommendations

[1043] User device:

[1044] Within the application, users can search for restaurants near their current location, and the app also uses the built-in camera and microphone to collect emotional data to recognize the user's current emotional state.

[1045] server:

[1046] The system receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates a list of appropriate restaurants taking into account the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants. The extracted restaurant list is then optimized, taking into account the safety score, and sent to the user.

[1047] User device:

[1048] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[1049] 5. Using User Feedback

[1050] User device:

[1051] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[1052] server:

[1053] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[1054] Specific examples

[1055] Parsing menu information by the server

[1056] For example, the server collects menu information from a restaurant and analyzes the ingredients in each menu item. If it determines that a menu item called "grilled chicken" does not contain nuts or gluten, it will rate this menu as safe for customers with nut allergies or who are gluten-free.

[1057] User Recommendations

[1058] If a user has a nut allergy, wants to eat gluten-free, and has recently been stressed, they can search for restaurants near their current location and the system will recommend restaurants that offer gluten-free menus, are nut-free, and have a quiet, relaxing environment.

[1059] Prompt Sentence Examples

[1060] "Recommend restaurants that offer safe meals based on dietary restrictions and allergies. If the user is stressed, prioritize quiet and relaxing restaurants. For example, if the user is vegan and has a nut allergy, generate a list of restaurants based on this information. Don't forget to take emotional information into account."

[1061] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1062] Step 1:

[1063] User device:

[1064] The user starts the application and enters their dietary restrictions and allergy information on the initial setup screen. The entered information may include, for example, nut allergies, gluten-free diets, or vegan diets. This information is sent to the server as input data.

[1065] Step 2:

[1066] server:

[1067] The dietary restrictions and allergy information submitted by the user is encrypted and sent to the server. The server receives this information, creates an individual profile for the user, and stores it in a database. The input data is output as saved profile information.

[1068] Step 3:

[1069] server:

[1070] The server uses APIs from food applications and websites to collect restaurant menu information in real time. If APIs are not available, scraping techniques are used to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is stored in a database. The input data is restaurant information, and the output data is the saved menu information.

[1071] Step 4:

[1072] server:

[1073] The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment. The input data is the menu information and the user's profile information, and the output data is a safety score for each menu item. Specifically, the server checks whether the allergens match the ingredient list.

[1074] Step 5:

[1075] User device:

[1076] A user searches for restaurants near their current location within the application. The device obtains the current location information and sends a search request to the server. The input data is the search request, and the output data is a list of search results from the server.

[1077] Step 6:

[1078] User device:

[1079] To understand the user's current emotional state, emotional data is collected using the built-in camera and microphone. The device then transmits the collected emotional data to a server. The input data is emotional state information, and the output data is a notification of completion of transmission to the server.

[1080] Step 7:

[1081] server:

[1082] It receives the user's search request and emotional data and extracts relevant restaurants from the database. It generates a list of suitable restaurants taking into account the user's emotional state. The input data is the search request and emotional state information, and the output data is an optimized recommendation list. Specifically, the emotion engine analyzes the emotional data and matches the restaurant's environmental information with the emotional state.

[1083] Step 8:

[1084] User device:

[1085] The recommendation list is displayed on the user's device, where the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.The input data is the recommendation list sent from the server, and the output data is the displayed restaurant information.

[1086] Step 9:

[1087] User device:

[1088] A user provides feedback about a restaurant they visited. The input data is the feedback information, and the output data is a notification that the feedback has been sent.

[1089] Step 10:

[1090] server:

[1091] The collected feedback is stored in a database and used as training data for the system. The input data is the user's feedback information, and the output data is the updated training data. Specifically, the feedback information is analyzed and used to improve the recommendation algorithm next time.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] [Third embodiment]

[1096] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1097] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1098] 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).

[1099] 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.

[1100] 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.

[1101] 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).

[1102] 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.

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] 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."

[1108] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[1109] Explanation of program processing (in natural language)

[1110] 1. Enter and save user information

[1111] User's device

[1112] Users launch the application and enter their dietary restrictions and allergies on an initial setup screen, including, for example, nut allergies or gluten-free requirements.

[1113] The user may also turn on location services to provide current location information.

[1114] server

[1115] Information sent by the user is encrypted and sent to the server.

[1116] The server uses this information to create an individual profile for the user and stores it in a database.

[1117] 2. Collection and analysis of restaurant information

[1118] server

[1119] The server collects restaurant menu information in real time through the API of food applications and websites, or uses scraping technology to extract the required data if the API is not available.

[1120] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[1121] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[1122] 3. Restaurant Safety Rating

[1123] server

[1124] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[1125] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[1126] 4. Providing Recommendations

[1127] User's device

[1128] Within the application, users can search for restaurants near their current location.

[1129] server

[1130] It receives a user's search request and retrieves relevant restaurant information from a database, including restaurants that cater to specific dietary restrictions or allergies.

[1131] The extracted restaurant list is sorted in descending order of safety score.

[1132] The server generates the final recommendation list and sends it to the user.

[1133] User's device

[1134] Users can view a list of recommended restaurants in the application, including the restaurant name, address, and safety information for each menu item.

[1135] 5. Using User Feedback

[1136] User's device

[1137] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[1138] server

[1139] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[1140] Specific examples

[1141] 1. Parsing menu information by the server

[1142] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[1143] 2. User Recommendations

[1144] If User B has a nut allergy and wants a gluten-free meal, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe.

[1145] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying eating out with peace of mind.

[1146] The processing flow will be explained below.

[1147] Step 1:

[1148] User's device

[1149] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[1150] Step 2:

[1151] User's device

[1152] The user enables location services and allows the use of current location information.

[1153] This information is encrypted and sent to the server.

[1154] Step 3:

[1155] server

[1156] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[1157] Step 4:

[1158] server

[1159] We use APIs from food applications and websites to collect restaurant menu information in real time, and if APIs are not available, we use scraping technology to extract the required data.

[1160] Step 5:

[1161] server

[1162] The collected menu information is stored in a database along with detailed information such as the names of the dishes included, ingredients, and cooking methods.

[1163] Step 6:

[1164] server

[1165] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[1166] Step 7:

[1167] server

[1168] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[1169] Step 8:

[1170] User's device

[1171] Within the application, users can search for restaurants near their current location.

[1172] Step 9:

[1173] User's device

[1174] A search request is sent to the server.

[1175] Step 10:

[1176] server

[1177] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[1178] Step 11:

[1179] server

[1180] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed to generate a recommendation list.

[1181] Step 12:

[1182] server

[1183] The recommendation list is sent to the user's terminal.

[1184] Step 13:

[1185] User's device

[1186] The recommendation list is displayed on the user's device, where the user can view detailed menu information and safety level breakdowns.

[1187] Step 14:

[1188] User's device

[1189] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[1190] Step 15:

[1191] server

[1192] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[1193] Example 1

[1194] 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."

[1195] Conventional restaurant recommendation systems have difficulty finding suitable restaurants because they are unable to fully consider users' dietary restrictions and allergy information. Furthermore, due to insufficient safety assessment, users with allergies are unable to enjoy eating out with peace of mind. Furthermore, the lack of a mechanism for effectively utilizing user feedback makes it difficult to improve the accuracy of the system.

[1196] 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.

[1197] In this invention, the server includes means for inputting and saving a user's dietary restrictions and allergy information, means for collecting restaurant menu information and storing it in a database, means for analyzing the collected menu information and evaluating the safety of the menu based on the ingredients and allergy information, means for recommending restaurants that meet the search request based on the user's profile information and current location, and means for collecting and analyzing user feedback to improve the accuracy of the system, thereby enabling efficient recommendation of safe restaurants that meet the user's individual dietary restrictions and allergy information.

[1198] "User dietary restrictions" refer to requirements that individual users must meet to avoid certain dishes or ingredients, such as allergies or restrictions on certain ingredients, dietary restrictions based on religion or belief, or intake restrictions based on health conditions or doctor's instructions.

[1199] "Allergy information" is information indicating whether the user has an allergy to a specific food ingredient or seasoning. Specifically, it includes allergies to nuts, gluten, dairy products, shellfish, etc.

[1200] "Menu information" refers to data such as the names, ingredients, cooking methods, prices, and nutritional information of the dishes and drinks served by the restaurant.

[1201] A "database" is a system that stores and manages various types of information in an organized manner, and enables searching and updating. Specific examples include SQL databases and NoSQL databases.

[1202] A "safety rating" is a numerical or ranking indicator of how safe a menu item is for a particular user, based on the ingredients and allergy information contained in the menu item.

[1203] A "search request" is a request for information search made by a user to the system, and appropriate data is extracted based on this request.

[1204] "Recommendation" means that the system provides appropriate options based on the user's requirements and conditions.

[1205] "Feedback" refers to opinions and evaluations provided by users regarding the quality and satisfaction of the services or products they have used.

[1206] "Analysis" is the act of analyzing data in detail to extract information and discover its meaning and trends.

[1207] "Profile information" is a collection of information about an individual user, such as the user's personal information, dietary restrictions and allergy information, and past usage history.

[1208] This invention is a system that recommends restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[1209] Entering and saving user information

[1210] User's device

[1211] A user launches an application and authenticates at the login screen using an email address and password or a social login (e.g., Google or social media). After authentication, the user enters dietary restrictions and allergy information on the initial setup screen. The information is entered as input fields, with specific restrictions such as nut allergies or gluten-free options selected using checkboxes or input boxes. Turning on location services allows the user to provide their current location to the application.

[1212] server

[1213] Data sent by users is encrypted using SSL / TLS and sent to the server. The server receives the encrypted data and decrypts it using an algorithm such as AES before storing it in the database. Based on the decrypted information, a unique user ID is generated and the user's profile is saved in the database. The saved profile information is later used for restaurant recommendations.

[1214] Collection and analysis of restaurant information

[1215] server

[1216] Restaurant information is collected using APIs (e.g., public APIs) from food applications and food review sites. If APIs are not available, the necessary data is obtained using scraping techniques using Python's BeautifulSoup or Selenium. The collected data is stored in a temporary database and cleaned, including filling in missing data and removing duplicate data. After the data is cleaned, the menu information is analyzed and ingredients and cooking methods are extracted using NLP (natural language processing) techniques.

[1217] Restaurant Safety Rating

[1218] server

[1219] Based on the user's profile information, the analyzed menu information is compared and a safety score is assigned. For example, if a user profile has a "nut allergy" and there is a menu item that "contains nuts," the safety score for that menu item will be set low. The safety scores for all menu items are tallied to calculate a safety score for the entire restaurant. The score is saved in a database and will be used the next time the restaurant is evaluated.

[1220] Providing recommendation information

[1221] User's device

[1222] The user opens a search screen within the application to search for nearby restaurants. The search criteria include filter options such as "nut-free" and "gluten-free." When the user presses the search button, a search request is sent to the server.

[1223] server

[1224] The server receives the request, searches the database based on the user's current location and profile criteria, extracts restaurant information that matches the criteria, sorts them by safety score, and generates a final recommendation list that is sent back to the user's device in JSON format.

[1225] User's device

[1226] The user can then review the list of recommendations they receive, which includes the restaurant name, address, and safety information for the menu. For some restaurants, the user can also view ratings and reviews.

[1227] Using User Feedback

[1228] User's device

[1229] Users provide feedback about the restaurant they visited. Feedback is in text input format and includes items such as "food safety" and "quality of service." When they press the "send feedback" button, the feedback is sent to the server.

[1230] server

[1231] The collected feedback is stored in a database and categorised using text analysis, which will be used to improve the accuracy of future safety assessments and restaurant recommendation algorithms.

[1232] Specific examples

[1233] Parsing menu information by the server

[1234] For example, if the server collects menu information from a restaurant and determines that the menu item "grilled chicken" does not contain nuts or gluten, the menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[1235] User Recommendations

[1236] If a user has a nut allergy and wants gluten-free food, when he searches for restaurants near his current location, the server will prioritize recommendations of restaurants that are nut-free and offer gluten-free menu items. For example, a restaurant's "grilled chicken" is included in the recommendation list because it has a high safety rating.

[1237] Prompt Sentence Examples

[1238] "If I have a nut allergy and gluten-free diet, can you recommend some restaurants near my location?"

[1239] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1240] Step 1:

[1241] User's device

[1242] A user launches an application and authenticates at a login screen using an email address and password or a social login (e.g., Google or social media).

[1243] Enter your email address, password, or social login details

[1244] Output: Authentication token

[1245] What happens: An authentication token is generated and the user is authenticated.

[1246] Step 2:

[1247] User's device

[1248] After authentication, the user enters information about dietary restrictions and allergies on the initial setup screen.

[1249] Input: Dietary restrictions (e.g., nut allergies, gluten-free), location information

[1250] Output: User's dietary restrictions and location

[1251] Specific operation: Information entered on the device is checked for integrity and then sent to the server.

[1252] Step 3:

[1253] server

[1254] Data sent by the user is encrypted using SSL / TLS and sent to the server.

[1255] Input: Encrypted user data

[1256] Output: Decrypted user data

[1257] Specific operation: The server decrypts the received data using the AES algorithm and stores it in the database along with the user ID.

[1258] Step 4:

[1259] server

[1260] The server periodically collects restaurant menu information using APIs of food applications and food review sites, or uses scraping techniques if APIs are not available.

[1261] Input: Restaurant information obtained from API or data collected by scraping

[1262] Output: Cleaned restaurant menu information

[1263] What happens: The server cleans the data, filling in missing data and removing duplicates.

[1264] Step 5:

[1265] server

[1266] The cleaned data is analyzed and each restaurant's menu information is classified into ingredients and cooking methods using NLP technology.

[1267] Input: Cleaned menu information

[1268] Output: Categorized ingredients and cooking methods

[1269] Specific operation: Runs NLP algorithms to extract ingredients and cooking methods from menu information and saves them in a database.

[1270] Step 6:

[1271] server

[1272] A safety score is calculated based on the user's profile information and analyzed menu information.

[1273] Input: User profile information, menu information

[1274] Output: Safety score

[1275] Specific operation: The ingredient information of each menu item is compared with the user's allergy information, a safety score is calculated, and the result is saved in the database.

[1276] Step 7:

[1277] User's device

[1278] Users can search for nearby restaurants within the application, using filter options to find the right search criteria.

[1279] Input: Search request (current location and dietary restrictions)

[1280] Output: Search result list

[1281] What happens next: A search request is sent to the server.

[1282] Step 8:

[1283] server

[1284] The server receives the user's search request and extracts restaurant information that meets the search criteria.

[1285] Input: Search request, restaurant information in database

[1286] Output: Recommendation list

[1287] What it does: Extracts relevant restaurant information from a database and generates a prioritized list based on safety scores.

[1288] Step 9:

[1289] User's device

[1290] The user checks the list of recommended restaurants displayed on the device, along with safety information for each restaurant.

[1291] Input: Recommendation list sent from the server

[1292] Output: The displayed list of restaurants

[1293] What happens: The application receives the recommendation list and displays it to the user.

[1294] Step 10:

[1295] User's device

[1296] Users provide feedback about the restaurants they visit.

[1297] Input: User feedback (e.g. text data)

[1298] Output: Feedback data to be sent

[1299] What it does: Collects feedback and sends it to the server.

[1300] Step 11:

[1301] server

[1302] The server analyzes the received feedback, stores it in a database, and uses it for analysis to improve the accuracy of the system.

[1303] Input: User feedback

[1304] Output: Analysis results and updated evaluation data

[1305] Specific operation: The feedback content is classified using text analysis, and the feedback information is reflected in future safety assessments and recommendation algorithms.

[1306] (Application example 1)

[1307] 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."

[1308] Conventional systems only recommend restaurants that can provide safe meals based on a user's dietary restrictions and allergies, but lack systems that support delivery services. This limits the options available to users for obtaining safe meals at home or at work. Furthermore, the lack of an integrated method for efficiently recommending delivery services that meet a user's dietary restrictions makes it difficult to improve the user experience.

[1309] 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.

[1310] In this invention, the server includes a means for inputting and saving a user's dietary restriction and allergy information, a means for collecting and storing menu information from restaurants and delivery services in a database, a means for evaluating the safety of menu items based on ingredients and allergy information, a means for recommending restaurants or delivery services with a high safety rating in the area in response to a user's search request, and a means for collecting and analyzing user feedback. This allows users to easily select safe meals and enjoy meals at any location, such as their home or workplace. Furthermore, by utilizing user feedback to improve the accuracy of recommendations in future periods, the system can be continuously improved.

[1311] "Means for inputting and saving information about a user's dietary restrictions and allergies" is a function that allows a user to input information about their own dietary restrictions and allergies and save that information within the system.

[1312] The "means for collecting restaurant menu information and storing it in a database" is a function for collecting menu information from external restaurants and storing it in a database.

[1313] The "means for evaluating the safety of menu items based on ingredient and allergy information" is a function that compares the collected ingredient information of menu items with the user's allergy information to evaluate how safe each menu item is for the user.

[1314] "Means for recommending safe restaurants in the area in response to a user's search request" is a function that receives a search request from a user and recommends safe restaurants in the area based on that information.

[1315] The "means for collecting and analyzing user feedback" is a function that collects feedback information provided by users and analyzes it to help improve the system.

[1316] The "means for recommending delivery services" is a function that recommends delivery services that can provide safe meals based on the user's dietary restrictions and allergy information.

[1317] "Use the user's location information to search for restaurants or delivery services near the current location" is a function that uses the user's current location information to search for safe restaurants and delivery services in the vicinity.

[1318] "Collecting menu information from restaurants and delivery services using APIs or scraping technology" refers to a method of collecting menu information from restaurants and delivery services externally using APIs or scraping technology.

[1319] This invention relates to a system that recommends restaurants and delivery services that can provide safe meals based on a user's dietary restrictions and allergy information. The system operates using a server, user terminals, databases, APIs, scraping technology, etc.

[1320] The server does the following:

[1321] 1. Enter and save your user information:

[1322] Users enter their dietary restrictions and allergy information using a device such as a smartphone. This information, along with the user's location information, is sent to a server, encrypted, and then stored in a database.

[1323] 2. Collection of restaurant and delivery service menu information:

[1324] The server uses external APIs and scraping technology to collect menu information from restaurants and delivery services, and stores the collected information in a database.

[1325] 3. Menu Safety Rating:

[1326] The server compares the collected information on ingredients in the menu with the user's allergy information and executes an algorithm to evaluate the safety of the food. The results of this evaluation are stored in a database.

[1327] 4. Providing Recommendations:

[1328] When a user searches for restaurants or delivery services near their current location, the server prioritizes and recommends safe options that meet the user's dietary restrictions. This information is displayed on the user's device.

[1329] 5. Collecting and Analyzing User Feedback:

[1330] Users provide feedback about the restaurants they visit and the delivery services they use, which is sent to the server and analyzed to help improve the system's recommendations.

[1331] Examples:

[1332] When a user with a nut allergy searches for gluten-free meals near their current location, the server compares the collected menu information with the user's allergy information and recommends restaurants and delivery services that offer nut-free and gluten-free menus, allowing the user to choose a safe meal.

[1333] Example prompt sentence:

[1334] When a user searches for "nut allergy" and "gluten-free," the server provides a list of recommended restaurants and delivery services. For example, it might recommend "restaurants that serve gluten-free grilled chicken that doesn't contain nuts."

[1335] The specific hardware and software used includes a smartphone, a server, an SQLite database, Flask (a web server framework), and scraping technology, as well as using APIs to obtain external data.

[1336] By using generative AI models and prompt sentences, it is possible to further improve the analysis of user feedback and the recommendation algorithm, thus realizing a system that increases the reliability and convenience of providing users with safe and comfortable meals.

[1337] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1338] Step 1:

[1339] The user enters their dietary restrictions and allergy information and sends it from their device to the server. The user uses a smartphone app to enter information such as nut allergies and gluten-free eating, and turns on location services. This information is encrypted and sent to the server. The input data is dietary restrictions, allergy information, and location information, and the output is encrypted user profile data.

[1340] Step 2:

[1341] The server stores the received user information in a database. The server decrypts the received encrypted data and stores the user's dietary restrictions, allergy information, and location information in a database. The input data is the encrypted user profile data, and the output is the user profile stored in the database.

[1342] Step 3:

[1343] The server uses API or scraping technology to collect menu information from restaurants and delivery services. It obtains menu information in real time using APIs provided by external services or collects menu information through web scraping. The input data is the API response or scraping results, and the output is the collected menu information.

[1344] Step 4:

[1345] The server stores the collected menu information in a database. The server automatically stores the acquired menu information in a database for subsequent processing. The input data is the collected menu information, and the output is the menu information stored in the database.

[1346] Step 5:

[1347] The server evaluates the safety of the menu items. It matches the collected menu information with the user's allergy information and runs a safety evaluation algorithm. For example, a menu item containing nuts will be evaluated as less safe for a user with a nut allergy. The input data are the user profile and menu information, and the output is a safety evaluation score for each menu item.

[1348] Step 6:

[1349] The server recommends safe restaurants and delivery services in the area in response to a user's search request. When a user searches for safe meals near their current location, the server generates a recommendation list based on the safety scores of the corresponding menu items from the database. The input data is the user's search request and current location information, and the output is a recommended list of safe restaurants and delivery services.

[1350] Step 7:

[1351] The user selects a restaurant or delivery service from the recommended list and visits or orders. The user selects a restaurant or delivery service that provides safe meals from the provided recommended list and uses it. The input data is the recommended list, and the output is information about the selected restaurant or delivery service.

[1352] Step 8:

[1353] Users provide feedback and send it to the server. Feedback about the safety of food and the quality of service is input from the terminal and sent to the server. This feedback is saved as learning data for the system and used to improve the accuracy of recommendations in the future. The input data is the user's feedback, and the output is the feedback information saved in the database.

[1354] In this way, the present invention realizes a system that efficiently recommends restaurants and delivery services that offer safe meals based on the user's dietary restrictions and allergy information.

[1355] 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.

[1356] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine for recognizing the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface available on the user's device.

[1357] Explanation of program processing (in natural language)

[1358] 1. Enter and save user information

[1359] User's device

[1360] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[1361] The user may also turn on location services to provide current location information.

[1362] server

[1363] The information sent by the user is encrypted before being sent to the server.

[1364] The server uses this information to create an individual profile for the user and stores it in a database.

[1365] 2. Collection and analysis of restaurant information

[1366] server

[1367] Use APIs from food applications and websites to collect restaurant menu information in real time, or use scraping techniques to extract the required data if APIs are not available.

[1368] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[1369] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[1370] 3. Restaurant Safety Rating

[1371] server

[1372] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[1373] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[1374] 4. Providing Recommendations

[1375] User's device

[1376] Within the application, users can search for restaurants near their current location.

[1377] User's device

[1378] Emotional data is collected using the built-in camera and microphone to understand the user's current emotional state.

[1379] server

[1380] It receives a user's search request and emotion data and extracts relevant restaurants from the database.

[1381] The system generates a list of appropriate restaurants based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants.

[1382] The extracted restaurant list is optimized taking into account the safety score and sent to the user.

[1383] User's device

[1384] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[1385] 5. Using User Feedback

[1386] User's device

[1387] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[1388] server

[1389] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[1390] Specific examples

[1391] 1. Parsing menu information by the server

[1392] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[1393] 2. User Recommendations

[1394] If User B has a nut allergy, wants a gluten-free diet, and has recently been stressed, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe and has a quiet, relaxing environment.

[1395] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying dining out with peace of mind. In addition, by utilizing the user's emotional information, more personalized recommendations become possible.

[1396] The processing flow will be explained below.

[1397] Step 1:

[1398] User's device

[1399] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[1400] Step 2:

[1401] User's device

[1402] The user enables location services and allows the use of current location information.

[1403] This information is encrypted and sent to the server.

[1404] Step 3:

[1405] server

[1406] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[1407] Step 4:

[1408] server

[1409] Use APIs to collect restaurant menu information from food applications and websites in real time, or use scraping techniques to extract the required data if APIs are not available.

[1410] Step 5:

[1411] server

[1412] The collected menu information is stored in a database along with detailed dish names, ingredients, cooking methods, etc.

[1413] Step 6:

[1414] server

[1415] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[1416] Step 7:

[1417] server

[1418] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[1419] Step 8:

[1420] User's device

[1421] Within the application, users can search for restaurants near their current location.

[1422] Step 9:

[1423] User's device

[1424] A search request is sent to the server.

[1425] Step 10:

[1426] server

[1427] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[1428] Step 11:

[1429] server

[1430] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed.

[1431] Step 12:

[1432] server

[1433] To take into account the user's emotional state, the emotion engine analyzes emotional data obtained from the user's device, including data collected through the built-in camera and microphone.

[1434] Step 13:

[1435] server

[1436] Optimize the recommendation list based on the user's emotional state: for example, prioritize quiet, relaxing restaurants if the user is feeling stressed.

[1437] Step 14:

[1438] server

[1439] A final recommendation list is generated and sent to the user's terminal.

[1440] Step 15:

[1441] User's device

[1442] The recommendation list is displayed on the user's device, where they can see detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[1443] Step 16:

[1444] User's device

[1445] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[1446] Step 17:

[1447] server

[1448] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[1449] Example 2

[1450] 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."

[1451] To enjoy eating out with peace of mind, users need to find safe restaurants based on dietary restrictions and allergies. However, this process is cumbersome, and it can be difficult to obtain appropriate information. Furthermore, because the user's emotional state also influences the selection of an appropriate restaurant, recommendations that take into account the user's current emotional state, rather than just ingredient information, are needed. Furthermore, it is a challenge to continuously improve recommendation accuracy by effectively utilizing user feedback.

[1452] 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.

[1453] In this invention, the server includes means for inputting and saving a user's dietary restriction and allergy information, means for collecting restaurant menu information and storing it in a database, means for evaluating the safety of menu items based on ingredient and allergy information, means for recommending safe restaurants in the area in response to a user's search request, means for collecting user emotional information to improve recommendation accuracy, and means for collecting and analyzing user feedback. This allows users to not only find safe restaurants that meet their dietary restrictions and allergies, but also receive recommendations that suit their emotional state at the time, allowing them to enjoy eating out with peace of mind. Furthermore, collecting and analyzing feedback information enables continuous improvement in recommendation accuracy.

[1454] "User's dietary restrictions and allergy information" refers to information about a user's inability to or restrictions on the intake of certain ingredients, such as nut allergies, gluten-free diets, or vegan diets.

[1455] "Storage means" refers to devices or software that record the entered information in a safe and appropriate location and make it available for retrieval when necessary.

[1456] "Restaurant menu information" refers to information about the dishes served at each restaurant, their ingredients, cooking methods, etc.

[1457] "Means of collection" refers to devices or software that obtain necessary information from outside using APIs, scraping technology, etc.

[1458] A "database" refers to a data repository that stores collected information systematically and allows it to be searched and used as needed.

[1459] "Methods for evaluating the safety of menu items based on ingredient and allergy information" refers to the process or algorithm that analyzes the ingredients contained in each menu item and compares them with the user's allergy information to quantify the safety of the menu item.

[1460] "User search request" means an input or instruction a User makes using a Device to search for specific information.

[1461] The "relevant area" refers to the location information of the device currently being operated by the user or the area specified by the user.

[1462] A "safe restaurant" refers to a restaurant that has been rated as safe in light of the user's dietary restrictions and allergy information.

[1463] "Recommendation means" refers to the system's functions and algorithms that extract restaurants that meet the criteria from the database and present them to the user.

[1464] "User's emotional information" is data that represents the user's current psychological state and mood, and refers to information acquired from facial expressions, tone of voice, speech content, and the like.

[1465] "Means to improve recommendation accuracy" refers to algorithms and system functions that incorporate users' emotional information to suggest more appropriate restaurants.

[1466] "Means for collecting and analyzing feedback" refers to the process or system for storing user-provided opinions and ratings in a database and analyzing that data.

[1467] This invention is a system that recommends restaurants that can provide safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine that recognizes the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface that can be used on the user's device.

[1468] Entering and saving user information

[1469] User's device

[1470] Users launch the smartphone application and enter their dietary restrictions and allergies on the initial setup screen. For example, they can enter information such as nut allergies, gluten-free diets, or vegan diets. Next, they authorize the use of location services, which then uses the GPS function to collect their current location.

[1471] server

[1472] The server receives the dietary restriction information and current location information sent by the user. The received data is encrypted and stored securely. A user profile is created in the database and the entered information is stored.

[1473] Collection and analysis of restaurant information

[1474] server

[1475] The server collects restaurant menu information in real time from external food applications and websites using APIs. If APIs are not available, it uses web scraping technology to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to parse the ingredient list for each menu item and match it with the user's allergy information.

[1476] Specific working example:

[1477] The server confirms that there is a dish called "grilled chicken" on the menu and that it is nut- and gluten-free.

[1478] Restaurant Safety Rating

[1479] server

[1480] The server compares the user's profile information stored in the database with the restaurant's menu information, checking whether the ingredients in each menu item match the user's allergy information. It then scores the safety of the menu items, rating, for example, "dishes containing nuts" as low safety for users with nut allergies. The safety score for the entire restaurant is then tallied and stored in the database.

[1481] Specific working example:

[1482] The server rates the safety of "grilled chicken" at 95 out of 100, and takes into account the restaurant's overall safety rating even if other high-risk menu items are present.

[1483] Providing recommendation information

[1484] User's device

[1485] The user opens the application and searches for nearby restaurants. The application collects the user's emotional data (e.g., facial expressions, tone of voice) using the device's built-in camera and microphone.

[1486] server

[1487] The server receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize quiet and relaxing restaurants. Finally, it sends the user an optimized restaurant list that also takes safety scores into account.

[1488] User's device

[1489] A list of recommended restaurants will be displayed on the user's device, where the user can view detailed menu information, safety ratings, and special recommendation reasons based on sentiment.

[1490] Specific working example:

[1491] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[1492] Using User Feedback

[1493] User's device

[1494] Users enter feedback about the restaurants they visit in the application, including, for example, food safety, quality of service, and reactions to specific ingredients.

[1495] server

[1496] The server stores the received feedback in a database and analyzes the aggregated feedback data to use as training data for the system, thereby improving the accuracy of future safety assessments and restaurant recommendations.

[1497] Specific working example:

[1498] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[1499] Prompt Sentence Examples

[1500] Below are some example prompts to input to a generative AI model:

[1501] "Please suggest restaurants to a user who has a nut allergy and wants gluten-free food, and has been feeling stressed lately. The user's current location is around Tokyo Station."

[1502] Based on this prompt, the system can recommend an appropriate restaurant.

[1503] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1504] Step 1: Enter and save user information

[1505] User's device

[1506] 1. The user launches the application on their smartphone.

[1507] 2. On the initial setup screen, enter your dietary restrictions (e.g., nut allergies, gluten-free, vegan, etc.) and allergy information. This will be your input data.

[1508] 3. Next, allow location services to collect your location using the GPS function.

[1509] 4. This information is sent to the server, and the output is encrypted user information and current location information.

[1510] server

[1511] 5. The server receives the dietary restriction information and current location information sent by the user. The input is the encrypted user information and current location information.

[1512] 6. Decrypt and securely store the received data.

[1513] 7. Create a user profile in the database and store the entered information. The output is a user profile stored in the database.

[1514] Step 2: Collect and analyze restaurant information

[1515] server

[1516] 1. The server collects restaurant menu information from external food applications and websites using APIs. If APIs are not available, scraping techniques are used. The input is the menu information collected via APIs.

[1517] 2. Automatically store the collected menu information in a database. The output is restaurant menu information stored in a database.

[1518] 3. The server uses an analysis algorithm to analyze the ingredient list for each menu item and compares the entered menu information with the user's allergy information. The input is the user's allergy information stored in the database and the restaurant's menu information.

[1519] 4. The safety of the menu is evaluated by comparing ingredients with allergy information and stored in a database. The output is the safety of the evaluated menu.

[1520] Specific operation example

[1521] The server confirms that the dish "grilled chicken" is nut- and gluten-free and rates it "highly safe."

[1522] Step 3: Restaurant Safety Assessment

[1523] server

[1524] 1. Compare the user profile information stored in the database with restaurant menu information. The input data is the user profile information and restaurant menu information.

[1525] 2. Check whether the ingredients in each menu match the user's allergy information.

[1526] 3. Score the safety of the menu items, for example, "dishes containing nuts" are rated as less safe for users with nut allergies. The output is a safety score for each menu item.

[1527] 4. The safety score of the entire restaurant is calculated and stored in a database. The output is the overall safety score of the restaurant.

[1528] Specific operation example

[1529] The server rates the safety of the "grilled chicken" at 95 out of 100, which determines the restaurant's overall safety rating.

[1530] Step 4: Provide a recommendation

[1531] User's device

[1532] 1. A user opens the application and searches for nearby restaurants. The input is the user's search request.

[1533] 2. Collect user emotion data using the device's built-in camera and microphone. The input is the collected emotion data.

[1534] 3. This information is sent to the server, and the output is an encrypted search request and sentiment data.

[1535] server

[1536] 4. The server receives the user's search request and emotion data. The input is the encrypted search request and emotion data.

[1537] 5. Extract relevant restaurants from the database. The input is the user's search request.

[1538] 6. Generate an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, prioritize quiet and relaxing restaurants. The output is a restaurant list optimized for the user's emotional state.

[1539] 7. The search results are optimized taking into account the safety score and sent to the user.

[1540] User's device

[1541] 8. The recommended restaurant list is displayed. The user can see detailed menu information, safety ratings, and special recommendation reasons based on sentiment. The output is the displayed restaurant list.

[1542] Specific operation example

[1543] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[1544] Step 5: Use user feedback

[1545] User's device

[1546] 1. A user enters feedback about a restaurant they visited in the application. The input is feedback information.

[1547] 2. Send the feedback to the server. The output is the encrypted feedback information.

[1548] server

[1549] 3. The server stores the received feedback in a database and analyzes the aggregated feedback data. The input is the encrypted feedback information.

[1550] 4. The feedback data is used as training data for the system, which will improve the accuracy of future safety assessments and restaurant recommendations. The output is the analyzed feedback information.

[1551] Specific operation example

[1552] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[1553] (Application example 2)

[1554] 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."

[1555] Conventional food recommendation systems are limited to recommending restaurants based on a user's dietary restrictions and allergies, and do not consider individual emotional states. This makes it difficult to recommend restaurants and menus that suit a user's physical and mental state. To solve this problem, there is a need for more personalized recommendations that utilize the user's emotional information.

[1556] 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 recognizing user emotional information to improve recommendation accuracy, means for recommending restaurants with high safety ratings in the relevant area in response to the user's search request, and means for collecting and analyzing user feedback. This makes it possible to recommend restaurants and menus that are personalized according to the user's emotional state.

[1557] "User's dietary restrictions and allergy information" refers to information that indicates when a user needs to avoid certain ingredients or when a user's food choices are restricted based on their health condition.

[1558] "Menu information" refers to detailed information such as the name, ingredients, cooking method, and price of the dishes served at a restaurant.

[1559] A "database" is a set of data storage mechanisms for efficiently storing, managing, and retrieving collected information.

[1560] "Safety level" is a score that evaluates how low a risk a menu item poses to a user's dietary restrictions or allergy information.

[1561] "Emotional information" is data that indicates the user's psychological state and emotions, and is information collected from the user's facial expressions, tone of voice, and the like.

[1562] "Recommendation accuracy" refers to the system's ability to select restaurants and menus that best suit the user's conditions and emotions.

[1563] A "search request" is a request made by a user to the system for specific information.

[1564] "Feedback" refers to users providing ratings and comments about restaurants and menus they have actually visited.

[1565] This invention is a system that recommends appropriate restaurants and menus based on a user's dietary restrictions, allergy information, and emotional information. The system operates as follows.

[1566] 1. Enter and save user information

[1567] User device:

[1568] Users launch the app and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, or vegan diets. They can also turn on location services to provide their current location.

[1569] server:

[1570] The information sent by the user is encrypted and sent to the server, which uses this information to create an individual profile for the user and stores it in a database.

[1571] 2. Collection and analysis of restaurant information

[1572] server:

[1573] Restaurant menu information is collected in real time from food applications and websites using APIs. If APIs are not available, the necessary data is extracted using scraping technology. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[1574] 3. Restaurant Safety Rating

[1575] server:

[1576] The system compares the user's profile information with the menu information stored in the database and scores the safety of each menu item. For example, a menu item containing nuts is rated as less safe for a user with a nut allergy. The safety score for the entire restaurant is then aggregated and stored in the database. This information is used later in the recommendation process.

[1577] 4. Providing Recommendations

[1578] User device:

[1579] Within the application, users can search for restaurants near their current location, and the app also uses the built-in camera and microphone to collect emotional data to recognize the user's current emotional state.

[1580] server:

[1581] The system receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates a list of appropriate restaurants taking into account the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants. The extracted restaurant list is then optimized, taking into account the safety score, and sent to the user.

[1582] User device:

[1583] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[1584] 5. Using User Feedback

[1585] User device:

[1586] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[1587] server:

[1588] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[1589] Specific examples

[1590] Parsing menu information by the server

[1591] For example, the server collects menu information from a restaurant and analyzes the ingredients in each menu item. If it determines that a menu item called "grilled chicken" does not contain nuts or gluten, it will rate this menu as safe for customers with nut allergies or who are gluten-free.

[1592] User Recommendations

[1593] If a user has a nut allergy, wants to eat gluten-free, and has recently been stressed, they can search for restaurants near their current location and the system will recommend restaurants that offer gluten-free menus, are nut-free, and have a quiet, relaxing environment.

[1594] Prompt Sentence Examples

[1595] "Recommend restaurants that offer safe meals based on dietary restrictions and allergies. If the user is stressed, prioritize quiet and relaxing restaurants. For example, if the user is vegan and has a nut allergy, generate a list of restaurants based on this information. Don't forget to take emotional information into account."

[1596] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1597] Step 1:

[1598] User device:

[1599] The user starts the application and enters their dietary restrictions and allergy information on the initial setup screen. The entered information may include, for example, nut allergies, gluten-free diets, or vegan diets. This information is sent to the server as input data.

[1600] Step 2:

[1601] server:

[1602] The dietary restrictions and allergy information submitted by the user is encrypted and sent to the server. The server receives this information, creates an individual profile for the user, and stores it in a database. The input data is output as saved profile information.

[1603] Step 3:

[1604] server:

[1605] The server uses APIs from food applications and websites to collect restaurant menu information in real time. If APIs are not available, scraping techniques are used to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is stored in a database. The input data is restaurant information, and the output data is the saved menu information.

[1606] Step 4:

[1607] server:

[1608] The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment. The input data is the menu information and the user's profile information, and the output data is a safety score for each menu item. Specifically, the server checks whether the allergens match the ingredient list.

[1609] Step 5:

[1610] User device:

[1611] A user searches for restaurants near their current location within the application. The device obtains the current location information and sends a search request to the server. The input data is the search request, and the output data is a list of search results from the server.

[1612] Step 6:

[1613] User device:

[1614] To understand the user's current emotional state, emotional data is collected using the built-in camera and microphone. The device then transmits the collected emotional data to a server. The input data is emotional state information, and the output data is a notification of completion of transmission to the server.

[1615] Step 7:

[1616] server:

[1617] It receives the user's search request and emotional data and extracts relevant restaurants from the database. It generates a list of suitable restaurants taking into account the user's emotional state. The input data is the search request and emotional state information, and the output data is an optimized recommendation list. Specifically, the emotion engine analyzes the emotional data and matches the restaurant's environmental information with the emotional state.

[1618] Step 8:

[1619] User device:

[1620] The recommendation list is displayed on the user's device, where the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.The input data is the recommendation list sent from the server, and the output data is the displayed restaurant information.

[1621] Step 9:

[1622] User device:

[1623] A user provides feedback about a restaurant they visited. The input data is the feedback information, and the output data is a notification that the feedback has been sent.

[1624] Step 10:

[1625] server:

[1626] The collected feedback is stored in a database and used as training data for the system. The input data is the user's feedback information, and the output data is the updated training data. Specifically, the feedback information is analyzed and used to improve the recommendation algorithm next time.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] [Fourth embodiment]

[1631] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1632] 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.

[1633] 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).

[1634] 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.

[1635] 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.

[1636] 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).

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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."

[1644] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[1645] Explanation of program processing (in natural language)

[1646] 1. Enter and save user information

[1647] User's device

[1648] Users launch the application and enter their dietary restrictions and allergies on an initial setup screen, including, for example, nut allergies or gluten-free requirements.

[1649] The user may also turn on location services to provide current location information.

[1650] server

[1651] Information sent by the user is encrypted and sent to the server.

[1652] The server uses this information to create an individual profile for the user and stores it in a database.

[1653] 2. Collection and analysis of restaurant information

[1654] server

[1655] The server collects restaurant menu information in real time through the API of food applications and websites, or uses scraping technology to extract the required data if the API is not available.

[1656] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[1657] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[1658] 3. Restaurant Safety Rating

[1659] server

[1660] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[1661] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[1662] 4. Providing Recommendations

[1663] User's device

[1664] Within the application, users can search for restaurants near their current location.

[1665] server

[1666] It receives a user's search request and retrieves relevant restaurant information from a database, including restaurants that cater to specific dietary restrictions or allergies.

[1667] The extracted restaurant list is sorted in descending order of safety score.

[1668] The server generates the final recommendation list and sends it to the user.

[1669] User's device

[1670] Users can view a list of recommended restaurants in the application, including the restaurant name, address, and safety information for each menu item.

[1671] 5. Using User Feedback

[1672] User's device

[1673] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[1674] server

[1675] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[1676] Specific examples

[1677] 1. Parsing menu information by the server

[1678] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[1679] 2. User Recommendations

[1680] If User B has a nut allergy and wants a gluten-free meal, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe.

[1681] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying eating out with peace of mind.

[1682] The processing flow will be explained below.

[1683] Step 1:

[1684] User's device

[1685] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[1686] Step 2:

[1687] User's device

[1688] The user enables location services and allows the use of current location information.

[1689] This information is encrypted and sent to the server.

[1690] Step 3:

[1691] server

[1692] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[1693] Step 4:

[1694] server

[1695] We use APIs from food applications and websites to collect restaurant menu information in real time, and if APIs are not available, we use scraping technology to extract the required data.

[1696] Step 5:

[1697] server

[1698] The collected menu information is stored in a database along with detailed information such as the names of the dishes included, ingredients, and cooking methods.

[1699] Step 6:

[1700] server

[1701] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[1702] Step 7:

[1703] server

[1704] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[1705] Step 8:

[1706] User's device

[1707] Within the application, users can search for restaurants near their current location.

[1708] Step 9:

[1709] User's device

[1710] A search request is sent to the server.

[1711] Step 10:

[1712] server

[1713] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[1714] Step 11:

[1715] server

[1716] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed to generate a recommendation list.

[1717] Step 12:

[1718] server

[1719] The recommendation list is sent to the user's terminal.

[1720] Step 13:

[1721] User's device

[1722] The recommendation list is displayed on the user's device, where the user can view detailed menu information and safety level breakdowns.

[1723] Step 14:

[1724] User's device

[1725] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[1726] Step 15:

[1727] server

[1728] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[1729] Example 1

[1730] 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."

[1731] Conventional restaurant recommendation systems have difficulty finding suitable restaurants because they are unable to fully consider users' dietary restrictions and allergy information. Furthermore, due to insufficient safety assessment, users with allergies are unable to enjoy eating out with peace of mind. Furthermore, the lack of a mechanism for effectively utilizing user feedback makes it difficult to improve the accuracy of the system.

[1732] 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.

[1733] In this invention, the server includes means for inputting and saving a user's dietary restrictions and allergy information, means for collecting restaurant menu information and storing it in a database, means for analyzing the collected menu information and evaluating the safety of the menu based on the ingredients and allergy information, means for recommending restaurants that meet the search request based on the user's profile information and current location, and means for collecting and analyzing user feedback to improve the accuracy of the system, thereby enabling efficient recommendation of safe restaurants that meet the user's individual dietary restrictions and allergy information.

[1734] "User dietary restrictions" refer to requirements that individual users must meet to avoid certain dishes or ingredients, such as allergies or restrictions on certain ingredients, dietary restrictions based on religion or belief, or intake restrictions based on health conditions or doctor's instructions.

[1735] "Allergy information" is information indicating whether the user has an allergy to a specific food ingredient or seasoning. Specifically, it includes allergies to nuts, gluten, dairy products, shellfish, etc.

[1736] "Menu information" refers to data such as the names, ingredients, cooking methods, prices, and nutritional information of the dishes and drinks served by the restaurant.

[1737] A "database" is a system that stores and manages various types of information in an organized manner, and enables searching and updating. Specific examples include SQL databases and NoSQL databases.

[1738] A "safety rating" is a numerical or ranking indicator of how safe a menu item is for a particular user, based on the ingredients and allergy information contained in the menu item.

[1739] A "search request" is a request for information search made by a user to the system, and appropriate data is extracted based on this request.

[1740] "Recommendation" means that the system provides appropriate options based on the user's requirements and conditions.

[1741] "Feedback" refers to opinions and evaluations provided by users regarding the quality and satisfaction of the services or products they have used.

[1742] "Analysis" is the act of analyzing data in detail to extract information and discover its meaning and trends.

[1743] "Profile information" is a collection of information about an individual user, such as the user's personal information, dietary restrictions and allergy information, and past usage history.

[1744] This invention is a system that recommends restaurants that offer safe meals based on a user's dietary restrictions and allergy information. The system operates through a mobile application or web interface that can be used on the user's device.

[1745] Entering and saving user information

[1746] User's device

[1747] A user launches an application and authenticates at the login screen using an email address and password or a social login (e.g., Google or social media). After authentication, the user enters dietary restrictions and allergy information on the initial setup screen. The information is entered as input fields, with specific restrictions such as nut allergies or gluten-free options selected using checkboxes or input boxes. Turning on location services allows the user to provide their current location to the application.

[1748] server

[1749] Data sent by users is encrypted using SSL / TLS and sent to the server. The server receives the encrypted data and decrypts it using an algorithm such as AES before storing it in the database. Based on the decrypted information, a unique user ID is generated and the user's profile is saved in the database. The saved profile information is later used for restaurant recommendations.

[1750] Collection and analysis of restaurant information

[1751] server

[1752] Restaurant information is collected using APIs (e.g., public APIs) from food applications and food review sites. If APIs are not available, the necessary data is obtained using scraping techniques using Python's BeautifulSoup or Selenium. The collected data is stored in a temporary database and cleaned, including filling in missing data and removing duplicate data. After the data is cleaned, the menu information is analyzed and ingredients and cooking methods are extracted using NLP (natural language processing) techniques.

[1753] Restaurant Safety Rating

[1754] server

[1755] Based on the user's profile information, the analyzed menu information is compared and a safety score is assigned. For example, if a user profile has a "nut allergy" and there is a menu item that "contains nuts," the safety score for that menu item will be set low. The safety scores for all menu items are tallied to calculate a safety score for the entire restaurant. The score is saved in a database and will be used the next time the restaurant is evaluated.

[1756] Providing recommendation information

[1757] User's device

[1758] The user opens a search screen within the application to search for nearby restaurants. The search criteria include filter options such as "nut-free" and "gluten-free." When the user presses the search button, a search request is sent to the server.

[1759] server

[1760] The server receives the request, searches the database based on the user's current location and profile criteria, extracts restaurant information that matches the criteria, sorts them by safety score, and generates a final recommendation list that is sent back to the user's device in JSON format.

[1761] User's device

[1762] The user can then review the list of recommendations they receive, which includes the restaurant name, address, and safety information for the menu. For some restaurants, the user can also view ratings and reviews.

[1763] Using User Feedback

[1764] User's device

[1765] Users provide feedback about the restaurant they visited. Feedback is in text input format and includes items such as "food safety" and "quality of service." When they press the "send feedback" button, the feedback is sent to the server.

[1766] server

[1767] The collected feedback is stored in a database and categorised using text analysis, which will be used to improve the accuracy of future safety assessments and restaurant recommendation algorithms.

[1768] Specific examples

[1769] Parsing menu information by the server

[1770] For example, if the server collects menu information from a restaurant and determines that the menu item "grilled chicken" does not contain nuts or gluten, the menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[1771] User Recommendations

[1772] If a user has a nut allergy and wants gluten-free food, when he searches for restaurants near his current location, the server will prioritize recommendations of restaurants that are nut-free and offer gluten-free menu items. For example, a restaurant's "grilled chicken" is included in the recommendation list because it has a high safety rating.

[1773] Prompt Sentence Examples

[1774] "If I have a nut allergy and gluten-free diet, can you recommend some restaurants near my location?"

[1775] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1776] Step 1:

[1777] User's device

[1778] A user launches an application and authenticates at a login screen using an email address and password or a social login (e.g., Google or social media).

[1779] Enter your email address, password, or social login details

[1780] Output: Authentication token

[1781] What happens: An authentication token is generated and the user is authenticated.

[1782] Step 2:

[1783] User's device

[1784] After authentication, the user enters information about dietary restrictions and allergies on the initial setup screen.

[1785] Input: Dietary restrictions (e.g., nut allergies, gluten-free), location information

[1786] Output: User's dietary restrictions and location

[1787] Specific operation: Information entered on the device is checked for integrity and then sent to the server.

[1788] Step 3:

[1789] server

[1790] Data sent by the user is encrypted using SSL / TLS and sent to the server.

[1791] Input: Encrypted user data

[1792] Output: Decrypted user data

[1793] Specific operation: The server decrypts the received data using the AES algorithm and stores it in the database along with the user ID.

[1794] Step 4:

[1795] server

[1796] The server periodically collects restaurant menu information using APIs of food applications and food review sites, or uses scraping techniques if APIs are not available.

[1797] Input: Restaurant information obtained from API or data collected by scraping

[1798] Output: Cleaned restaurant menu information

[1799] What happens: The server cleans the data, filling in missing data and removing duplicates.

[1800] Step 5:

[1801] server

[1802] The cleaned data is analyzed and each restaurant's menu information is classified into ingredients and cooking methods using NLP technology.

[1803] Input: Cleaned menu information

[1804] Output: Categorized ingredients and cooking methods

[1805] Specific operation: Runs NLP algorithms to extract ingredients and cooking methods from menu information and saves them in a database.

[1806] Step 6:

[1807] server

[1808] A safety score is calculated based on the user's profile information and analyzed menu information.

[1809] Input: User profile information, menu information

[1810] Output: Safety score

[1811] Specific operation: The ingredient information of each menu item is compared with the user's allergy information, a safety score is calculated, and the result is saved in the database.

[1812] Step 7:

[1813] User's device

[1814] Users can search for nearby restaurants within the application, using filter options to find the right search criteria.

[1815] Input: Search request (current location and dietary restrictions)

[1816] Output: Search result list

[1817] What happens next: A search request is sent to the server.

[1818] Step 8:

[1819] server

[1820] The server receives the user's search request and extracts restaurant information that meets the search criteria.

[1821] Input: Search request, restaurant information in database

[1822] Output: Recommendation list

[1823] What it does: Extracts relevant restaurant information from a database and generates a prioritized list based on safety scores.

[1824] Step 9:

[1825] User's device

[1826] The user checks the list of recommended restaurants displayed on the device, along with safety information for each restaurant.

[1827] Input: Recommendation list sent from the server

[1828] Output: The displayed list of restaurants

[1829] What happens: The application receives the recommendation list and displays it to the user.

[1830] Step 10:

[1831] User's device

[1832] Users provide feedback about the restaurants they visit.

[1833] Input: User feedback (e.g. text data)

[1834] Output: Feedback data to be sent

[1835] What it does: Collects feedback and sends it to the server.

[1836] Step 11:

[1837] server

[1838] The server analyzes the received feedback, stores it in a database, and uses it for analysis to improve the accuracy of the system.

[1839] Input: User feedback

[1840] Output: Analysis results and updated evaluation data

[1841] Specific operation: The feedback content is classified using text analysis, and the feedback information is reflected in future safety assessments and recommendation algorithms.

[1842] (Application example 1)

[1843] 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."

[1844] Conventional systems only recommend restaurants that can provide safe meals based on a user's dietary restrictions and allergies, but lack systems that support delivery services. This limits the options available to users for obtaining safe meals at home or at work. Furthermore, the lack of an integrated method for efficiently recommending delivery services that meet a user's dietary restrictions makes it difficult to improve the user experience.

[1845] 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.

[1846] In this invention, the server includes a means for inputting and saving a user's dietary restriction and allergy information, a means for collecting and storing menu information from restaurants and delivery services in a database, a means for evaluating the safety of menu items based on ingredients and allergy information, a means for recommending restaurants or delivery services with a high safety rating in the area in response to a user's search request, and a means for collecting and analyzing user feedback. This allows users to easily select safe meals and enjoy meals at any location, such as their home or workplace. Furthermore, by utilizing user feedback to improve the accuracy of recommendations in future periods, the system can be continuously improved.

[1847] "Means for inputting and saving information about a user's dietary restrictions and allergies" is a function that allows a user to input information about their own dietary restrictions and allergies and save that information within the system.

[1848] The "means for collecting restaurant menu information and storing it in a database" is a function for collecting menu information from external restaurants and storing it in a database.

[1849] The "means for evaluating the safety of menu items based on ingredient and allergy information" is a function that compares the collected ingredient information of menu items with the user's allergy information to evaluate how safe each menu item is for the user.

[1850] "Means for recommending safe restaurants in the area in response to a user's search request" is a function that receives a search request from a user and recommends safe restaurants in the area based on that information.

[1851] The "means for collecting and analyzing user feedback" is a function that collects feedback information provided by users and analyzes it to help improve the system.

[1852] The "means for recommending delivery services" is a function that recommends delivery services that can provide safe meals based on the user's dietary restrictions and allergy information.

[1853] "Use the user's location information to search for restaurants or delivery services near the current location" is a function that uses the user's current location information to search for safe restaurants and delivery services in the vicinity.

[1854] "Collecting menu information from restaurants and delivery services using APIs or scraping technology" refers to a method of collecting menu information from restaurants and delivery services externally using APIs or scraping technology.

[1855] This invention relates to a system that recommends restaurants and delivery services that can provide safe meals based on a user's dietary restrictions and allergy information. The system operates using a server, user terminals, databases, APIs, scraping technology, etc.

[1856] The server does the following:

[1857] 1. Enter and save your user information:

[1858] Users enter their dietary restrictions and allergy information using a device such as a smartphone. This information, along with the user's location information, is sent to a server, encrypted, and then stored in a database.

[1859] 2. Collection of restaurant and delivery service menu information:

[1860] The server uses external APIs and scraping technology to collect menu information from restaurants and delivery services, and stores the collected information in a database.

[1861] 3. Menu Safety Rating:

[1862] The server compares the collected information on ingredients in the menu with the user's allergy information and executes an algorithm to evaluate the safety of the food. The results of this evaluation are stored in a database.

[1863] 4. Providing Recommendations:

[1864] When a user searches for restaurants or delivery services near their current location, the server prioritizes and recommends safe options that meet the user's dietary restrictions. This information is displayed on the user's device.

[1865] 5. Collecting and Analyzing User Feedback:

[1866] Users provide feedback about the restaurants they visit and the delivery services they use, which is sent to the server and analyzed to help improve the system's recommendations.

[1867] Examples:

[1868] When a user with a nut allergy searches for gluten-free meals near their current location, the server compares the collected menu information with the user's allergy information and recommends restaurants and delivery services that offer nut-free and gluten-free menus, allowing the user to choose a safe meal.

[1869] Example prompt sentence:

[1870] When a user searches for "nut allergy" and "gluten-free," the server provides a list of recommended restaurants and delivery services. For example, it might recommend "restaurants that serve gluten-free grilled chicken that doesn't contain nuts."

[1871] The specific hardware and software used includes a smartphone, a server, an SQLite database, Flask (a web server framework), and scraping technology, as well as using APIs to obtain external data.

[1872] By using generative AI models and prompt sentences, it is possible to further improve the analysis of user feedback and the recommendation algorithm, thus realizing a system that increases the reliability and convenience of providing users with safe and comfortable meals.

[1873] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1874] Step 1:

[1875] The user enters their dietary restrictions and allergy information and sends it from their device to the server. The user uses a smartphone app to enter information such as nut allergies and gluten-free eating, and turns on location services. This information is encrypted and sent to the server. The input data is dietary restrictions, allergy information, and location information, and the output is encrypted user profile data.

[1876] Step 2:

[1877] The server stores the received user information in a database. The server decrypts the received encrypted data and stores the user's dietary restrictions, allergy information, and location information in a database. The input data is the encrypted user profile data, and the output is the user profile stored in the database.

[1878] Step 3:

[1879] The server uses API or scraping technology to collect menu information from restaurants and delivery services. It obtains menu information in real time using APIs provided by external services or collects menu information through web scraping. The input data is the API response or scraping results, and the output is the collected menu information.

[1880] Step 4:

[1881] The server stores the collected menu information in a database. The server automatically stores the acquired menu information in a database for subsequent processing. The input data is the collected menu information, and the output is the menu information stored in the database.

[1882] Step 5:

[1883] The server evaluates the safety of the menu items. It matches the collected menu information with the user's allergy information and runs a safety evaluation algorithm. For example, a menu item containing nuts will be evaluated as less safe for a user with a nut allergy. The input data are the user profile and menu information, and the output is a safety evaluation score for each menu item.

[1884] Step 6:

[1885] The server recommends safe restaurants and delivery services in the area in response to a user's search request. When a user searches for safe meals near their current location, the server generates a recommendation list based on the safety scores of the corresponding menu items from the database. The input data is the user's search request and current location information, and the output is a recommended list of safe restaurants and delivery services.

[1886] Step 7:

[1887] The user selects a restaurant or delivery service from the recommended list and visits or orders. The user selects a restaurant or delivery service that provides safe meals from the provided recommended list and uses it. The input data is the recommended list, and the output is information about the selected restaurant or delivery service.

[1888] Step 8:

[1889] Users provide feedback and send it to the server. Feedback about the safety of food and the quality of service is input from the terminal and sent to the server. This feedback is saved as learning data for the system and used to improve the accuracy of recommendations in the future. The input data is the user's feedback, and the output is the feedback information saved in the database.

[1890] In this way, the present invention realizes a system that efficiently recommends restaurants and delivery services that offer safe meals based on the user's dietary restrictions and allergy information.

[1891] 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.

[1892] This invention is a system for recommending restaurants that offer safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine for recognizing the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface available on the user's device.

[1893] Explanation of program processing (in natural language)

[1894] 1. Enter and save user information

[1895] User's device

[1896] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[1897] The user may also turn on location services to provide current location information.

[1898] server

[1899] The information sent by the user is encrypted before being sent to the server.

[1900] The server uses this information to create an individual profile for the user and stores it in a database.

[1901] 2. Collection and analysis of restaurant information

[1902] server

[1903] Use APIs from food applications and websites to collect restaurant menu information in real time, or use scraping techniques to extract the required data if APIs are not available.

[1904] The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database.

[1905] The server also uses an analytical algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[1906] 3. Restaurant Safety Rating

[1907] server

[1908] The system compares the user's profile information with the menu information stored in the database and assigns a safety score to each menu item. For example, a menu item containing nuts will be rated as less safe for users with nut allergies.

[1909] The safety rating of the entire restaurant is aggregated and stored in a database, which is then used in the recommendation process.

[1910] 4. Providing Recommendations

[1911] User's device

[1912] Within the application, users can search for restaurants near their current location.

[1913] User's device

[1914] Emotional data is collected using the built-in camera and microphone to understand the user's current emotional state.

[1915] server

[1916] It receives a user's search request and emotion data and extracts relevant restaurants from the database.

[1917] The system generates a list of appropriate restaurants based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants.

[1918] The extracted restaurant list is optimized taking into account the safety score and sent to the user.

[1919] User's device

[1920] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[1921] 5. Using User Feedback

[1922] User's device

[1923] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[1924] server

[1925] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[1926] Specific examples

[1927] 1. Parsing menu information by the server

[1928] For example, the server collects menu information from Restaurant A and analyzes the ingredients included in each menu item. If it is determined that the menu item "grilled chicken" does not contain nuts or gluten, this menu item will be rated as "highly safe" for users with nut allergies or who are gluten-free.

[1929] 2. User Recommendations

[1930] If User B has a nut allergy, wants a gluten-free diet, and has recently been stressed, when he searches for restaurants near his current location, the server will prioritize recommending restaurants that are nut-free and offer gluten-free menus. For example, Restaurant A is included in the recommendation list because its "grilled chicken" is rated as safe and has a quiet, relaxing environment.

[1931] In this way, the system of the present invention significantly expands the user's dining options and provides support for enjoying dining out with peace of mind. In addition, by utilizing the user's emotional information, more personalized recommendations become possible.

[1932] The processing flow will be explained below.

[1933] Step 1:

[1934] User's device

[1935] Users launch the application and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, and vegan diets.

[1936] Step 2:

[1937] User's device

[1938] The user enables location services and allows the use of current location information.

[1939] This information is encrypted and sent to the server.

[1940] Step 3:

[1941] server

[1942] The system receives dietary restrictions and allergy information sent by users and stores it in a database as an individual profile for each user.

[1943] Step 4:

[1944] server

[1945] Use APIs to collect restaurant menu information from food applications and websites in real time, or use scraping techniques to extract the required data if APIs are not available.

[1946] Step 5:

[1947] server

[1948] The collected menu information is stored in a database along with detailed dish names, ingredients, cooking methods, etc.

[1949] Step 6:

[1950] server

[1951] It analyzes stored menu information, compares it with the user's allergy information, and runs algorithms to determine whether certain ingredients (e.g., nuts, gluten) are included and assess their safety.

[1952] Step 7:

[1953] server

[1954] A safety score is calculated for each menu item and stored in a database, along with an aggregate safety score for the entire restaurant.

[1955] Step 8:

[1956] User's device

[1957] Within the application, users can search for restaurants near their current location.

[1958] Step 9:

[1959] User's device

[1960] A search request is sent to the server.

[1961] Step 10:

[1962] server

[1963] Based on the user's current location information and dietary restrictions / allergy information, restaurants in the relevant area are extracted from the database.

[1964] Step 11:

[1965] server

[1966] Among the extracted restaurants, restaurants with high safety scores are prioritized and listed.

[1967] Step 12:

[1968] server

[1969] To take into account the user's emotional state, the emotion engine analyzes emotional data obtained from the user's device, including data collected through the built-in camera and microphone.

[1970] Step 13:

[1971] server

[1972] Optimize the recommendation list based on the user's emotional state: for example, prioritize quiet, relaxing restaurants if the user is feeling stressed.

[1973] Step 14:

[1974] server

[1975] A final recommendation list is generated and sent to the user's terminal.

[1976] Step 15:

[1977] User's device

[1978] The recommendation list is displayed on the user's device, where they can see detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[1979] Step 16:

[1980] User's device

[1981] Users enter feedback about the restaurant they visit (e.g., whether they had any allergic reactions, quality of service, etc.).

[1982] Step 17:

[1983] server

[1984] The feedback information is sent to the server and stored in a database. This feedback is incorporated into the system's learning data and will be used to improve the accuracy of future safety ratings and restaurant recommendations.

[1985] Example 2

[1986] 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."

[1987] To enjoy eating out with peace of mind, users need to find safe restaurants based on dietary restrictions and allergies. However, this process is cumbersome, and it can be difficult to obtain appropriate information. Furthermore, because the user's emotional state also influences the selection of an appropriate restaurant, recommendations that take into account the user's current emotional state, rather than just ingredient information, are needed. Furthermore, it is a challenge to continuously improve recommendation accuracy by effectively utilizing user feedback.

[1988] 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.

[1989] In this invention, the server includes means for inputting and saving a user's dietary restriction and allergy information, means for collecting restaurant menu information and storing it in a database, means for evaluating the safety of menu items based on ingredient and allergy information, means for recommending safe restaurants in the area in response to a user's search request, means for collecting user emotional information to improve recommendation accuracy, and means for collecting and analyzing user feedback. This allows users to not only find safe restaurants that meet their dietary restrictions and allergies, but also receive recommendations that suit their emotional state at the time, allowing them to enjoy eating out with peace of mind. Furthermore, collecting and analyzing feedback information enables continuous improvement in recommendation accuracy.

[1990] "User's dietary restrictions and allergy information" refers to information about a user's inability to or restrictions on the intake of certain ingredients, such as nut allergies, gluten-free diets, or vegan diets.

[1991] "Storage means" refers to devices or software that record the entered information in a safe and appropriate location and make it available for retrieval when necessary.

[1992] "Restaurant menu information" refers to information about the dishes served at each restaurant, their ingredients, cooking methods, etc.

[1993] "Means of collection" refers to devices or software that obtain necessary information from outside using APIs, scraping technology, etc.

[1994] A "database" refers to a data repository that stores collected information systematically and allows it to be searched and used as needed.

[1995] "Methods for evaluating the safety of menu items based on ingredient and allergy information" refers to the process or algorithm that analyzes the ingredients contained in each menu item and compares them with the user's allergy information to quantify the safety of the menu item.

[1996] "User search request" means an input or instruction a User makes using a Device to search for specific information.

[1997] The "relevant area" refers to the location information of the device currently being operated by the user or the area specified by the user.

[1998] A "safe restaurant" refers to a restaurant that has been rated as safe in light of the user's dietary restrictions and allergy information.

[1999] "Recommendation means" refers to the system's functions and algorithms that extract restaurants that meet the criteria from the database and present them to the user.

[2000] "User's emotional information" is data that represents the user's current psychological state and mood, and refers to information acquired from facial expressions, tone of voice, speech content, and the like.

[2001] "Means to improve recommendation accuracy" refers to algorithms and system functions that incorporate users' emotional information to suggest more appropriate restaurants.

[2002] "Means for collecting and analyzing feedback" refers to the process or system for storing user-provided opinions and ratings in a database and analyzing that data.

[2003] This invention is a system that recommends restaurants that can provide safe meals based on a user's dietary restrictions and allergy information, and further includes an emotion engine that recognizes the user's emotion information to improve recommendation accuracy. The system operates through a mobile application or web interface that can be used on the user's device.

[2004] Entering and saving user information

[2005] User's device

[2006] Users launch the smartphone application and enter their dietary restrictions and allergies on the initial setup screen. For example, they can enter information such as nut allergies, gluten-free diets, or vegan diets. Next, they authorize the use of location services, which then uses the GPS function to collect their current location.

[2007] server

[2008] The server receives the dietary restriction information and current location information sent by the user. The received data is encrypted and stored securely. A user profile is created in the database and the entered information is stored.

[2009] Collection and analysis of restaurant information

[2010] server

[2011] The server collects restaurant menu information in real time from external food applications and websites using APIs. If APIs are not available, it uses web scraping technology to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to parse the ingredient list for each menu item and match it with the user's allergy information.

[2012] Specific working example:

[2013] The server confirms that there is a dish called "grilled chicken" on the menu and that it is nut- and gluten-free.

[2014] Restaurant Safety Rating

[2015] server

[2016] The server compares the user's profile information stored in the database with the restaurant's menu information, checking whether the ingredients in each menu item match the user's allergy information. It then scores the safety of the menu items, rating, for example, "dishes containing nuts" as low safety for users with nut allergies. The safety score for the entire restaurant is then tallied and stored in the database.

[2017] Specific working example:

[2018] The server rates the safety of "grilled chicken" at 95 out of 100, and takes into account the restaurant's overall safety rating even if other high-risk menu items are present.

[2019] Providing recommendation information

[2020] User's device

[2021] The user opens the application and searches for nearby restaurants. The application collects the user's emotional data (e.g., facial expressions, tone of voice) using the device's built-in camera and microphone.

[2022] server

[2023] The server receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize quiet and relaxing restaurants. Finally, it sends the user an optimized restaurant list that also takes safety scores into account.

[2024] User's device

[2025] A list of recommended restaurants will be displayed on the user's device, where the user can view detailed menu information, safety ratings, and special recommendation reasons based on sentiment.

[2026] Specific working example:

[2027] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[2028] Using User Feedback

[2029] User's device

[2030] Users enter feedback about the restaurants they visit in the application, including, for example, food safety, quality of service, and reactions to specific ingredients.

[2031] server

[2032] The server stores the received feedback in a database and analyzes the aggregated feedback data to use as training data for the system, thereby improving the accuracy of future safety assessments and restaurant recommendations.

[2033] Specific working example:

[2034] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[2035] Prompt Sentence Examples

[2036] Below are some example prompts to input to a generative AI model:

[2037] "Please suggest restaurants to a user who has a nut allergy and wants gluten-free food, and has been feeling stressed lately. The user's current location is around Tokyo Station."

[2038] Based on this prompt, the system can recommend an appropriate restaurant.

[2039] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2040] Step 1: Enter and save user information

[2041] User's device

[2042] 1. The user launches the application on their smartphone.

[2043] 2. On the initial setup screen, enter your dietary restrictions (e.g., nut allergies, gluten-free, vegan, etc.) and allergy information. This will be your input data.

[2044] 3. Next, allow location services to collect your location using the GPS function.

[2045] 4. This information is sent to the server, and the output is encrypted user information and current location information.

[2046] server

[2047] 5. The server receives the dietary restriction information and current location information sent by the user. The input is the encrypted user information and current location information.

[2048] 6. Decrypt and securely store the received data.

[2049] 7. Create a user profile in the database and store the entered information. The output is a user profile stored in the database.

[2050] Step 2: Collect and analyze restaurant information

[2051] server

[2052] 1. The server collects restaurant menu information from external food applications and websites using APIs. If APIs are not available, scraping techniques are used. The input is the menu information collected via APIs.

[2053] 2. Automatically store the collected menu information in a database. The output is restaurant menu information stored in a database.

[2054] 3. The server uses an analysis algorithm to analyze the ingredient list for each menu item and compares the entered menu information with the user's allergy information. The input is the user's allergy information stored in the database and the restaurant's menu information.

[2055] 4. The safety of the menu is evaluated by comparing ingredients with allergy information and stored in a database. The output is the safety of the evaluated menu.

[2056] Specific operation example

[2057] The server confirms that the dish "grilled chicken" is nut- and gluten-free and rates it "highly safe."

[2058] Step 3: Restaurant Safety Assessment

[2059] server

[2060] 1. Compare the user profile information stored in the database with restaurant menu information. The input data is the user profile information and restaurant menu information.

[2061] 2. Check whether the ingredients in each menu match the user's allergy information.

[2062] 3. Score the safety of the menu items, for example, "dishes containing nuts" are rated as less safe for users with nut allergies. The output is a safety score for each menu item.

[2063] 4. The safety score of the entire restaurant is calculated and stored in a database. The output is the overall safety score of the restaurant.

[2064] Specific operation example

[2065] The server rates the safety of the "grilled chicken" at 95 out of 100, which determines the restaurant's overall safety rating.

[2066] Step 4: Provide a recommendation

[2067] User's device

[2068] 1. A user opens the application and searches for nearby restaurants. The input is the user's search request.

[2069] 2. Collect user emotion data using the device's built-in camera and microphone. The input is the collected emotion data.

[2070] 3. This information is sent to the server, and the output is an encrypted search request and sentiment data.

[2071] server

[2072] 4. The server receives the user's search request and emotion data. The input is the encrypted search request and emotion data.

[2073] 5. Extract relevant restaurants from the database. The input is the user's search request.

[2074] 6. Generate an appropriate restaurant list based on the user's emotional state. For example, if the user is feeling stressed, prioritize quiet and relaxing restaurants. The output is a restaurant list optimized for the user's emotional state.

[2075] 7. The search results are optimized taking into account the safety score and sent to the user.

[2076] User's device

[2077] 8. The recommended restaurant list is displayed. The user can see detailed menu information, safety ratings, and special recommendation reasons based on sentiment. The output is the displayed restaurant list.

[2078] Specific operation example

[2079] Users can find Restaurant A, which serves grilled chicken in a safe and quiet environment.

[2080] Step 5: Use user feedback

[2081] User's device

[2082] 1. A user enters feedback about a restaurant they visited in the application. The input is feedback information.

[2083] 2. Send the feedback to the server. The output is the encrypted feedback information.

[2084] server

[2085] 3. The server stores the received feedback in a database and analyzes the aggregated feedback data. The input is the encrypted feedback information.

[2086] 4. The feedback data is used as training data for the system, which will improve the accuracy of future safety assessments and restaurant recommendations. The output is the analyzed feedback information.

[2087] Specific operation example

[2088] A user submits feedback such as "The grilled chicken was very tasty, but I wasn't informed that the dessert contained nuts."

[2089] (Application example 2)

[2090] 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."

[2091] Conventional food recommendation systems are limited to recommending restaurants based on a user's dietary restrictions and allergies, and do not consider individual emotional states. This makes it difficult to recommend restaurants and menus that suit a user's physical and mental state. To solve this problem, there is a need for more personalized recommendations that utilize the user's emotional information.

[2092] 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 recognizing user emotional information to improve recommendation accuracy, means for recommending restaurants with high safety ratings in the relevant area in response to the user's search request, and means for collecting and analyzing user feedback. This makes it possible to recommend restaurants and menus that are personalized according to the user's emotional state.

[2093] "User's dietary restrictions and allergy information" refers to information that indicates when a user needs to avoid certain ingredients or when a user's food choices are restricted based on their health condition.

[2094] "Menu information" refers to detailed information such as the name, ingredients, cooking method, and price of the dishes served at a restaurant.

[2095] A "database" is a set of data storage mechanisms for efficiently storing, managing, and retrieving collected information.

[2096] "Safety level" is a score that evaluates how low a risk a menu item poses to a user's dietary restrictions or allergy information.

[2097] "Emotional information" is data that indicates the user's psychological state and emotions, and is information collected from the user's facial expressions, tone of voice, and the like.

[2098] "Recommendation accuracy" refers to the system's ability to select restaurants and menus that best suit the user's conditions and emotions.

[2099] A "search request" is a request made by a user to the system for specific information.

[2100] "Feedback" refers to users providing ratings and comments about restaurants and menus they have actually visited.

[2101] This invention is a system that recommends appropriate restaurants and menus based on a user's dietary restrictions, allergy information, and emotional information. The system operates as follows.

[2102] 1. Enter and save user information

[2103] User device:

[2104] Users launch the app and enter their dietary restrictions and allergies on the initial setup screen, such as nut allergies, gluten-free diets, or vegan diets. They can also turn on location services to provide their current location.

[2105] server:

[2106] The information sent by the user is encrypted and sent to the server, which uses this information to create an individual profile for the user and stores it in a database.

[2107] 2. Collection and analysis of restaurant information

[2108] server:

[2109] Restaurant menu information is collected in real time from food applications and websites using APIs. If APIs are not available, the necessary data is extracted using scraping technology. The collected menu information (e.g., dish name, ingredients, cooking method) is automatically stored in a database. The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment.

[2110] 3. Restaurant Safety Rating

[2111] server:

[2112] The system compares the user's profile information with the menu information stored in the database and scores the safety of each menu item. For example, a menu item containing nuts is rated as less safe for a user with a nut allergy. The safety score for the entire restaurant is then aggregated and stored in the database. This information is used later in the recommendation process.

[2113] 4. Providing Recommendations

[2114] User device:

[2115] Within the application, users can search for restaurants near their current location, and the app also uses the built-in camera and microphone to collect emotional data to recognize the user's current emotional state.

[2116] server:

[2117] The system receives the user's search request and emotional data, extracts relevant restaurants from the database, and generates a list of appropriate restaurants taking into account the user's emotional state. For example, if the user is feeling stressed, it will prioritize recommending quiet and relaxing restaurants. The extracted restaurant list is then optimized, taking into account the safety score, and sent to the user.

[2118] User device:

[2119] The recommendation list is displayed on the user's device, and the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.

[2120] 5. Using User Feedback

[2121] User device:

[2122] Users can provide feedback about the restaurants they visit, including food safety, service quality, and reactions to specific ingredients.

[2123] server:

[2124] The collected feedback is stored in a database and used as training data for the system, which will help improve the accuracy of future safety ratings and restaurant recommendations.

[2125] Specific examples

[2126] Parsing menu information by the server

[2127] For example, the server collects menu information from a restaurant and analyzes the ingredients in each menu item. If it determines that a menu item called "grilled chicken" does not contain nuts or gluten, it will rate this menu as safe for customers with nut allergies or who are gluten-free.

[2128] User Recommendations

[2129] If a user has a nut allergy, wants to eat gluten-free, and has recently been stressed, they can search for restaurants near their current location and the system will recommend restaurants that offer gluten-free menus, are nut-free, and have a quiet, relaxing environment.

[2130] Prompt Sentence Examples

[2131] "Recommend restaurants that offer safe meals based on dietary restrictions and allergies. If the user is stressed, prioritize quiet and relaxing restaurants. For example, if the user is vegan and has a nut allergy, generate a list of restaurants based on this information. Don't forget to take emotional information into account."

[2132] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2133] Step 1:

[2134] User device:

[2135] The user starts the application and enters their dietary restrictions and allergy information on the initial setup screen. The entered information may include, for example, nut allergies, gluten-free diets, or vegan diets. This information is sent to the server as input data.

[2136] Step 2:

[2137] server:

[2138] The dietary restrictions and allergy information submitted by the user is encrypted and sent to the server. The server receives this information, creates an individual profile for the user, and stores it in a database. The input data is output as saved profile information.

[2139] Step 3:

[2140] server:

[2141] The server uses APIs from food applications and websites to collect restaurant menu information in real time. If APIs are not available, scraping techniques are used to extract the necessary data. The collected menu information (e.g., dish name, ingredients, cooking method) is stored in a database. The input data is restaurant information, and the output data is the saved menu information.

[2142] Step 4:

[2143] server:

[2144] The server uses an analysis algorithm to compare the ingredient list for each menu item with the user's allergy information and perform a safety assessment. The input data is the menu information and the user's profile information, and the output data is a safety score for each menu item. Specifically, the server checks whether the allergens match the ingredient list.

[2145] Step 5:

[2146] User device:

[2147] A user searches for restaurants near their current location within the application. The device obtains the current location information and sends a search request to the server. The input data is the search request, and the output data is a list of search results from the server.

[2148] Step 6:

[2149] User device:

[2150] To understand the user's current emotional state, emotional data is collected using the built-in camera and microphone. The device then transmits the collected emotional data to a server. The input data is emotional state information, and the output data is a notification of completion of transmission to the server.

[2151] Step 7:

[2152] server:

[2153] It receives the user's search request and emotional data and extracts relevant restaurants from the database. It generates a list of suitable restaurants taking into account the user's emotional state. The input data is the search request and emotional state information, and the output data is an optimized recommendation list. Specifically, the emotion engine analyzes the emotional data and matches the restaurant's environmental information with the emotional state.

[2154] Step 8:

[2155] User device:

[2156] The recommendation list is displayed on the user's device, where the user can check detailed menu information, safety ratings, and special recommendation reasons based on emotions.The input data is the recommendation list sent from the server, and the output data is the displayed restaurant information.

[2157] Step 9:

[2158] User device:

[2159] A user provides feedback about a restaurant they visited. The input data is the feedback information, and the output data is a notification that the feedback has been sent.

[2160] Step 10:

[2161] server:

[2162] The collected feedback is stored in a database and used as training data for the system. The input data is the user's feedback information, and the output data is the updated training data. Specifically, the feedback information is analyzed and used to improve the recommendation algorithm next time.

[2163] 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.

[2164] 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.

[2165] 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.

[2166] 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.

[2167] 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.

[2168] 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.

[2169] 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).

[2170] 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.

[2171] 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."

[2172] 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.

[2173] 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).

[2174] 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.

[2175] 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.

[2176] 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.

[2177] 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.

[2178] 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.

[2179] 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.

[2180] 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.

[2181] 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.

[2182] 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.

[2183] 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.

[2184] The following ...

Claims

1. a means for inputting and storing the user's dietary restrictions and allergy information; a means for collecting and storing restaurant menu information in a database; A means to evaluate the safety of menu items based on ingredients and allergy information, and A means for recommending safe restaurants in the area in response to a user's search request; A system including a means for collecting and analyzing user feedback.

2. The system according to claim 1, wherein the system searches for restaurants around the user's current location using the user's location information.

3. The system of claim 1, wherein restaurant menu information is collected using an API or scraping technology.

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