system

Abstract

We provide the system. [Solution] A means of obtaining information on food preferences, dietary restrictions, and health information from users, A means for analyzing the acquired information and selecting an appropriate food and beverage facility or food provider, A means for automatically coordinating reservations or orders at the selected food and beverage establishments or food providers, A means for suggesting a meal menu based on the user's current health status, A means for sharing information about the aforementioned food and beverage establishment or food provider and food menu with other users, A means for optimizing the meal menu using a generative AI model, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When dining out, there is often a problem that it takes a lot of effort to select and reserve a dining facility according to the individual food preferences and health conditions of users. Also, since the opportunity for users to share this information with each other is limited, it is difficult to obtain hints for developing new dining facilities.

Means for Solving the Problems

[0005] This invention provides a means for analyzing food preference information, restriction information, and health information entered by a user to select the most suitable dining establishment. It also provides a means for automatically adjusting reservations at the selected dining establishment. Furthermore, it includes a means for monitoring the user's health status in real time and suggesting meal menus based on this information. In addition, by providing a means for efficiently sharing the obtained information with other users, the system solves the above problems.

[0006] A "user" is an individual who uses the system to obtain information about restaurants and other food establishments and receive meal suggestions based on their health condition.

[0007] "Food preference information" refers to data on the foods and dishes that users prefer to consume.

[0008] "Restriction information" refers to data about allergies and dietary restrictions that users have.

[0009] "Health information" refers to data about a user's physical condition and health status.

[0010] "Food and beverage establishments" refer to shops, restaurants, and cafes that serve food and drinks.

[0011] "Methods for automatically adjusting reservations" refer to a function where the system automatically confirms reservations based on the availability of seats at restaurants and bars.

[0012] "Methods for suggesting meal menus" refers to a function that takes the user's health condition into consideration and presents the most suitable ingredients and dishes.

[0013] "Means of sharing information with other users" refers to a function that allows one user to share information about restaurants and food with other users. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system that suggests the most suitable dining establishments based on a user's food preferences, restrictions, and health status, and further automates the reservation process. This system mainly consists of three components: a server, a terminal, and a user.

[0036] First, users enter their food preferences and restrictions (e.g., allergies or religious reasons), as well as their current health information, through their smartphones, tablets, or other devices. This information is temporarily stored on the device and then transmitted to the server via the network.

[0037] The server receives data sent by users and stores it in a cloud-based database. This database is used as foundational data for providing meal suggestions based on user preferences and health status, utilizing big data analytics techniques. The server also periodically obtains the latest restaurant information from external food and beverage provider data providers and matches it with user information to select appropriate restaurants.

[0038] After selection, the terminal receives a list of suggested restaurants from the server and presents it visually to the user. The terminal is also configured to display detailed information about the restaurants and food recommendations tailored to the user's health condition.

[0039] For example, if a user prefers Japanese food and has a nut allergy, the server will list restaurants that meet these conditions and offer appropriate menus based on the user's current health data (e.g., blood sugar levels and calorie expenditure). This list is sent to the user's terminal for easy review.

[0040] The server also processes reservation requests from users and checks the reservation status of the selected facility. If a reservation is available, it quickly returns that information to the terminal for the user to confirm. Furthermore, the terminal sends data from IoT devices to the server, enabling more accurate dietary suggestions based on health predictions.

[0041] This system streamlines the above-mentioned processes, making the dining-out experience more efficient and healthier for users.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses a terminal to input data about their food preferences, allergy information, and health status. The terminal temporarily stores this data in a local database and prepares it for transmission to the server.

[0045] Step 2:

[0046] The terminal sends user data to the server via the network. The server formats the received data, organizes it by user, and then stores it in a cloud database.

[0047] Step 3:

[0048] The server periodically retrieves the latest data from an external food and beverage establishment information provider and updates its own database. This information includes the establishment's location, menu, and business hours.

[0049] Step 4:

[0050] The server initiates a process to select dining establishments based on the user's dietary preferences, restrictions, and health information. This includes analyzing the user's health data and generating suggestions using AI algorithms.

[0051] Step 5:

[0052] The server sends the selection results to the terminal, which receives them and displays a list of suggested restaurants to the user. Detailed information and recommended menus for each restaurant are also displayed simultaneously.

[0053] Step 6:

[0054] The user selects a destination from a suggested list and completes the booking process on the device. The device then sends the user's selection to the server.

[0055] Step 7:

[0056] The server contacts the restaurant's reservation system to check availability. If a reservation is possible, it confirms the reservation and generates confirmation information.

[0057] Step 8:

[0058] The server sends reservation confirmation information to the terminal, which receives it and notifies the user. It also updates the dietary recommendations based on the presented health menu.

[0059] (Example 1)

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

[0061] In modern society, people's eating habits are becoming increasingly diverse, and their preferences and restrictions regarding food are becoming more complex. Therefore, there is a need for efficient methods to select and reserve restaurants that meet individual needs. Furthermore, suggesting meals tailored to individual health conditions is also important, and a system that comprehensively addresses these needs is required.

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

[0063] In this invention, the server includes means for acquiring food preference attributes, restriction attributes, and health attributes from the user, means for storing the attributes in a storage area on the cloud, and means for periodically acquiring facility information from an external provider database. This enables the rapid selection and reservation of dining facilities that meet the user's needs.

[0064] A "user" refers to an individual who uses the system to provide information about their dietary preferences, restrictions, and health status.

[0065] "Food preference attributes" refer to information about the specific foods, drinks, types of cuisine, and styles that a user prefers.

[0066] "Restriction attributes" refer to information about food and beverages that users should avoid or are prohibited from eating or drinking.

[0067] "Health attributes" refer to information related to the user's current health status, and appropriate dietary suggestions are provided based on this information.

[0068] "Cloud storage" refers to virtual storage space for storing and accessing data via the internet.

[0069] A "provider database" is an information storage system for acquiring and managing information about food and beverage establishments from external sources.

[0070] "Mathematical analysis techniques" refer to mathematical methods and algorithms used for analyzing and processing data.

[0071] A "detection device" is an external hardware device that monitors the user's health status and transmits that data to the system.

[0072] A "storage" is a data storage area for structuring external information and updating it regularly.

[0073] This invention is a system that suggests the most suitable dining establishments based on the user's food preferences, restrictions, and health condition, and automates the reservation process. The system mainly consists of three elements: a server, a terminal, and a user.

[0074] Users use smartphones or tablets to input their food preferences, dietary restrictions (such as allergies or religious background), and health information. This information is temporarily stored on the device and then transmitted to the server via secure communication using SSL / TLS.

[0075] The server utilizes cloud technology to securely store acquired user information and perform big data analysis. Here, it structures the information using database management systems such as MySQL® and MongoDB, and performs data analysis using analytical tools like Python's Pandas library and Google® Cloud BigQuery. Furthermore, it collects data from external food and beverage establishments via APIs and records it in the provider database.

[0076] Based on the analysis results, the server selects restaurants and bars that meet the user's needs and sends a list to the device. This list includes detailed information about the establishments (address, opening hours, menu options, etc.). Using a framework such as React Native, this information is presented to the user visually on the device.

[0077] For example, if a user prefers Japanese food and wants to avoid nuts, the server will list restaurants that meet these conditions and can provide menus that take into account the user's current blood sugar levels and calorie expenditure. This list is then transferred to the user's terminal, allowing them to easily review and select from it.

[0078] Furthermore, during the reservation process, the server processes the user's request and checks the reservation status of the selected facility. If a reservation is possible, the system quickly returns the result to the terminal and notifies the user. In addition, the system can make new suggestions using prompt messages generated by an AI model.

[0079] Example of a prompt:

[0080] 1. "Using the user's food preferences (e.g., Japanese cuisine), please propose a restaurant that takes nut allergies into consideration."

[0081] 2. "Find restaurants that offer calorie-restricted menus based on the user's health data."

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users input their food preferences, restrictions, and health status using devices such as smartphones and tablets. The entered data is temporarily stored on the device. Specifically, users enter information using the application's forms, and this data is structured in JSON format. The input data includes food preferences (e.g., Japanese food), restrictions (e.g., nut allergy), and health information (e.g., blood sugar levels). The structured data is then ready to be sent to the server as output.

[0085] Step 2:

[0086] The terminal securely transmits user data to the server using the SSL / TLS protocol. The previously structured data is used as input. The data is received by the server, its integrity is checked, and then it is stored in a cloud database. As output, the data is securely stored in the cloud database.

[0087] Step 3:

[0088] The server retrieves user data from a cloud-based database and performs data analysis using the Python Pandas library. Inputs include user preferences, restrictions, and health data. Specifically, it filters the user data to extract information on restaurants and bars that match the specified criteria. The output is a list of restaurants and bars that meet the criteria.

[0089] Step 4:

[0090] The server retrieves the latest information on food and beverage establishments from an external provider database. It periodically accesses the external database using an API to update the data. The input is establishment information from the external provider. The updated establishment information is stored in a cloud database as output.

[0091] Step 5:

[0092] The terminal presents the user with a list of restaurants and bars received from the server via a user interface. The list is visually displayed using the React Native framework. The input is a list of establishments from the server. The output allows the user to view and select establishment information.

[0093] Step 6:

[0094] The server checks the reservation status of the selected facility based on the user's facility selection. It queries the availability of the facility using the facility reservation API. The input is the facility data selected by the user. The output is the availability result and is sent to the terminal.

[0095] Step 7:

[0096] The terminal receives reservation information from the server and displays a confirmation screen to the user. Once the user confirms, the final reservation information is finalized. The input is the reservation result from the server. The output displays a screen confirming the reservation and allowing the user to check the reservation status.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In modern society, users often face various constraints related to their health status and food preferences. However, there are limited systems that take this information into consideration to select the most suitable food providers and restaurants and enable smooth usage. Therefore, there is a need to develop an environment where users can easily select meals that meet their requirements and where reservations and orders can be made automatically.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for acquiring information on food preferences, restrictions, and health information from the user; means for analyzing the acquired information and selecting an appropriate restaurant or food provider; and means for automatically coordinating reservations or orders at the selected restaurant or food provider. This allows users to choose meals according to their health condition and food preferences, and the automation of reservations and orders makes the service easier to use.

[0102] A "user" refers to an individual or organization that utilizes this system and is the entity that provides information on food preferences, restrictions, and health.

[0103] "Food preference information" refers to information about the types of dishes, seasonings, ingredients, etc. that the user prefers.

[0104] "Restriction information" refers to conditions that users must consider when making food choices, such as allergies or religious restrictions.

[0105] "Health information" refers to data about the user's physical health status, including parameters such as blood glucose levels and calorie consumption.

[0106] "Food and beverage establishments" refers to facilities that serve meals, including restaurants and cafes.

[0107] A "food provider" refers to a company or organization that provides services such as cooking and serving meals.

[0108] "Means for automatically coordinating reservations or orders" refers to a function in which the system automatically makes reservations or orders meals from restaurants or food providers selected by the user.

[0109] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to suggest the optimal meal choices based on user data.

[0110] An "external sensor device" is a hardware device used to acquire health status data from the user.

[0111] An "external database" is a database used by a system to store information about a food service establishment or food provider and to access that information.

[0112] The system for realizing this invention collects the user's food preferences, dietary restrictions, and health information, and has the function of automatically selecting an appropriate restaurant or food provider and making a reservation or order.

[0113] The system starts when a user inputs data about their food preferences and health status using a smartphone or tablet. This information is temporarily stored on the device and transmitted to a server via the internet. The server stores this information in a cloud-based database and, by incorporating big data analytics technology, determines which restaurant or food provider is best suited to the user's needs. Then, using a generative AI model, it optimizes specific meal menus based on the user's health data.

[0114] The server also periodically retrieves data from external restaurants and food providers and compares it with user information. This process enables the system to make appropriate suggestions to users. Selected options are resent to devices such as smartphones and tablets and displayed for the user to visually confirm. Users select their preferred options from the suggested choices, and the system automatically makes the reservation or order.

[0115] For example, if a user prefers a vegan diet and is gluten-intolerant, the server will list restaurants or food providers that meet these criteria and send the list to the user's terminal. Once the user selects a restaurant, the system will automatically complete the reservation process.

[0116] As an example of a prompt, data can be sent to the generating AI model in the form of, "Please suggest the optimal meal plan based on the user's restrictions and health data. Example: 'Suggest a vegan and gluten-free meal plan'."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] Users use their smartphones or tablets to input their dietary preferences, restrictions, and health information. This input information is temporarily stored on the device. This input includes allergies, dietary preferences (e.g., vegan, low-carb), and recent health data. The device organizes this information as structured data and prepares it for transmission to the server.

[0120] Step 2:

[0121] The device sends user data to the server. The transmitted data is analyzed on the server. The server stores the data in a cloud-based database and uses big data analytics technology to select restaurants or food providers that match the user's preferences. The analysis matches user data with information about restaurants and extracts options that meet the user's criteria.

[0122] Step 3:

[0123] The server uses a generative AI model to optimize meal plans based on the user's health data. This process takes the user's health parameters and past meal history as input, and runs a model that generates an optimal meal plan. As a result, a proposed menu is generated and sent from the server to the terminal.

[0124] Step 4:

[0125] The server periodically retrieves and updates information on restaurants and food providers from external data providers. This input includes information such as the establishment's menu, reputation, and location, and the database is updated on the server side to reflect the latest information.

[0126] Step 5:

[0127] The terminal displays a list of restaurants or food service providers and their menus, received from the server, to the user. This output allows the user to view visual information on their device. The user can tap on a selected restaurant or menu item from the suggestion list to view more detailed information.

[0128] Step 6:

[0129] When a user selects a desired restaurant or food provider, the terminal transmits that information to the server. The server automatically completes the reservation or order with the selected restaurant or provider. This process involves coordinating with the restaurant's reservation system to check seating availability and menu stock before confirming the reservation or order.

[0130] Step 7:

[0131] This system also provides a function for users to share applied information about facilities and meal menus. The goal is to add user feedback and ratings to the database and enable the sharing of information with other users.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] This invention is a food and beverage facility proposal system that incorporates the user's emotional state, and aims to improve the user experience using emotion recognition technology. This system mainly consists of four components: a server, a terminal, a user, and an emotion engine.

[0134] First, the user inputs information such as their food preferences and restrictions, health information, and current emotional state through their terminal device. This emotional information is then analyzed by an emotion engine. The emotion engine recognizes the user's emotional state using various data, including voice tone, facial expressions, and the speed of the user's selection actions. This information is temporarily stored on the terminal before being sent to the server.

[0135] The server receives data from users and stores it in a cloud database. The server also periodically retrieves information from external food and beverage establishment information providers and analyzes it in conjunction with user data. Based on the analysis results from the emotion engine, the server selects food and beverage establishments that take into account the user's stress level and satisfaction predictions.

[0136] To provide meal suggestions tailored to the user's emotional state, the device receives suggestions from the server and presents them to the user. These suggestions include menu recommendations that match the user's mood and options for dining establishments that can enhance a particular mood. For example, if the user is seeking relaxation, a restaurant with a quiet atmosphere will be recommended.

[0137] Furthermore, the system allows users to share emotions and corresponding facility information with other users to support information sharing among them. This feature aims to promote communication among users with similar emotional states and provide a space for empathy.

[0138] Furthermore, the reservation process can also be adapted to the user's emotional state. If the emotion engine detects a high-stress state, the reservation confirmation and modification procedures are designed to proceed more smoothly.

[0139] Through the processes described above, this system enables personalized service tailored to the user's emotions, providing a richer dining experience.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user uses a device to input data about their food preferences, dietary restrictions, health status, and emotional state. The device temporarily stores the entered data in a local database.

[0143] Step 2:

[0144] The device activates an emotion engine and collects data such as the user's voice tone, facial expressions, and operation speed through sensors and cameras. This data is used to analyze the user's emotional state.

[0145] Step 3:

[0146] The emotion engine analyzes the collected data to identify the user's emotional state. The results are returned to the device in the form of numerical values ​​representing stress levels and emotional tendencies.

[0147] Step 4:

[0148] The device sends integrated data to the server, including analysis results from the emotion engine, as well as the user's food preferences and health information.

[0149] Step 5:

[0150] The server receives the integrated data and stores it in a database. Based on this information, the server begins selecting dining establishments optimized for the user's emotional state.

[0151] Step 6:

[0152] The server retrieves the latest data from an external food and beverage establishment information provider and filters the establishments to match the user's emotional and health state. For example, a user who wants to relax will be shown a food and beverage establishment with a quiet and calm atmosphere.

[0153] Step 7:

[0154] The server sends a list of selected restaurants to the terminal. The terminal receives this list and displays a list of recommendations to the user based on their emotional state.

[0155] Step 8:

[0156] The user selects their preferred restaurant from the suggested options and makes a reservation. The terminal then sends this information to the server.

[0157] Step 9:

[0158] The server accesses the reservation system of the selected restaurant and checks availability in real time. If a reservation is available, it immediately confirms the reservation and generates confirmation information.

[0159] Step 10:

[0160] The server sends reservation confirmation information to the terminal, which then presents it to the user. The server considers the user's emotional state and displays an appropriate message for the reservation confirmation.

[0161] This process allows the system to provide a personalized dining experience that reflects the user's emotional state.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] In modern society, there is a demand for services that are tailored to the emotional state of individual users. However, conventional food and beverage establishment recommendation systems have failed to consider the emotional state of users and have remained limited to providing uniform services. As a result, the user experience has been insufficient, and it has been difficult to make suggestions that meet the unique needs of each user, leading to a decline in satisfaction.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes means for acquiring preference information, constraint information, and biometric information from the user; means for recognizing the user's emotional state using emotion analysis technology and generating analysis results; and means for predicting the user's mental state based on the emotion analysis results using a generative AI model. This enables the provision of detailed suggestions for dining facilities and personalized experiences tailored to the user's emotional state.

[0167] A "user" refers to a person who uses the system and is the subject who receives suggestions for restaurants, bars, and other establishments based on their emotional state and preferences.

[0168] "Preference information" refers to data that indicates a user's likes and dislikes, and is used as a criterion when suggesting meal options.

[0169] "Restriction information" refers to information that includes constraints that users need to consider when making food choices, such as allergies or dietary restrictions.

[0170] "Biometric information" refers to data about the user's health status and physical characteristics, which is acquired through external measuring devices, etc.

[0171] "Emotion analysis technology" refers to technology that uses data such as voice tone and facial expressions to identify a user's emotional state.

[0172] "Analysis results" refer to data analyzing the user's emotional state obtained through emotion analysis technology, and are useful information for providing services.

[0173] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate information about the user's mental state and appropriate action suggestions.

[0174] "Mental state" refers to the user's mental state and emotional condition, and is a factor that affects the accuracy of the suggestions.

[0175] "Usage facilities" refers to establishments such as restaurants, and is one of the options offered to users.

[0176] "Content" refers to the menus and services offered to users, and is customized according to user requests.

[0177] This invention is a system that analyzes a user's emotional state and provides personalized suggestions based on that analysis. This system mainly consists of three components: a server, a terminal, and a user.

[0178] First, the user inputs their preferences, restrictions, and biometric information through the device. The device flexibly collects this data using voice input and a touchscreen. Furthermore, the device captures the user's voice tone and facial expressions in real time through its built-in camera and microphone, and analyzes them using sentiment analysis technology. This sentiment analysis technology utilizes a generative AI model to evaluate the user's mental state from multiple perspectives.

[0179] Next, the terminal securely transmits all data, including the analysis results, to the server using encrypted communication. The server stores this information in a cloud database and performs analysis by combining it with usage facility data from external sources that are acquired periodically. The server then uses a generative AI model to suggest restaurants and menus optimized for the user's emotional state.

[0180] The suggested content is sent to the user's device and displayed in a visually easy-to-understand format. Based on this information, the user can make reservations for their desired facilities. Furthermore, features that allow users to share their emotional state and suggested facility information with other users can stimulate communication.

[0181] As a concrete example of this system, if a user enters a prompt such as, "I'm a little tired today, so please recommend a place where I can relax," the system will suggest quiet and calming cafes and restaurants. This allows users to spend comfortable time in a place that suits their emotional state at that moment.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] Users use the device to input preference information, restriction information, and biometric information. This input can be done via the device's keyboard or voice input function. The entered information is temporarily stored on the device.

[0185] Step 2:

[0186] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. Based on this captured data, it uses emotion analysis technology to recognize the user's emotional state. A generative AI model analyzes this data and outputs an analysis result indicating the user's mental state. This analysis result is stored on the device for use in subsequent processing.

[0187] Step 3:

[0188] The terminal packages the analysis results and user-entered information and sends it to the server in an encrypted format. Before transmission, the data is validated to ensure integrity, and the format is adjusted as needed.

[0189] Step 4:

[0190] The server analyzes the received data package and stores it in a cloud database. This stored data is used in combination with information about the facilities used, obtained from external sources. The server uses a generative AI model to generate suggestions that take into account the user's mental state based on the sentiment analysis results. These suggestions are used to select the most suitable dining facilities and menus for the user.

[0191] Step 5:

[0192] Suggestions generated on the server are sent to the device and displayed on the user interface. The displayed information includes a map of the facility, photos of the menu, and reviews. Users can also make reservations based on this information by tapping the screen.

[0193] Step 6:

[0194] Users can share their emotional state and suggested information with other users. This sharing takes place through the device's social networking function, stimulating communication with other users. The shared information can also be used as reference by users in similar emotional states.

[0195] (Application Example 2)

[0196] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0197] Conventional food and beverage recommendation systems fail to consider the user's emotional state, making it difficult to provide appropriate recommendations and a satisfying dining experience. Furthermore, they lack the means to support users in preparing meals in a relaxed state at home. Therefore, there is a need for a system that analyzes the user's emotional state, makes food and beverage recommendations based on that analysis, and provides support for meal preparation.

[0198] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0199] In this invention, the server includes means for acquiring the user's food and drink preferences, restrictions, and health information; means for analyzing the acquired information to select an appropriate dining facility; and means for analyzing the user's emotional state and suggesting food and drinks that correspond to that emotional state. This makes it possible to suggest the most suitable food and drinks according to the user's emotions and effectively support meal preparation at home.

[0200] A "user" refers to an individual or household that uses this system and is the entity that receives food and beverage-related suggestions.

[0201] "Food and drink preference information" refers to information that indicates the types of ingredients and dishes a user prefers, as well as their taste preferences.

[0202] "Constraint information" refers to information that indicates conditions such as allergies or dietary restrictions that a user has that prevent them from avoiding certain foods and beverages.

[0203] "Health information" refers to information about the user's physical condition and health status, and serves as basic data when providing dietary suggestions.

[0204] "Means of acquisition" refers to the methods or techniques used to gather necessary information from users.

[0205] "Means for selecting appropriate dining facilities" refers to methods or technologies that select the most suitable dining location for a user based on the information acquired.

[0206] "Means for analyzing a user's emotional state" refers to methods or technologies that analyze data such as voice, facial expressions, and behavior to identify a user's current emotions.

[0207] "Means of suggesting food and beverages according to emotional state" refers to methods or technologies for recommending appropriate food and beverages to users based on their analyzed emotional state.

[0208] The system for carrying out this invention consists of multiple components. The main elements and their respective roles are described below.

[0209] Users input information about their food and drink preferences, restrictions, and health through mobile devices or devices installed in their homes. This information is temporarily stored on the device during the initial stage and then sent to the server as needed.

[0210] The server receives information sent by the user and stores it in a cloud-based database. Based on this data, the server performs analysis and selects appropriate dining establishments that match the user's preferences. It also incorporates an emotion engine to analyze the user's emotional state using voice and facial expression data. Based on the emotional state, it creates food and beverage recommendations according to the user's needs, such as whether they want to relax or feel energized.

[0211] Furthermore, the terminal provides the user with suggestions received from the server. These suggestions include menu recommendations tailored to the user's mood and restaurants and bars that enhance specific moods. The terminal is equipped with a voice interface that can guide the user through the preparation process for their chosen food and drinks.

[0212] As an example, if a user is experiencing high stress levels after returning home from work, this system can suggest a relaxing beverage such as chamomile tea. It can also assist with preparation if needed.

[0213] An example of a prompt message would be: "How can I suggest a relaxing drink and help prepare it if the user is tired?"

[0214] These processes make it possible to provide users with a dining experience tailored to their needs and support a comfortable lifestyle.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] Users input information about their food and drink preferences, restrictions, and health via mobile devices or home devices. This information is then prepared for sequential analysis by an emotion engine. The entered data is temporarily stored on the device and organized as the user's profile.

[0218] Step 2:

[0219] The device collects user voice and facial expression data through an emotion engine. This data is used as input for analyzing emotional states. Based on changes in voice tone and facial expressions, the engine quantifies and outputs the user's emotional state, such as joy, anger, sadness, or happiness.

[0220] Step 3:

[0221] The device sends the collected information to the server. The transmitted data includes information about the user's food and drink preferences, health information, and emotional state. This data is recorded in a cloud-based database and stored for subsequent analysis.

[0222] Step 4:

[0223] The server analyzes the received data and selects appropriate dining establishments based on the user's emotional state, food and drink preferences, and health information. It also generates optimal food and drink or menu options for the user through an AI model that takes their emotional state into consideration.

[0224] Step 5:

[0225] Suggestions generated by the server are sent to the terminal. The terminal displays the suggested information to the user and provides detailed explanations and options via a voice interface. This allows the user to gain knowledge about the suggested food, beverages, and establishments.

[0226] Step 6:

[0227] The device assists the user in preparing food and beverages they have selected. It uses a voice interface to guide users through ingredient confirmation and cooking procedures. For example, it provides step-by-step instructions for making a relaxing herbal tea.

[0228] Step 7:

[0229] When needed, the device provides the ability to share information with other users. It allows users to share information about recommended food, drinks, and establishments with other users who share similar emotional states, and to actively exchange opinions through online communities.

[0230] Through these steps, it is possible to improve the comfort of daily life by providing experiences based on the user's emotions and dining needs.

[0231] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0232] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0233] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0237] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0239] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0240] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0241] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0242] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0243] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0244] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0245] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0246] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0247] This invention relates to a system that suggests the most suitable dining establishments based on a user's food preferences, restrictions, and health status, and further automates the reservation process. This system mainly consists of three components: a server, a terminal, and a user.

[0248] First, users enter their food preferences and restrictions (e.g., allergies or religious reasons), as well as their current health information, through their smartphones, tablets, or other devices. This information is temporarily stored on the device and then transmitted to the server via the network.

[0249] The server receives data sent by users and stores it in a cloud-based database. This database is used as foundational data for providing meal suggestions based on user preferences and health status, utilizing big data analytics techniques. The server also periodically obtains the latest restaurant information from external food and beverage provider data providers and matches it with user information to select appropriate restaurants.

[0250] After selection, the terminal receives a list of suggested restaurants from the server and presents it visually to the user. The terminal is also configured to display detailed information about the restaurants and food recommendations tailored to the user's health condition.

[0251] For example, if a user prefers Japanese food and has a nut allergy, the server will list restaurants that meet these conditions and offer appropriate menus based on the user's current health data (e.g., blood sugar levels and calorie expenditure). This list is sent to the user's terminal for easy review.

[0252] The server also processes reservation requests from users and checks the reservation status of the selected facility. If a reservation is available, it quickly returns that information to the terminal for the user to confirm. Furthermore, the terminal sends data from IoT devices to the server, enabling more accurate dietary suggestions based on health predictions.

[0253] This system streamlines the above-mentioned processes, making the dining-out experience more efficient and healthier for users.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user uses a terminal to input data about their food preferences, allergy information, and health status. The terminal temporarily stores this data in a local database and prepares it for transmission to the server.

[0257] Step 2:

[0258] The terminal sends user data to the server via the network. The server formats the received data, organizes it by user, and then stores it in a cloud database.

[0259] Step 3:

[0260] The server periodically retrieves the latest data from an external food and beverage establishment information provider and updates its own database. This information includes the establishment's location, menu, and business hours.

[0261] Step 4:

[0262] The server initiates a process to select dining establishments based on the user's dietary preferences, restrictions, and health information. This includes analyzing the user's health data and generating suggestions using AI algorithms.

[0263] Step 5:

[0264] The server sends the selection results to the terminal, which receives them and displays a list of suggested restaurants to the user. Detailed information and recommended menus for each restaurant are also displayed simultaneously.

[0265] Step 6:

[0266] The user selects a destination from a suggested list and completes the booking process on the device. The device then sends the user's selection to the server.

[0267] Step 7:

[0268] The server contacts the restaurant's reservation system to check availability. If a reservation is possible, it confirms the reservation and generates confirmation information.

[0269] Step 8:

[0270] The server sends reservation confirmation information to the terminal, which receives it and notifies the user. It also updates the dietary recommendations based on the presented health menu.

[0271] (Example 1)

[0272] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] In modern society, people's eating habits are becoming increasingly diverse, and their preferences and restrictions regarding food are becoming more complex. Therefore, there is a need for efficient methods to select and reserve restaurants that meet individual needs. Furthermore, suggesting meals tailored to individual health conditions is also important, and a system that comprehensively addresses these needs is required.

[0274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0275] In this invention, the server includes means for acquiring food preference attributes, restriction attributes, and health attributes from the user, means for storing the attributes in a storage area on the cloud, and means for periodically acquiring facility information from an external provider database. This enables the rapid selection and reservation of dining facilities that meet the user's needs.

[0276] A "user" refers to an individual who uses the system to provide information about their dietary preferences, restrictions, and health status.

[0277] "Food preference attributes" refer to information about the specific foods, drinks, types of cuisine, and styles that a user prefers.

[0278] "Restriction attributes" refer to information about food and beverages that users should avoid or are prohibited from eating or drinking.

[0279] The "health attribute" refers to information related to the user's current health status, and appropriate dietary suggestions are made based on this information.

[0280] The "cloud storage area" is a virtual storage space for storing and accessing data via the Internet.

[0281] The "provider database" is an information storage system for obtaining and managing food service facility information from external sources.

[0282] The "mathematical analysis technology" refers to mathematical methods and algorithms used for analyzing and processing data.

[0283] The "detection device" is an external hardware device that monitors the user's health status and transmits the data to the system.

[0284] The "repository" is a data storage area for structuring external information and updating it regularly.

[0285] This invention is a system that proposes an optimal food service facility based on the user's food preferences, restrictions, and health status, and automates the reservation process. The system mainly consists of three elements: a server, a terminal, and a user.

[0286] The user uses a terminal such as a smartphone or tablet to input food preferences, restriction conditions (such as allergies or religious background), and health information. This information is temporarily stored on the terminal and transmitted to the server through secure communication using SSL / TLS.

[0287] The server leverages cloud technology to securely store acquired user information and perform big data analysis. Here, it structures the information using database management systems such as MySQL and MongoDB, and performs data analysis using analytical tools like Python's Pandas library and Google Cloud BigQuery. Furthermore, it collects data from external food and beverage establishments via APIs and records it in the provider database.

[0288] Based on the analysis results, the server selects restaurants and bars that meet the user's needs and sends a list to the device. This list includes detailed information about the establishments (address, opening hours, menu options, etc.). Using a framework such as React Native, this information is presented to the user visually on the device.

[0289] For example, if a user prefers Japanese food and wants to avoid nuts, the server will list restaurants that meet these conditions and can provide menus that take into account the user's current blood sugar levels and calorie expenditure. This list is then transferred to the user's terminal, allowing them to easily review and select from it.

[0290] Furthermore, during the reservation process, the server processes the user's request and checks the reservation status of the selected facility. If a reservation is possible, the system quickly returns the result to the terminal and notifies the user. In addition, the system can make new suggestions using prompt messages generated by an AI model.

[0291] Example of a prompt:

[0292] 1. "Using the user's food preferences (e.g., Japanese cuisine), please propose a restaurant that takes nut allergies into consideration."

[0293] 2. "Find restaurants that offer calorie-restricted menus based on the user's health data."

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] Users input their food preferences, restrictions, and health status using devices such as smartphones and tablets. The entered data is temporarily stored on the device. Specifically, users enter information using the application's forms, and this data is structured in JSON format. The input data includes food preferences (e.g., Japanese food), restrictions (e.g., nut allergy), and health information (e.g., blood sugar levels). The structured data is then ready to be sent to the server as output.

[0297] Step 2:

[0298] The terminal securely transmits user data to the server using the SSL / TLS protocol. The previously structured data is used as input. The data is received by the server, its integrity is checked, and then it is stored in a cloud database. As output, the data is securely stored in the cloud database.

[0299] Step 3:

[0300] The server retrieves user data from a cloud-based database and performs data analysis using the Python Pandas library. Inputs include user preferences, restrictions, and health data. Specifically, it filters the user data to extract information on restaurants and bars that match the specified criteria. The output is a list of restaurants and bars that meet the criteria.

[0301] Step 4:

[0302] The server retrieves the latest information on food and beverage establishments from an external provider database. It periodically accesses the external database using an API to update the data. The input is establishment information from the external provider. The updated establishment information is stored in a cloud database as output.

[0303] Step 5:

[0304] The terminal presents the list of food and beverage facilities received from the server to the user via the user interface. The list is visually displayed by the React Native framework. There is a facility list from the server as input. As output, the user can view the facility information and make selections.

[0305] Step 6:

[0306] Based on the user's facility selection, the server checks the reservation status of the selected facility. It inquires about the reservation availability using the facility reservation API. The input is the facility data selected by the user. As output, a reservation availability result is generated and sent to the terminal.

[0307] Step 7:

[0308] The terminal receives the reservation information from the server and displays a confirmation screen to the user. When the user confirms, the final reservation information is determined. The input is the reservation result from the server. As output, a screen where the reservation is confirmed and the user can check the reservation status is displayed.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In modern society, users often face various restrictions regarding their health conditions and food preferences. However, there are limited systems that can select the optimal food providers and food and beverage facilities considering such information and enable smooth utilization. Therefore, there is a need to create an environment where users can easily make food choices that meet their requirements and where reservations and orders can be automatically placed.

[0312] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes means for acquiring information on food preferences, restrictions, and health information from the user; means for analyzing the acquired information and selecting an appropriate restaurant or food provider; and means for automatically coordinating reservations or orders at the selected restaurant or food provider. This allows users to choose meals according to their health condition and food preferences, and the automation of reservations and orders makes the service easier to use.

[0314] A "user" refers to an individual or organization that utilizes this system and is the entity that provides information on food preferences, restrictions, and health.

[0315] "Food preference information" refers to information about the types of dishes, seasonings, ingredients, etc. that the user prefers.

[0316] "Restriction information" refers to conditions that users must consider when making food choices, such as allergies or religious restrictions.

[0317] "Health information" refers to data about the user's physical health status, including parameters such as blood glucose levels and calorie consumption.

[0318] "Food and beverage establishments" refers to facilities that serve meals, including restaurants and cafes.

[0319] A "food provider" refers to a company or organization that provides services such as cooking and serving meals.

[0320] "Means for automatically coordinating reservations or orders" refers to a function in which the system automatically makes reservations or orders meals from restaurants or food providers selected by the user.

[0321] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to suggest the optimal meal choices based on user data.

[0322] An "external sensor device" is a hardware device used to acquire health status data from the user.

[0323] An "external database" is a database used by a system to store information about a food establishment or food provider and to access that information.

[0324] The system for realizing this invention collects the user's food preferences, dietary restrictions, and health information, and has the function of automatically selecting an appropriate restaurant or food provider and making a reservation or order.

[0325] The system starts when a user inputs data about their food preferences and health status using a smartphone or tablet. This information is temporarily stored on the device and transmitted to a server via the internet. The server stores this information in a cloud-based database and, by incorporating big data analytics technology, determines which restaurant or food provider is best suited to the user's needs. Then, using a generative AI model, it optimizes specific meal menus based on the user's health data.

[0326] The server also periodically retrieves data from external restaurants and food providers and compares it with user information. This process enables the system to make appropriate suggestions to users. Selected options are resent to devices such as smartphones and tablets and displayed for the user to visually confirm. Users select their preferred options from the suggested choices, and the system automatically makes the reservation or order.

[0327] For example, if a user prefers a vegan diet and is gluten-intolerant, the server will list restaurants or food providers that meet these criteria and send the list to the user's terminal. Once the user selects a restaurant, the system will automatically complete the reservation process.

[0328] As an example of a prompt, data can be sent to the generating AI model in the form of, "Please suggest the optimal meal plan based on the user's restrictions and health data. Example: 'Suggest a vegan and gluten-free meal plan'."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] Users use their smartphones or tablets to input their dietary preferences, restrictions, and health information. This input information is temporarily stored on the device. This input includes allergies, dietary preferences (e.g., vegan, low-carb), and recent health data. The device organizes this information as structured data and prepares it for transmission to the server.

[0332] Step 2:

[0333] The device sends user data to the server. The transmitted data is analyzed on the server. The server stores the data in a cloud-based database and uses big data analytics technology to select restaurants or food providers that match the user's preferences. The analysis matches user data with information about restaurants and extracts options that meet the user's criteria.

[0334] Step 3:

[0335] The server uses a generative AI model to optimize meal plans based on the user's health data. This process takes the user's health parameters and past meal history as input, and runs a model that generates an optimal meal plan. As a result, a proposed menu is generated and sent from the server to the terminal.

[0336] Step 4:

[0337] The server periodically retrieves and updates information on restaurants and food providers from external data providers. This input includes information such as the establishment's menu, reputation, and location, and the database is updated on the server side to reflect the latest information.

[0338] Step 5:

[0339] The device displays a list of restaurants or food service providers and their menus, received from the server, to the user. This output allows the user to view visual information on their device. The user can tap on a selected restaurant or menu item from the suggestion list to view more detailed information.

[0340] Step 6:

[0341] When a user selects a desired restaurant or food provider, the terminal transmits that information to the server. The server automatically completes the reservation or order with the selected restaurant or provider. This process involves coordinating with the restaurant's reservation system to check seating availability and menu stock before confirming the reservation or order.

[0342] Step 7:

[0343] This system also provides a function for users to share applied information about facilities and meal menus. The goal is to add user feedback and ratings to the database and enable the sharing of information with other users.

[0344] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0345] This invention is a food and beverage facility proposal system that incorporates the user's emotional state, and aims to improve the user experience using emotion recognition technology. This system mainly consists of four components: a server, a terminal, a user, and an emotion engine.

[0346] First, the user inputs information such as their food preferences and restrictions, health information, and current emotional state through their terminal device. This emotional information is then analyzed by an emotion engine. The emotion engine recognizes the user's emotional state using various data, including voice tone, facial expressions, and the speed of the user's selection actions. This information is temporarily stored on the terminal before being sent to the server.

[0347] The server receives data from users and stores it in a cloud database. The server also periodically retrieves information from external food and beverage establishment information providers and analyzes it in conjunction with user data. Based on the analysis results from the emotion engine, the server selects food and beverage establishments that take into account the user's stress level and satisfaction predictions.

[0348] To provide meal suggestions tailored to the user's emotional state, the device receives suggestions from the server and presents them to the user. These suggestions include menu recommendations that match the user's mood and options for dining establishments that can enhance a particular mood. For example, if the user is seeking relaxation, a restaurant with a quiet atmosphere will be recommended.

[0349] Furthermore, the system allows users to share emotions and corresponding facility information with other users to support information sharing among them. This feature aims to promote communication among users with similar emotional states and provide a space for empathy.

[0350] Furthermore, the reservation process can also be adapted to the user's emotional state. If the emotion engine detects a high-stress state, the reservation confirmation and modification procedures are designed to proceed more smoothly.

[0351] Through the processes described above, this system enables personalized service tailored to the user's emotions, providing a richer dining experience.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] The user uses a device to input data about their food preferences, dietary restrictions, health status, and emotional state. The device temporarily stores the entered data in a local database.

[0355] Step 2:

[0356] The device activates an emotion engine and collects data such as the user's voice tone, facial expressions, and operation speed through sensors and cameras. This data is used to analyze the user's emotional state.

[0357] Step 3:

[0358] The emotion engine analyzes the collected data to identify the user's emotional state. The results are returned to the device in the form of numerical values ​​representing stress levels and emotional tendencies.

[0359] Step 4:

[0360] The device sends integrated data to the server, including analysis results from the emotion engine, as well as the user's food preferences and health information.

[0361] Step 5:

[0362] The server receives the integrated data and stores it in a database. Based on this information, the server begins selecting dining establishments optimized for the user's emotional state.

[0363] Step 6:

[0364] The server retrieves the latest data from an external food and beverage establishment information provider and filters the establishments to match the user's emotional and health state. For example, a user who wants to relax will be presented with a quiet and calming food and beverage establishment.

[0365] Step 7:

[0366] The server sends a list of selected restaurants to the terminal. The terminal receives this list and displays a list of recommendations to the user based on their emotional state.

[0367] Step 8:

[0368] The user selects their preferred restaurant from the suggested options and makes a reservation. The terminal then sends this information to the server.

[0369] Step 9:

[0370] The server accesses the reservation system of the selected restaurant and checks availability in real time. If a reservation is available, it immediately confirms the reservation and generates confirmation information.

[0371] Step 10:

[0372] The server sends reservation confirmation information to the terminal, which then presents it to the user. The server considers the user's emotional state and displays an appropriate message for the reservation confirmation.

[0373] This process allows the system to provide a personalized dining experience that reflects the user's emotional state.

[0374] (Example 2)

[0375] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0376] In modern society, there is a demand for services that are tailored to the emotional state of individual users. However, conventional food and beverage establishment recommendation systems have failed to consider the emotional state of users and have remained limited to providing uniform services. As a result, the user experience has been insufficient, and it has been difficult to make suggestions that meet the unique needs of each user, leading to a decline in satisfaction.

[0377] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0378] In this invention, the server includes means for acquiring preference information, constraint information, and biometric information from the user; means for recognizing the user's emotional state using emotion analysis technology and generating analysis results; and means for predicting the user's mental state based on the emotion analysis results using a generative AI model. This enables the provision of detailed suggestions for dining facilities and personalized experiences tailored to the user's emotional state.

[0379] A "user" refers to a person who uses the system and is the subject who receives suggestions for restaurants, bars, and other establishments based on their emotional state and preferences.

[0380] "Preference information" refers to data that indicates a user's likes and dislikes, and is used as a criterion when suggesting meal options.

[0381] "Restriction information" refers to information that includes constraints that users need to consider when making food choices, such as allergies or dietary restrictions.

[0382] "Biometric information" refers to data about the user's health status and physical characteristics, which is acquired through external measuring devices, etc.

[0383] "Emotion analysis technology" refers to technology that uses data such as voice tone and facial expressions to identify a user's emotional state.

[0384] "Analysis results" refer to data analyzing the user's emotional state obtained through emotion analysis technology, and are useful information for providing services.

[0385] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate information about the user's mental state and appropriate action suggestions.

[0386] "Mental state" refers to the user's mental state and emotional condition, and is a factor that affects the accuracy of the suggestions.

[0387] "Usage facilities" refers to establishments such as restaurants, and is one of the options offered to users.

[0388] "Content" refers to the menus and services offered to users, and is customized according to user requests.

[0389] This invention is a system that analyzes a user's emotional state and provides personalized suggestions based on that analysis. This system mainly consists of three components: a server, a terminal, and a user.

[0390] First, the user inputs their preferences, restrictions, and biometric information through the device. The device flexibly collects this data using voice input and a touchscreen. Furthermore, the device captures the user's voice tone and facial expressions in real time through its built-in camera and microphone, and analyzes them using sentiment analysis technology. This sentiment analysis technology utilizes a generative AI model to evaluate the user's mental state from multiple perspectives.

[0391] Next, the terminal securely transmits all data, including the analysis results, to the server using encrypted communication. The server stores this information in a cloud database and performs analysis by combining it with usage facility data from external sources that are acquired periodically. The server then uses a generative AI model to suggest restaurants and menus optimized for the user's emotional state.

[0392] The suggested content is sent to the user's device and displayed in a visually easy-to-understand format. Based on this information, the user can make reservations for their desired facilities. Furthermore, features that allow users to share their emotional state and suggested facility information with other users can stimulate communication.

[0393] As a concrete example of this system, if a user enters a prompt such as, "I'm a little tired today, so please recommend a place where I can relax," the system will suggest quiet and calming cafes and restaurants. This allows users to spend comfortable time in a place that suits their emotional state at that moment.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] Users use the device to input preference information, restriction information, and biometric information. This input can be done via the device's keyboard or voice input function. The entered information is temporarily stored on the device.

[0397] Step 2:

[0398] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. Based on this captured data, it uses emotion analysis technology to recognize the user's emotional state. A generative AI model analyzes this data and outputs an analysis result indicating the user's mental state. This analysis result is stored on the device for use in subsequent processing.

[0399] Step 3:

[0400] The terminal packages the analysis results and user-entered information and sends it to the server in an encrypted format. Before transmission, the data is validated to ensure integrity, and the format is adjusted as needed.

[0401] Step 4:

[0402] The server analyzes the received data package and stores it in a cloud database. This stored data is used in combination with information about the facilities used, obtained from external sources. The server uses a generative AI model to generate suggestions that take into account the user's mental state based on the sentiment analysis results. These suggestions are used to select the most suitable dining facilities and menus for the user.

[0403] Step 5:

[0404] Suggestions generated on the server are sent to the device and displayed on the user interface. The displayed information includes a map of the facility, photos of the menu, and reviews. Users can also make reservations based on this information by tapping the screen.

[0405] Step 6:

[0406] Users can share their emotional state and suggested information with other users. This sharing takes place through the device's social networking function, stimulating communication with other users. The shared information can also be used as reference by users with similar emotional states.

[0407] (Application Example 2)

[0408] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0409] Conventional food and beverage recommendation systems fail to consider the user's emotional state, making it difficult to provide appropriate recommendations and a satisfying dining experience. Furthermore, they lack the means to support users in preparing meals in a relaxed state at home. Therefore, there is a need for a system that analyzes the user's emotional state, makes food and beverage recommendations based on that analysis, and provides support for meal preparation.

[0410] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0411] In this invention, the server includes means for acquiring the user's food and drink preferences, restrictions, and health information; means for analyzing the acquired information to select an appropriate dining facility; and means for analyzing the user's emotional state and suggesting food and drinks that correspond to that emotional state. This makes it possible to suggest the most suitable food and drinks according to the user's emotions and effectively support meal preparation at home.

[0412] A "user" refers to an individual or household that uses this system and is the entity that receives food and beverage-related suggestions.

[0413] "Food and drink preference information" refers to information that indicates the types of ingredients and dishes a user prefers, as well as their taste preferences.

[0414] "Constraint information" refers to information that indicates conditions such as allergies or dietary restrictions that a user has that prevent them from avoiding certain foods and beverages.

[0415] "Health information" refers to information about the user's physical condition and health status, and serves as basic data when providing dietary suggestions.

[0416] "Means of acquisition" refers to the methods or techniques used to gather necessary information from users.

[0417] "Means for selecting appropriate dining facilities" refers to methods or technologies that select the most suitable dining location for a user based on the information acquired.

[0418] "Means for analyzing a user's emotional state" refers to methods or technologies that analyze data such as voice, facial expressions, and behavior to identify a user's current emotions.

[0419] "Means of suggesting food and beverages according to emotional state" refers to methods or technologies for recommending appropriate food and beverages to users based on their analyzed emotional state.

[0420] The system for carrying out this invention consists of multiple components. The main elements and their respective roles are described below.

[0421] Users input information about their food and drink preferences, restrictions, and health through mobile devices or devices installed in their homes. This information is temporarily stored on the device during the initial stage and then sent to the server as needed.

[0422] The server receives information sent by the user and stores it in a cloud-based database. Based on this data, the server performs analysis and selects appropriate dining establishments that match the user's preferences. It also incorporates an emotion engine to analyze the user's emotional state using voice and facial expression data. Based on the emotional state, it creates food and beverage recommendations according to the user's needs, such as whether they want to relax or feel energized.

[0423] Furthermore, the terminal provides the user with suggestions received from the server. These suggestions include menu recommendations tailored to the user's mood and restaurants and bars that enhance specific moods. The terminal is equipped with a voice interface that can guide the user through the preparation process for the food and drinks they have selected.

[0424] As an example, if a user is experiencing high stress levels after returning home from work, this system can suggest a relaxing beverage such as chamomile tea. It can also assist with preparation if needed.

[0425] An example of a prompt message would be: "How can I suggest a relaxing drink and help prepare it if the user is tired?"

[0426] These processes make it possible to provide users with a dining experience tailored to their needs and support a comfortable lifestyle.

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] Users input information about their food and drink preferences, restrictions, and health via mobile devices or home devices. This information is then prepared for sequential analysis by an emotion engine. The entered data is temporarily stored on the device and organized as the user's profile.

[0430] Step 2:

[0431] The device collects user voice and facial expression data through an emotion engine. This data is used as input for analyzing emotional states. Based on changes in voice tone and facial expressions, the engine quantifies and outputs the user's emotional state, such as joy, anger, sadness, or happiness.

[0432] Step 3:

[0433] The device sends the collected information to the server. The transmitted data includes information about the user's food and drink preferences, health information, and emotional state. This data is recorded in a cloud-based database and stored for subsequent analysis.

[0434] Step 4:

[0435] The server analyzes the received data and selects appropriate dining establishments based on the user's emotional state, food and drink preferences, and health information. It also generates optimal food and drink or menu options for the user through an AI model that takes their emotional state into consideration.

[0436] Step 5:

[0437] Suggestions generated by the server are sent to the terminal. The terminal displays the suggested information to the user and provides detailed explanations and options via a voice interface. This allows the user to gain knowledge about the suggested food, beverages, and establishments.

[0438] Step 6:

[0439] The device assists the user in preparing food and beverages of their choice. It uses a voice interface to guide users through ingredient confirmation and cooking procedures. For example, it provides step-by-step instructions for making a relaxing herbal tea.

[0440] Step 7:

[0441] When needed, the device provides the ability to share information with other users. It allows users to share information about recommended food, drinks, and establishments with other users who share similar emotional states, and to actively exchange opinions through online communities.

[0442] Through these steps, it is possible to improve the comfort of daily life by providing experiences based on the user's emotions and dining needs.

[0443] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0444] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0445] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0449] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0450] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0451] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0452] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0453] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0454] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0455] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0456] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0457] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0458] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0459] This invention relates to a system that suggests the most suitable dining establishments based on a user's food preferences, restrictions, and health status, and further automates the reservation process. This system mainly consists of three components: a server, a terminal, and a user.

[0460] First, users enter their food preferences and restrictions (e.g., allergies or religious reasons), as well as their current health information, through their smartphones, tablets, or other devices. This information is temporarily stored on the device and then transmitted to the server via the network.

[0461] The server receives data sent by users and stores it in a cloud-based database. This database is used as foundational data for providing meal suggestions based on user preferences and health status, utilizing big data analytics techniques. The server also periodically obtains the latest restaurant information from external food and beverage provider data providers and matches it with user information to select appropriate restaurants.

[0462] After selection, the terminal receives a list of suggested restaurants from the server and presents it visually to the user. The terminal is also configured to display detailed information about the restaurants and food recommendations tailored to the user's health condition.

[0463] For example, if a user prefers Japanese food and has a nut allergy, the server will list restaurants that meet these conditions and offer appropriate menus based on the user's current health data (e.g., blood sugar levels and calorie expenditure). This list is sent to the user's terminal for easy review.

[0464] The server also processes reservation requests from users and checks the reservation status of the selected facility. If a reservation is available, it quickly returns that information to the terminal for the user to confirm. Furthermore, the terminal sends data from IoT devices to the server, enabling more accurate dietary suggestions based on health predictions.

[0465] This system streamlines the above-mentioned processes, making the dining-out experience more efficient and healthier for users.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] The user uses a terminal to input data about their food preferences, allergy information, and health status. The terminal temporarily stores this data in a local database and prepares it for transmission to the server.

[0469] Step 2:

[0470] The terminal sends user data to the server via the network. The server formats the received data, organizes it by user, and then stores it in a cloud database.

[0471] Step 3:

[0472] The server periodically retrieves the latest data from an external food and beverage establishment information provider and updates its own database. This information includes the establishment's location, menu, and business hours.

[0473] Step 4:

[0474] The server initiates a process to select dining establishments based on the user's dietary preferences, restrictions, and health information. This includes analyzing the user's health data and generating suggestions using AI algorithms.

[0475] Step 5:

[0476] The server sends the selection results to the terminal, which receives them and displays a list of suggested restaurants to the user. Detailed information and recommended menus for each restaurant are also displayed simultaneously.

[0477] Step 6:

[0478] The user selects a destination from a suggested list and completes the booking process on the device. The device then sends the user's selection to the server.

[0479] Step 7:

[0480] The server contacts the restaurant's reservation system to check availability. If a reservation is possible, it confirms the reservation and generates confirmation information.

[0481] Step 8:

[0482] The server sends reservation confirmation information to the terminal, which receives it and notifies the user. It also updates the dietary recommendations based on the presented health menu.

[0483] (Example 1)

[0484] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] In modern society, people's eating habits are becoming increasingly diverse, and their preferences and restrictions regarding food are becoming more complex. Therefore, there is a need for efficient methods to select and reserve restaurants that meet individual needs. Furthermore, suggesting meals tailored to individual health conditions is also important, and a system that comprehensively addresses these needs is required.

[0486] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0487] In this invention, the server includes means for acquiring food preference attributes, restriction attributes, and health attributes from the user, means for storing the attributes in a storage area on the cloud, and means for periodically acquiring facility information from an external provider database. This enables the rapid selection and reservation of dining facilities that meet the user's needs.

[0488] A "user" refers to an individual who uses the system to provide information about their dietary preferences, restrictions, and health status.

[0489] "Food preference attributes" refer to information about the specific foods, drinks, types of cuisine, and styles that a user prefers.

[0490] "Restriction attributes" refer to information about food and beverages that users should avoid or are prohibited from eating or drinking.

[0491] "Health attributes" refer to information related to the user's current health status, and appropriate dietary suggestions are provided based on this information.

[0492] "Cloud storage" refers to virtual storage space for storing and accessing data via the internet.

[0493] A "provider database" is an information storage system for acquiring and managing information about food and beverage establishments from external sources.

[0494] "Mathematical analysis techniques" refer to mathematical methods and algorithms used for analyzing and processing data.

[0495] A "detection device" is an external hardware device that monitors the user's health status and transmits that data to the system.

[0496] A "storage" is a data storage area for structuring external information and updating it regularly.

[0497] This invention is a system that suggests the most suitable dining establishments based on the user's food preferences, restrictions, and health condition, and automates the reservation process. The system mainly consists of three elements: a server, a terminal, and a user.

[0498] Users use smartphones or tablets to input their food preferences, dietary restrictions (such as allergies or religious background), and health information. This information is temporarily stored on the device and then transmitted to the server via secure communication using SSL / TLS.

[0499] The server leverages cloud technology to securely store acquired user information and perform big data analysis. Here, it structures the information using database management systems such as MySQL and MongoDB, and performs data analysis using analytical tools like Python's Pandas library and Google Cloud BigQuery. Furthermore, it collects data from external food and beverage establishments via APIs and records it in the provider database.

[0500] Based on the analysis results, the server selects restaurants and bars that meet the user's needs and sends a list to the device. This list includes detailed information about the establishments (address, opening hours, menu options, etc.). Using a framework such as React Native, this information is presented to the user visually on the device.

[0501] For example, if a user prefers Japanese food and wants to avoid nuts, the server will list restaurants that meet these conditions and can provide menus that take into account the user's current blood sugar levels and calorie expenditure. This list is then transferred to the user's terminal, allowing them to easily review and select from it.

[0502] Furthermore, during the reservation process, the server processes the user's request and checks the reservation status of the selected facility. If a reservation is possible, the system quickly returns the result to the terminal and notifies the user. In addition, the system can make new suggestions using prompt messages generated by an AI model.

[0503] Example of a prompt:

[0504] 1. "Using the user's food preferences (e.g., Japanese cuisine), please propose a restaurant that takes nut allergies into consideration."

[0505] 2. "Find restaurants that offer calorie-restricted menus based on the user's health data."

[0506] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0507] Step 1:

[0508] Users input their food preferences, restrictions, and health status using devices such as smartphones and tablets. The entered data is temporarily stored on the device. Specifically, users enter information using the application's forms, and this data is structured in JSON format. The input data includes food preferences (e.g., Japanese food), restrictions (e.g., nut allergy), and health information (e.g., blood sugar levels). The structured data is then ready to be sent to the server as output.

[0509] Step 2:

[0510] The terminal securely transmits user data to the server using the SSL / TLS protocol. The previously structured data is used as input. The data is received by the server, its integrity is checked, and then it is stored in a cloud database. As output, the data is securely stored in the cloud database.

[0511] Step 3:

[0512] The server retrieves user data from a cloud-based database and performs data analysis using the Python Pandas library. Inputs include user preferences, restrictions, and health data. Specifically, it filters the user data to extract information on restaurants and bars that match the specified criteria. The output is a list of restaurants and bars that meet the criteria.

[0513] Step 4:

[0514] The server retrieves the latest information on food and beverage establishments from an external provider database. It periodically accesses the external database using an API to update the data. The input is establishment information from the external provider. The updated establishment information is stored in a cloud database as output.

[0515] Step 5:

[0516] The terminal presents the user with a list of restaurants and bars received from the server via a user interface. The list is visually displayed using the React Native framework. The input is a list of establishments from the server. The output allows the user to view and select establishment information.

[0517] Step 6:

[0518] The server checks the reservation status of the selected facility based on the user's facility selection. It queries the availability of the facility using the facility reservation API. The input is the facility data selected by the user. The output is the availability result and is sent to the terminal.

[0519] Step 7:

[0520] The terminal receives reservation information from the server and displays a confirmation screen to the user. Once the user confirms, the final reservation information is finalized. The input is the reservation result from the server. The output displays a screen confirming the reservation and allowing the user to check the reservation status.

[0521] (Application Example 1)

[0522] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In modern society, users often face various constraints related to their health status and food preferences. However, there are limited systems that take this information into consideration to select the most suitable food providers and restaurants and enable smooth usage. Therefore, there is a need to develop an environment where users can easily select meals that meet their requirements and where reservations and orders can be made automatically.

[0524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0525] In this invention, the server includes means for acquiring information on food preferences, restrictions, and health information from the user; means for analyzing the acquired information and selecting an appropriate restaurant or food provider; and means for automatically coordinating reservations or orders at the selected restaurant or food provider. This allows users to choose meals according to their health condition and food preferences, and the automation of reservations and orders makes the service easier to use.

[0526] A "user" refers to an individual or organization that utilizes this system and is the entity that provides information on food preferences, restrictions, and health.

[0527] "Food preference information" refers to information about the types of dishes, seasonings, ingredients, etc. that the user prefers.

[0528] "Restriction information" refers to conditions that users must consider when making food choices, such as allergies or religious restrictions.

[0529] "Health information" refers to data about the user's physical health status, including parameters such as blood glucose levels and calorie consumption.

[0530] "Food and beverage establishments" refers to facilities that serve meals, including restaurants and cafes.

[0531] A "food provider" refers to a company or organization that provides services such as cooking and serving meals.

[0532] "Means for automatically coordinating reservations or orders" refers to a function in which the system automatically makes reservations or orders meals from restaurants or food providers selected by the user.

[0533] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to suggest the optimal meal choices based on user data.

[0534] An "external sensor device" is a hardware device used to acquire health status data from the user.

[0535] An "external database" is a database used by a system to store information about a food establishment or food provider and to access that information.

[0536] The system for realizing this invention collects the user's food preferences, dietary restrictions, and health information, and has the function of automatically selecting an appropriate restaurant or food provider and making a reservation or order.

[0537] The system starts when a user inputs data about their food preferences and health status using a smartphone or tablet. This information is temporarily stored on the device and transmitted to a server via the internet. The server stores this information in a cloud-based database and, by incorporating big data analytics technology, determines which restaurant or food provider is best suited to the user's needs. Then, using a generative AI model, it optimizes specific meal menus based on the user's health data.

[0538] The server also periodically retrieves data from external restaurants and food providers and compares it with user information. This process enables the system to make appropriate suggestions to users. Selected options are resent to devices such as smartphones and tablets and displayed for the user to visually confirm. Users select their preferred options from the suggested choices, and the system automatically makes the reservation or order.

[0539] For example, if a user prefers a vegan diet and is gluten-intolerant, the server will list restaurants or food providers that meet these criteria and send the list to the user's terminal. Once the user selects a restaurant, the system will automatically complete the reservation process.

[0540] As an example of a prompt, data can be sent to the generating AI model in the form of, "Please suggest the optimal meal plan based on the user's restrictions and health data. Example: 'Suggest a vegan and gluten-free meal plan'."

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] Users use their smartphones or tablets to input their dietary preferences, restrictions, and health information. This input information is temporarily stored on the device. This input includes allergies, dietary preferences (e.g., vegan, low-carb), and recent health data. The device organizes this information as structured data and prepares it for transmission to the server.

[0544] Step 2:

[0545] The device sends user data to the server. The transmitted data is analyzed on the server. The server stores the data in a cloud-based database and uses big data analytics technology to select restaurants or food providers that match the user's preferences. The analysis matches user data with information about restaurants and extracts options that meet the user's criteria.

[0546] Step 3:

[0547] The server uses a generative AI model to optimize meal plans based on the user's health data. This process takes the user's health parameters and past meal history as input, and runs a model that generates an optimal meal plan. As a result, a proposed menu is generated and sent from the server to the terminal.

[0548] Step 4:

[0549] The server periodically retrieves and updates information on restaurants and food providers from external data providers. This input includes information such as the establishment's menu, reputation, and location, and the database is updated on the server side to reflect the latest information.

[0550] Step 5:

[0551] The device displays a list of restaurants or food service providers and their menus, received from the server, to the user. This output allows the user to view visual information on their device. The user can tap on a selected restaurant or menu item from the suggestion list to view more detailed information.

[0552] Step 6:

[0553] When a user selects a desired restaurant or food provider, the terminal transmits that information to the server. The server automatically completes the reservation or order with the selected restaurant or provider. This process involves coordinating with the restaurant's reservation system to check seating availability and menu stock before confirming the reservation or order.

[0554] Step 7:

[0555] This system also provides a function for users to share applied information about facilities and meal menus. The goal is to add user feedback and ratings to the database and enable the sharing of information with other users.

[0556] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0557] This invention is a food and beverage facility proposal system that incorporates the user's emotional state, and aims to improve the user experience using emotion recognition technology. This system mainly consists of four components: a server, a terminal, a user, and an emotion engine.

[0558] First, the user inputs information such as their food preferences and restrictions, health information, and current emotional state through their terminal device. This emotional information is then analyzed by an emotion engine. The emotion engine recognizes the user's emotional state using various data, including voice tone, facial expressions, and the speed of the user's selection actions. This information is temporarily stored on the terminal before being sent to the server.

[0559] The server receives data from users and stores it in a cloud database. The server also periodically retrieves information from external food and beverage establishment information providers and analyzes it in conjunction with user data. Based on the analysis results from the emotion engine, the server selects food and beverage establishments that take into account the user's stress level and satisfaction predictions.

[0560] To provide meal suggestions tailored to the user's emotional state, the device receives suggestions from the server and presents them to the user. These suggestions include menu recommendations that match the user's mood and options for dining establishments that can enhance a particular mood. For example, if the user is seeking relaxation, a restaurant with a quiet atmosphere will be recommended.

[0561] Furthermore, the system allows users to share emotions and corresponding facility information with other users to support information sharing among them. This feature aims to promote communication among users with similar emotional states and provide a space for empathy.

[0562] Furthermore, the reservation process can also be adapted to the user's emotional state. If the emotion engine detects a high-stress state, the reservation confirmation and modification procedures are designed to proceed more smoothly.

[0563] Through the processes described above, this system enables personalized service tailored to the user's emotions, providing a richer dining experience.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] The user uses a device to input data about their food preferences, dietary restrictions, health status, and emotional state. The device temporarily stores the entered data in a local database.

[0567] Step 2:

[0568] The device activates an emotion engine and collects data such as the user's voice tone, facial expressions, and operation speed through sensors and cameras. This data is used to analyze the user's emotional state.

[0569] Step 3:

[0570] The emotion engine analyzes the collected data to identify the user's emotional state. The results are returned to the device in the form of numerical values ​​representing stress levels and emotional tendencies.

[0571] Step 4:

[0572] The device sends integrated data to the server, including analysis results from the emotion engine, as well as the user's food preferences and health information.

[0573] Step 5:

[0574] The server receives the integrated data and stores it in a database. Based on this information, the server begins selecting dining establishments optimized for the user's emotional state.

[0575] Step 6:

[0576] The server retrieves the latest data from an external food and beverage establishment information provider and filters the establishments to match the user's emotional and health state. For example, a user who wants to relax will be presented with a quiet and calming food and beverage establishment.

[0577] Step 7:

[0578] The server sends a list of selected restaurants to the terminal. The terminal receives this list and displays a list of recommendations to the user based on their emotional state.

[0579] Step 8:

[0580] The user selects their preferred restaurant from the suggested options and makes a reservation. The terminal then sends this information to the server.

[0581] Step 9:

[0582] The server accesses the reservation system of the selected restaurant and checks availability in real time. If a reservation is available, it immediately confirms the reservation and generates confirmation information.

[0583] Step 10:

[0584] The server sends reservation confirmation information to the terminal, which then presents it to the user. The server considers the user's emotional state and displays an appropriate message for the reservation confirmation.

[0585] This process allows the system to provide a personalized dining experience that reflects the user's emotional state.

[0586] (Example 2)

[0587] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0588] In modern society, there is a demand for services that are tailored to the emotional state of individual users. However, conventional food and beverage establishment recommendation systems have failed to consider the emotional state of users and have remained limited to providing uniform services. As a result, the user experience has been insufficient, and it has been difficult to make suggestions that meet the unique needs of each user, leading to a decline in satisfaction.

[0589] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0590] In this invention, the server includes means for acquiring preference information, constraint information, and biometric information from the user; means for recognizing the user's emotional state using emotion analysis technology and generating analysis results; and means for predicting the user's mental state based on the emotion analysis results using a generative AI model. This enables the provision of detailed suggestions for dining facilities and personalized experiences tailored to the user's emotional state.

[0591] A "user" refers to a person who uses the system and is the subject who receives suggestions for restaurants, bars, and other establishments based on their emotional state and preferences.

[0592] "Preference information" refers to data that indicates a user's likes and dislikes, and is used as a criterion when suggesting meal options.

[0593] "Restriction information" refers to information that includes constraints that users need to consider when making food choices, such as allergies or dietary restrictions.

[0594] "Biometric information" refers to data about the user's health status and physical characteristics, which is acquired through external measuring devices, etc.

[0595] "Emotion analysis technology" refers to technology that uses data such as voice tone and facial expressions to identify a user's emotional state.

[0596] "Analysis results" refer to data analyzing the user's emotional state obtained through emotion analysis technology, and are useful information for providing services.

[0597] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate information about the user's mental state and appropriate action suggestions.

[0598] "Mental state" refers to the user's mental state and emotional condition, and is a factor that affects the accuracy of the suggestions.

[0599] "Usage facilities" refers to establishments such as restaurants, and is one of the options offered to users.

[0600] "Content" refers to the menus and services offered to users, and is customized according to user requests.

[0601] This invention is a system that analyzes a user's emotional state and provides personalized suggestions based on that analysis. This system mainly consists of three components: a server, a terminal, and a user.

[0602] First, the user inputs their preferences, restrictions, and biometric information through the device. The device flexibly collects this data using voice input and a touchscreen. Furthermore, the device captures the user's voice tone and facial expressions in real time through its built-in camera and microphone, and analyzes them using sentiment analysis technology. This sentiment analysis technology utilizes a generative AI model to evaluate the user's mental state from multiple perspectives.

[0603] Next, the terminal securely transmits all data, including the analysis results, to the server using encrypted communication. The server stores this information in a cloud database and performs analysis by combining it with usage facility data from external sources that are acquired periodically. The server then uses a generative AI model to suggest restaurants and menus optimized for the user's emotional state.

[0604] The suggested content is sent to the user's device and displayed in a visually easy-to-understand format. Based on this information, the user can make reservations for their desired facilities. Furthermore, features that allow users to share their emotional state and suggested facility information with other users can stimulate communication.

[0605] As a concrete example of this system, if a user enters a prompt such as, "I'm a little tired today, so please recommend a place where I can relax," the system will suggest quiet and calming cafes and restaurants. This allows users to spend comfortable time in a place that suits their emotional state at that moment.

[0606] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0607] Step 1:

[0608] Users use the device to input preference information, restriction information, and biometric information. This input can be done via the device's keyboard or voice input function. The entered information is temporarily stored on the device.

[0609] Step 2:

[0610] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. Based on this captured data, it uses emotion analysis technology to recognize the user's emotional state. A generative AI model analyzes this data and outputs an analysis result indicating the user's mental state. This analysis result is stored on the device for use in subsequent processing.

[0611] Step 3:

[0612] The terminal packages the analysis results and user-entered information and sends it to the server in an encrypted format. Before transmission, the data is validated to ensure integrity, and the format is adjusted as needed.

[0613] Step 4:

[0614] The server analyzes the received data package and stores it in a cloud database. This stored data is used in combination with information about the facilities used, obtained from external sources. The server uses a generative AI model to generate suggestions that take into account the user's mental state based on the sentiment analysis results. These suggestions are used to select the most suitable dining facilities and menus for the user.

[0615] Step 5:

[0616] Suggestions generated on the server are sent to the device and displayed on the user interface. The displayed information includes a map of the facility, photos of the menu, and reviews. Users can also make reservations based on this information by tapping the screen.

[0617] Step 6:

[0618] Users can share their emotional state and suggested information with other users. This sharing takes place through the device's social networking function, stimulating communication with other users. The shared information can also be used as reference by users with similar emotional states.

[0619] (Application Example 2)

[0620] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0621] Conventional food and beverage recommendation systems fail to consider the user's emotional state, making it difficult to provide appropriate recommendations and a satisfying dining experience. Furthermore, they lack the means to support users in preparing meals in a relaxed state at home. Therefore, there is a need for a system that analyzes the user's emotional state, makes food and beverage recommendations based on that analysis, and provides support for meal preparation.

[0622] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0623] In this invention, the server includes means for acquiring the user's food and drink preferences, restrictions, and health information; means for analyzing the acquired information to select an appropriate dining facility; and means for analyzing the user's emotional state and suggesting food and drinks that correspond to that emotional state. This makes it possible to suggest the most suitable food and drinks according to the user's emotions and effectively support meal preparation at home.

[0624] A "user" refers to an individual or household that uses this system and is the entity that receives food and beverage-related suggestions.

[0625] "Food and drink preference information" refers to information that indicates the types of ingredients and dishes a user prefers, as well as their taste preferences.

[0626] "Constraint information" refers to information that indicates conditions such as allergies or dietary restrictions that a user has that prevent them from avoiding certain foods and beverages.

[0627] "Health information" refers to information about the user's physical condition and health status, and serves as basic data when providing dietary suggestions.

[0628] "Means of acquisition" refers to the methods or techniques used to gather necessary information from users.

[0629] "Means for selecting appropriate dining facilities" refers to methods or technologies that select the most suitable dining location for a user based on the information acquired.

[0630] "Means for analyzing a user's emotional state" refers to methods or technologies that analyze data such as voice, facial expressions, and behavior to identify a user's current emotions.

[0631] "Means of suggesting food and beverages according to emotional state" refers to methods or technologies for recommending appropriate food and beverages to users based on their analyzed emotional state.

[0632] The system for carrying out this invention consists of multiple components. The main elements and their respective roles are described below.

[0633] Users input information about their food and drink preferences, restrictions, and health through mobile devices or devices installed in their homes. This information is temporarily stored on the device during the initial stage and then sent to the server as needed.

[0634] The server receives information sent by the user and stores it in a cloud-based database. Based on this data, the server performs analysis and selects appropriate dining establishments that match the user's preferences. It also incorporates an emotion engine to analyze the user's emotional state using voice and facial expression data. Based on the emotional state, it creates food and beverage recommendations according to the user's needs, such as whether they want to relax or feel energized.

[0635] Furthermore, the terminal provides the user with suggestions received from the server. These suggestions include menu recommendations tailored to the user's mood and restaurants and bars that enhance specific moods. The terminal is equipped with a voice interface that can guide the user through the preparation process for the food and drinks they have selected.

[0636] As an example, if a user is experiencing high stress levels after returning home from work, this system can suggest a relaxing beverage such as chamomile tea. It can also assist with preparation if needed.

[0637] An example of a prompt message would be: "How can I suggest a relaxing drink and help prepare it if the user is tired?"

[0638] These processes make it possible to provide users with a dining experience tailored to their needs and support a comfortable lifestyle.

[0639] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0640] Step 1:

[0641] Users input information about their food and drink preferences, restrictions, and health via mobile devices or home devices. This information is then prepared for sequential analysis by an emotion engine. The entered data is temporarily stored on the device and organized as the user's profile.

[0642] Step 2:

[0643] The device collects user voice and facial expression data through an emotion engine. This data is used as input for analyzing emotional states. Based on changes in voice tone and facial expressions, the engine quantifies and outputs the user's emotional state, such as joy, anger, sadness, or happiness.

[0644] Step 3:

[0645] The device sends the collected information to the server. The transmitted data includes information about the user's food and drink preferences, health information, and emotional state. This data is recorded in a cloud-based database and stored for subsequent analysis.

[0646] Step 4:

[0647] The server analyzes the received data and selects appropriate dining establishments based on the user's emotional state, food and drink preferences, and health information. It also generates optimal food and drink or menu options for the user through an AI model that takes their emotional state into consideration.

[0648] Step 5:

[0649] Suggestions generated by the server are sent to the terminal. The terminal displays the suggested information to the user and provides detailed explanations and options via a voice interface. This allows the user to gain knowledge about the suggested food, beverages, and establishments.

[0650] Step 6:

[0651] The device assists the user in preparing food and beverages of their choice. It uses a voice interface to guide users through ingredient confirmation and cooking procedures. For example, it provides step-by-step instructions for making a relaxing herbal tea.

[0652] Step 7:

[0653] When needed, the device provides the ability to share information with other users. It allows users to share information about recommended food, drinks, and establishments with other users who share similar emotional states, and to actively exchange opinions through online communities.

[0654] Through these steps, it is possible to improve the comfort of daily life by providing experiences based on the user's emotions and dining needs.

[0655] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0656] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0657] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0658] [Fourth Embodiment]

[0659] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0660] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0661] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0662] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0663] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0664] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0665] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0666] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0667] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0668] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0669] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0670] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0671] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0672] This invention relates to a system that suggests the most suitable dining establishments based on a user's food preferences, restrictions, and health status, and further automates the reservation process. This system mainly consists of three components: a server, a terminal, and a user.

[0673] First, users enter their food preferences and restrictions (e.g., allergies or religious reasons), as well as their current health information, through their smartphones, tablets, or other devices. This information is temporarily stored on the device and then transmitted to the server via the network.

[0674] The server receives data sent by users and stores it in a cloud-based database. This database is used as foundational data for providing meal suggestions based on user preferences and health status, utilizing big data analytics techniques. The server also periodically obtains the latest restaurant information from external food and beverage provider data providers and matches it with user information to select appropriate restaurants.

[0675] After selection, the terminal receives a list of suggested restaurants from the server and presents it visually to the user. The terminal is also configured to display detailed information about the restaurants and food recommendations tailored to the user's health condition.

[0676] For example, if a user prefers Japanese food and has a nut allergy, the server will list restaurants that meet these conditions and offer appropriate menus based on the user's current health data (e.g., blood sugar levels and calorie expenditure). This list is sent to the user's terminal for easy review.

[0677] The server also processes reservation requests from users and checks the reservation status of the selected facility. If a reservation is available, it quickly returns that information to the terminal for the user to confirm. Furthermore, the terminal sends data from IoT devices to the server, enabling more accurate dietary suggestions based on health predictions.

[0678] This system streamlines the above-mentioned processes, making the dining-out experience more efficient and healthier for users.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The user uses a terminal to input data about their food preferences, allergy information, and health status. The terminal temporarily stores this data in a local database and prepares it for transmission to the server.

[0682] Step 2:

[0683] The terminal sends user data to the server via the network. The server formats the received data, organizes it by user, and then stores it in a cloud database.

[0684] Step 3:

[0685] The server periodically retrieves the latest data from an external food and beverage establishment information provider and updates its own database. This information includes the establishment's location, menu, and business hours.

[0686] Step 4:

[0687] The server initiates a process to select dining establishments based on the user's dietary preferences, restrictions, and health information. This includes analyzing the user's health data and generating suggestions using AI algorithms.

[0688] Step 5:

[0689] The server sends the selection results to the terminal, which receives them and displays a list of suggested restaurants to the user. Detailed information and recommended menus for each restaurant are also displayed simultaneously.

[0690] Step 6:

[0691] The user selects a destination from a suggested list and completes the booking process on the device. The device then sends the user's selection to the server.

[0692] Step 7:

[0693] The server contacts the restaurant's reservation system to check availability. If a reservation is possible, it confirms the reservation and generates confirmation information.

[0694] Step 8:

[0695] The server sends reservation confirmation information to the terminal, which receives it and notifies the user. It also updates the dietary recommendations based on the presented health menu.

[0696] (Example 1)

[0697] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] In modern society, people's eating habits are becoming increasingly diverse, and their preferences and restrictions regarding food are becoming more complex. Therefore, there is a need for efficient methods to select and reserve restaurants that meet individual needs. Furthermore, suggesting meals tailored to individual health conditions is also important, and a system that comprehensively addresses these needs is required.

[0699] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0700] In this invention, the server includes means for acquiring food preference attributes, restriction attributes, and health attributes from the user, means for storing the attributes in a storage area on the cloud, and means for periodically acquiring facility information from an external provider database. This enables the rapid selection and reservation of dining facilities that meet the user's needs.

[0701] A "user" refers to an individual who uses the system to provide information about their dietary preferences, restrictions, and health status.

[0702] "Food preference attributes" refer to information about the specific foods, drinks, types of cuisine, and styles that a user prefers.

[0703] "Restriction attributes" refer to information about food and beverages that users should avoid or are prohibited from eating or drinking.

[0704] "Health attributes" refer to information related to the user's current health status, and appropriate dietary suggestions are provided based on this information.

[0705] "Cloud storage" refers to virtual storage space for storing and accessing data via the internet.

[0706] A "provider database" is an information storage system for acquiring and managing information about food and beverage establishments from external sources.

[0707] "Mathematical analysis techniques" refer to mathematical methods and algorithms used for analyzing and processing data.

[0708] A "detection device" is an external hardware device that monitors the user's health status and transmits that data to the system.

[0709] A "storage" is a data storage area for structuring external information and updating it regularly.

[0710] This invention is a system that suggests the most suitable dining establishments based on the user's food preferences, restrictions, and health condition, and automates the reservation process. The system mainly consists of three elements: a server, a terminal, and a user.

[0711] Users use smartphones or tablets to input their food preferences, dietary restrictions (such as allergies or religious background), and health information. This information is temporarily stored on the device and then transmitted to the server via secure communication using SSL / TLS.

[0712] The server leverages cloud technology to securely store acquired user information and perform big data analysis. Here, it structures the information using database management systems such as MySQL and MongoDB, and performs data analysis using analytical tools like Python's Pandas library and Google Cloud BigQuery. Furthermore, it collects data from external food and beverage establishments via APIs and records it in the provider database.

[0713] Based on the analysis results, the server selects restaurants and bars that meet the user's needs and sends a list to the device. This list includes detailed information about the establishments (address, opening hours, menu options, etc.). Using a framework such as React Native, this information is presented to the user visually on the device.

[0714] For example, if a user prefers Japanese food and wants to avoid nuts, the server will list restaurants that meet these conditions and can provide menus that take into account the user's current blood sugar levels and calorie expenditure. This list is then transferred to the user's terminal, allowing them to easily review and select from it.

[0715] Furthermore, during the reservation process, the server processes the user's request and checks the reservation status of the selected facility. If a reservation is possible, the system quickly returns the result to the terminal and notifies the user. In addition, the system can make new suggestions using prompt messages generated by an AI model.

[0716] Example of a prompt:

[0717] 1. "Using the user's food preferences (e.g., Japanese cuisine), please propose a restaurant that takes nut allergies into consideration."

[0718] 2. "Find restaurants that offer calorie-restricted menus based on the user's health data."

[0719] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0720] Step 1:

[0721] Users input their food preferences, restrictions, and health status using devices such as smartphones and tablets. The entered data is temporarily stored on the device. Specifically, users enter information using the application's forms, and this data is structured in JSON format. The input data includes food preferences (e.g., Japanese food), restrictions (e.g., nut allergy), and health information (e.g., blood sugar levels). The structured data is then ready to be sent to the server as output.

[0722] Step 2:

[0723] The terminal securely transmits user data to the server using the SSL / TLS protocol. The previously structured data is used as input. The data is received by the server, its integrity is checked, and then it is stored in a cloud database. As output, the data is securely stored in the cloud database.

[0724] Step 3:

[0725] The server retrieves user data from a cloud-based database and performs data analysis using the Python Pandas library. Inputs include user preferences, restrictions, and health data. Specifically, it filters the user data to extract information on restaurants and bars that match the specified criteria. The output is a list of restaurants and bars that meet the criteria.

[0726] Step 4:

[0727] The server retrieves the latest information on food and beverage establishments from an external provider database. It periodically accesses the external database using an API to update the data. The input is establishment information from the external provider. The updated establishment information is stored in a cloud database as output.

[0728] Step 5:

[0729] The terminal presents the user with a list of restaurants and bars received from the server via a user interface. The list is visually displayed using the React Native framework. The input is a list of establishments from the server. The output allows the user to view and select establishment information.

[0730] Step 6:

[0731] The server checks the reservation status of the selected facility based on the user's facility selection. It queries the availability of the facility using the facility reservation API. The input is the facility data selected by the user. The output is the availability result and is sent to the terminal.

[0732] Step 7:

[0733] The terminal receives reservation information from the server and displays a confirmation screen to the user. Once the user confirms, the final reservation information is finalized. The input is the reservation result from the server. The output displays a screen confirming the reservation and allowing the user to check the reservation status.

[0734] (Application Example 1)

[0735] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In modern society, users often face various constraints related to their health status and food preferences. However, there are limited systems that take this information into consideration to select the most suitable food providers and restaurants and enable smooth usage. Therefore, there is a need to develop an environment where users can easily select meals that meet their requirements and where reservations and orders can be made automatically.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0738] In this invention, the server includes means for acquiring information on food preferences, restrictions, and health information from the user; means for analyzing the acquired information and selecting an appropriate restaurant or food provider; and means for automatically coordinating reservations or orders at the selected restaurant or food provider. This allows users to choose meals according to their health condition and food preferences, and the automation of reservations and orders makes the service easier to use.

[0739] A "user" refers to an individual or organization that utilizes this system and is the entity that provides information on food preferences, restrictions, and health.

[0740] "Food preference information" refers to information about the types of dishes, seasonings, ingredients, etc. that the user prefers.

[0741] "Restriction information" refers to conditions that users must consider when making food choices, such as allergies or religious restrictions.

[0742] "Health information" refers to data about the user's physical health status, including parameters such as blood glucose levels and calorie consumption.

[0743] "Food and beverage establishments" refers to facilities that serve meals, including restaurants and cafes.

[0744] A "food provider" refers to a company or organization that provides services such as cooking and serving meals.

[0745] "Means for automatically coordinating reservations or orders" refers to a function in which the system automatically makes reservations or orders meals from restaurants or food providers selected by the user.

[0746] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to suggest the optimal meal choices based on user data.

[0747] An "external sensor device" is a hardware device used to acquire health status data from the user.

[0748] An "external database" is a database used by a system to store information about a food establishment or food provider and to access that information.

[0749] The system for realizing this invention collects the user's food preferences, dietary restrictions, and health information, and has the function of automatically selecting an appropriate restaurant or food provider and making a reservation or order.

[0750] The system starts when a user inputs data about their food preferences and health status using a smartphone or tablet. This information is temporarily stored on the device and transmitted to a server via the internet. The server stores this information in a cloud-based database and, by incorporating big data analytics technology, determines which restaurant or food provider is best suited to the user's needs. Then, using a generative AI model, it optimizes specific meal menus based on the user's health data.

[0751] The server also periodically retrieves data from external restaurants and food providers and compares it with user information. This process enables the system to make appropriate suggestions to users. Selected options are resent to devices such as smartphones and tablets and displayed for the user to visually confirm. Users select their preferred options from the suggested choices, and the system automatically makes the reservation or order.

[0752] For example, if a user prefers a vegan diet and is gluten-intolerant, the server will list restaurants or food providers that meet these criteria and send the list to the user's terminal. Once the user selects a restaurant, the system will automatically complete the reservation process.

[0753] As an example of a prompt, data can be sent to the generating AI model in the form of, "Please suggest the optimal meal plan based on the user's restrictions and health data. Example: 'Suggest a vegan and gluten-free meal plan'."

[0754] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0755] Step 1:

[0756] Users use their smartphones or tablets to input their dietary preferences, restrictions, and health information. This input information is temporarily stored on the device. This input includes allergies, dietary preferences (e.g., vegan, low-carb), and recent health data. The device organizes this information as structured data and prepares it for transmission to the server.

[0757] Step 2:

[0758] The device sends user data to the server. The transmitted data is analyzed on the server. The server stores the data in a cloud-based database and uses big data analytics technology to select restaurants or food providers that match the user's preferences. The analysis matches user data with information about restaurants and extracts options that meet the user's criteria.

[0759] Step 3:

[0760] The server uses a generative AI model to optimize meal plans based on the user's health data. This process takes the user's health parameters and past meal history as input, and runs a model that generates an optimal meal plan. As a result, a proposed menu is generated and sent from the server to the terminal.

[0761] Step 4:

[0762] The server periodically retrieves and updates information on restaurants and food providers from external data providers. This input includes information such as the establishment's menu, reputation, and location, and the database is updated on the server side to reflect the latest information.

[0763] Step 5:

[0764] The device displays a list of restaurants or food service providers and their menus, received from the server, to the user. This output allows the user to view visual information on their device. The user can tap on a selected restaurant or menu item from the suggestion list to view more detailed information.

[0765] Step 6:

[0766] When a user selects a desired restaurant or food provider, the terminal transmits that information to the server. The server automatically completes the reservation or order with the selected restaurant or provider. This process involves coordinating with the restaurant's reservation system to check seating availability and menu stock before confirming the reservation or order.

[0767] Step 7:

[0768] This system also provides a function for users to share applied information about facilities and meal menus. The goal is to add user feedback and ratings to the database and enable the sharing of information with other users.

[0769] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0770] This invention is a food and beverage facility proposal system that incorporates the user's emotional state, and aims to improve the user experience using emotion recognition technology. This system mainly consists of four components: a server, a terminal, a user, and an emotion engine.

[0771] First, the user inputs information such as their food preferences and restrictions, health information, and current emotional state through their terminal device. This emotional information is then analyzed by an emotion engine. The emotion engine recognizes the user's emotional state using various data, including voice tone, facial expressions, and the speed of the user's selection actions. This information is temporarily stored on the terminal before being sent to the server.

[0772] The server receives data from users and stores it in a cloud database. The server also periodically retrieves information from external food and beverage establishment information providers and analyzes it in conjunction with user data. Based on the analysis results from the emotion engine, the server selects food and beverage establishments that take into account the user's stress level and satisfaction predictions.

[0773] To provide meal suggestions tailored to the user's emotional state, the device receives suggestions from the server and presents them to the user. These suggestions include menu recommendations that match the user's mood and options for dining establishments that can enhance a particular mood. For example, if the user is seeking relaxation, a restaurant with a quiet atmosphere will be recommended.

[0774] Furthermore, the system allows users to share emotions and corresponding facility information with other users to support information sharing among them. This feature aims to promote communication among users with similar emotional states and provide a space for empathy.

[0775] Furthermore, the reservation process can also be adapted to the user's emotional state. If the emotion engine detects a high-stress state, the reservation confirmation and modification procedures are designed to proceed more smoothly.

[0776] Through the processes described above, this system enables personalized service tailored to the user's emotions, providing a richer dining experience.

[0777] The following describes the processing flow.

[0778] Step 1:

[0779] The user uses a device to input data about their food preferences, dietary restrictions, health status, and emotional state. The device temporarily stores the entered data in a local database.

[0780] Step 2:

[0781] The device activates an emotion engine and collects data such as the user's voice tone, facial expressions, and operation speed through sensors and cameras. This data is used to analyze the user's emotional state.

[0782] Step 3:

[0783] The emotion engine analyzes the collected data to identify the user's emotional state. The results are returned to the device in the form of numerical values ​​representing stress levels and emotional tendencies.

[0784] Step 4:

[0785] The device sends integrated data to the server, including analysis results from the emotion engine, as well as the user's food preferences and health information.

[0786] Step 5:

[0787] The server receives the integrated data and stores it in a database. Based on this information, the server begins selecting dining establishments optimized for the user's emotional state.

[0788] Step 6:

[0789] The server retrieves the latest data from an external food and beverage establishment information provider and filters the establishments to match the user's emotional and health state. For example, a user who wants to relax will be presented with a quiet and calming food and beverage establishment.

[0790] Step 7:

[0791] The server sends a list of selected restaurants to the terminal. The terminal receives this list and displays a list of recommendations to the user based on their emotional state.

[0792] Step 8:

[0793] The user selects their preferred restaurant from the suggested options and makes a reservation. The terminal then sends this information to the server.

[0794] Step 9:

[0795] The server accesses the reservation system of the selected restaurant and checks availability in real time. If a reservation is available, it immediately confirms the reservation and generates confirmation information.

[0796] Step 10:

[0797] The server sends reservation confirmation information to the terminal, which then presents it to the user. The server considers the user's emotional state and displays an appropriate message for the reservation confirmation.

[0798] This process allows the system to provide a personalized dining experience that reflects the user's emotional state.

[0799] (Example 2)

[0800] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0801] In modern society, there is a demand for services that are tailored to the emotional state of individual users. However, conventional food and beverage establishment recommendation systems have failed to consider the emotional state of users and have remained limited to providing uniform services. As a result, the user experience has been insufficient, and it has been difficult to make suggestions that meet the unique needs of each user, leading to a decline in satisfaction.

[0802] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0803] In this invention, the server includes means for acquiring preference information, constraint information, and biometric information from the user; means for recognizing the user's emotional state using emotion analysis technology and generating analysis results; and means for predicting the user's mental state based on the emotion analysis results using a generative AI model. This enables the provision of detailed suggestions for dining facilities and personalized experiences tailored to the user's emotional state.

[0804] A "user" refers to a person who uses the system and is the subject who receives suggestions for restaurants, bars, and other establishments based on their emotional state and preferences.

[0805] "Preference information" refers to data that indicates a user's likes and dislikes, and is used as a criterion when suggesting meal options.

[0806] "Restriction information" refers to information that includes constraints that users need to consider when making food choices, such as allergies or dietary restrictions.

[0807] "Biometric information" refers to data about the user's health status and physical characteristics, which is acquired through external measuring devices, etc.

[0808] "Emotion analysis technology" refers to technology that uses data such as voice tone and facial expressions to identify a user's emotional state.

[0809] "Analysis results" refer to data analyzing the user's emotional state obtained through emotion analysis technology, and are useful information for providing services.

[0810] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate information about the user's mental state and appropriate action suggestions.

[0811] "Mental state" refers to the user's mental state and emotional condition, and is a factor that affects the accuracy of the suggestions.

[0812] "Usage facilities" refers to establishments such as restaurants, and is one of the options offered to users.

[0813] "Content" refers to the menus and services offered to users, and is customized according to user requests.

[0814] This invention is a system that analyzes a user's emotional state and provides personalized suggestions based on that analysis. This system mainly consists of three components: a server, a terminal, and a user.

[0815] First, the user inputs their preferences, restrictions, and biometric information through the device. The device flexibly collects this data using voice input and a touchscreen. Furthermore, the device captures the user's voice tone and facial expressions in real time through its built-in camera and microphone, and analyzes them using sentiment analysis technology. This sentiment analysis technology utilizes a generative AI model to evaluate the user's mental state from multiple perspectives.

[0816] Next, the terminal securely transmits all data, including the analysis results, to the server using encrypted communication. The server stores this information in a cloud database and performs analysis by combining it with usage facility data from external sources that are acquired periodically. The server then uses a generative AI model to suggest restaurants and menus optimized for the user's emotional state.

[0817] The suggested content is sent to the user's device and displayed in a visually easy-to-understand format. Based on this information, the user can make reservations for their desired facilities. Furthermore, features that allow users to share their emotional state and suggested facility information with other users can stimulate communication.

[0818] As a concrete example of this system, if a user enters a prompt such as, "I'm a little tired today, so please recommend a place where I can relax," the system will suggest quiet and calming cafes and restaurants. This allows users to spend comfortable time in a place that suits their emotional state at that moment.

[0819] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0820] Step 1:

[0821] Users use the device to input preference information, restriction information, and biometric information. This input can be done via the device's keyboard or voice input function. The entered information is temporarily stored on the device.

[0822] Step 2:

[0823] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. Based on this captured data, it uses emotion analysis technology to recognize the user's emotional state. A generative AI model analyzes this data and outputs an analysis result indicating the user's mental state. This analysis result is stored on the device for use in subsequent processing.

[0824] Step 3:

[0825] The terminal packages the analysis results and user-entered information and sends it to the server in an encrypted format. Before transmission, the data is validated to ensure integrity, and the format is adjusted as needed.

[0826] Step 4:

[0827] The server analyzes the received data package and stores it in a cloud database. This stored data is used in combination with information about the facilities used, obtained from external sources. The server uses a generative AI model to generate suggestions that take into account the user's mental state based on the sentiment analysis results. These suggestions are used to select the most suitable dining facilities and menus for the user.

[0828] Step 5:

[0829] Suggestions generated on the server are sent to the device and displayed on the user interface. The displayed information includes a map of the facility, photos of the menu, and reviews. Users can also make reservations based on this information by tapping the screen.

[0830] Step 6:

[0831] Users can share their emotional state and suggested information with other users. This sharing takes place through the device's social networking function, stimulating communication with other users. The shared information can also be used as reference by users with similar emotional states.

[0832] (Application Example 2)

[0833] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0834] Conventional food and beverage recommendation systems fail to consider the user's emotional state, making it difficult to provide appropriate recommendations and a satisfying dining experience. Furthermore, they lack the means to support users in preparing meals in a relaxed state at home. Therefore, there is a need for a system that analyzes the user's emotional state, makes food and beverage recommendations based on that analysis, and provides support for meal preparation.

[0835] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0836] In this invention, the server includes means for acquiring the user's food and drink preferences, restrictions, and health information; means for analyzing the acquired information to select an appropriate dining facility; and means for analyzing the user's emotional state and suggesting food and drinks that correspond to that emotional state. This makes it possible to suggest the most suitable food and drinks according to the user's emotions and effectively support meal preparation at home.

[0837] A "user" refers to an individual or household that uses this system and is the entity that receives food and beverage-related suggestions.

[0838] "Food and drink preference information" refers to information that indicates the types of ingredients and dishes a user prefers, as well as their taste preferences.

[0839] "Constraint information" refers to information that indicates conditions such as allergies or dietary restrictions that a user has that prevent them from avoiding certain foods and beverages.

[0840] "Health information" refers to information about the user's physical condition and health status, and serves as basic data when providing dietary suggestions.

[0841] "Means of acquisition" refers to the methods or techniques used to gather necessary information from users.

[0842] "Means for selecting appropriate dining facilities" refers to methods or technologies that select the most suitable dining location for a user based on the information acquired.

[0843] "Means for analyzing a user's emotional state" refers to methods or technologies that analyze data such as voice, facial expressions, and behavior to identify a user's current emotions.

[0844] "Means of suggesting food and beverages according to emotional state" refers to methods or technologies for recommending appropriate food and beverages to users based on their analyzed emotional state.

[0845] The system for carrying out this invention consists of multiple components. The main elements and their respective roles are described below.

[0846] Users input information about their food and drink preferences, restrictions, and health through mobile devices or devices installed in their homes. This information is temporarily stored on the device during the initial stage and then sent to the server as needed.

[0847] The server receives information sent by the user and stores it in a cloud-based database. Based on this data, the server performs analysis and selects appropriate dining establishments that match the user's preferences. It also incorporates an emotion engine to analyze the user's emotional state using voice and facial expression data. Based on the emotional state, it creates food and beverage recommendations according to the user's needs, such as whether they want to relax or feel energized.

[0848] Furthermore, the terminal provides the user with suggestions received from the server. These suggestions include menu recommendations tailored to the user's mood and restaurants and bars that enhance specific moods. The terminal is equipped with a voice interface that can guide the user through the preparation process for the food and drinks they have selected.

[0849] As an example, if a user is experiencing high stress levels after returning home from work, this system can suggest a relaxing beverage such as chamomile tea. It can also assist with preparation if needed.

[0850] An example of a prompt message would be: "How can I suggest a relaxing drink and help prepare it if the user is tired?"

[0851] These processes make it possible to provide users with a dining experience tailored to their needs and support a comfortable lifestyle.

[0852] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0853] Step 1:

[0854] Users input information about their food and drink preferences, restrictions, and health via mobile devices or home devices. This information is then prepared for sequential analysis by an emotion engine. The entered data is temporarily stored on the device and organized as the user's profile.

[0855] Step 2:

[0856] The device collects user voice and facial expression data through an emotion engine. This data is used as input for analyzing emotional states. Based on changes in voice tone and facial expressions, the engine quantifies and outputs the user's emotional state, such as joy, anger, sadness, or happiness.

[0857] Step 3:

[0858] The device sends the collected information to the server. The transmitted data includes information about the user's food and drink preferences, health information, and emotional state. This data is recorded in a cloud-based database and stored for subsequent analysis.

[0859] Step 4:

[0860] The server analyzes the received data and selects appropriate dining establishments based on the user's emotional state, food and drink preferences, and health information. It also generates optimal food and drink or menu options for the user through an AI model that takes their emotional state into consideration.

[0861] Step 5:

[0862] Suggestions generated by the server are sent to the terminal. The terminal displays the suggested information to the user and provides detailed explanations and options via a voice interface. This allows the user to gain knowledge about the suggested food, beverages, and establishments.

[0863] Step 6:

[0864] The device assists the user in preparing food and beverages of their choice. It uses a voice interface to guide users through ingredient confirmation and cooking procedures. For example, it provides step-by-step instructions for making a relaxing herbal tea.

[0865] Step 7:

[0866] When needed, the device provides the ability to share information with other users. It allows users to share information about recommended food, drinks, and establishments with other users who share similar emotional states, and to actively exchange opinions through online communities.

[0867] Through these steps, it is possible to improve the comfort of daily life by providing experiences based on the user's emotions and dining needs.

[0868] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0869] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0870] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0871] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0872] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0873] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0874] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0875] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0876] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0877] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0878] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0879] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0880] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0881] 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.

[0882] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0883] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0884] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0885] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0886] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0887] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0888] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0889] The following is further disclosed regarding the embodiments described above.

[0890] (Claim 1)

[0891] A means of obtaining information on food preferences, dietary restrictions, and health information from users,

[0892] A means for analyzing the acquired information and selecting an appropriate food and beverage establishment,

[0893] A means for automatically adjusting reservations at the selected dining establishments,

[0894] A means for suggesting a meal menu based on the user's current health status,

[0895] A means for sharing information about the aforementioned dining facilities and meal menus with other users,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for communicating with an external sensor device in order to obtain the health status of the user.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for periodically obtaining and updating information on the aforementioned food and beverage facility from an external database.

[0901] "Example 1"

[0902] (Claim 1)

[0903] A means of obtaining food preference attributes, restriction attributes, and health attributes from users,

[0904] A means for analyzing the acquired attributes and selecting an appropriate facility,

[0905] A means for automatically adjusting reservations at the selected facilities,

[0906] A means for suggesting cooking options based on the user's current health status,

[0907] A means of sharing the aforementioned facility information and menu options with other users,

[0908] Means for storing the attributes in a storage area on the cloud,

[0909] A means for accessing the storage area on the aforementioned cloud and performing analysis using a predetermined mathematical analysis technique,

[0910] A system that includes means for periodically obtaining facility information from an external provider database.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for communicating with an external detection device in order to obtain the health status of the user.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for periodically acquiring and updating information about the aforementioned facility from an external information storage facility.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] A means of obtaining information on food preferences, dietary restrictions, and health information from users,

[0918] A means for analyzing the acquired information and selecting an appropriate food and beverage facility or food provider,

[0919] A means for automatically coordinating reservations or orders at the selected food and beverage establishments or food providers,

[0920] A means for suggesting a meal menu based on the user's current health status,

[0921] A means for sharing information about the aforementioned food and beverage establishment or food provider and food menu with other users,

[0922] A means for optimizing the meal menu using a generative AI model,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, further comprising means for communicating with an external sensor device in order to obtain the health status of the user.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising means for periodically obtaining and updating information on the aforementioned food and beverage establishment or food provider from an external database.

[0928] "Example 2 of combining an emotion engine"

[0929] (Claim 1)

[0930] Means for obtaining preference information, restriction information, and biometric information from users,

[0931] A means for selecting a facility to use based on the acquired information and analysis results,

[0932] A means for automatically adjusting procedures at the selected facility,

[0933] A means of suggesting content based on the user's current emotional state,

[0934] A means of sharing information about the aforementioned facilities and content with other users,

[0935] A means for recognizing a user's emotional state using emotion analysis technology and generating analysis results,

[0936] A method for predicting a user's mental state based on emotion analysis results using a generative AI model,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, further comprising means for communicating with an external measuring device in order to acquire the user's biometric information.

[0940] (Claim 3)

[0941] The system according to claim 1, further comprising means for periodically acquiring and updating information on the aforementioned facilities used from an external source.

[0942] "Application example 2 when combining with an emotional engine"

[0943] (Claim 1)

[0944] A means of obtaining information on food and drink preferences, restrictions, and health information from users,

[0945] A means for analyzing the acquired information and selecting an appropriate dining facility,

[0946] A means for automatically adjusting reservations at the selected dining facilities,

[0947] A means for suggesting meal options based on the user's current health status,

[0948] Means for sharing information regarding the aforementioned dining facilities and meal options,

[0949] A means of analyzing the user's emotional state and suggesting food and beverages that correspond to that emotional state,

[0950] The means for supporting the preparation of the aforementioned proposed food and beverages,

[0951] A system that includes this.

[0952] (Claim 2)

[0953] The system according to claim 1, further comprising means for communicating with an external detection device in order to acquire the user's health status and emotional state.

[0954] (Claim 3)

[0955] The system according to claim 1, further comprising means for regularly acquiring and updating information on the dining facility from an external information base. [Explanation of symbols]

[0956] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining information on food preferences, dietary restrictions, and health information from users, A means for analyzing the acquired information and selecting an appropriate food and beverage facility or food provider, A means for automatically coordinating reservations or orders at the selected food and beverage establishments or food providers, A means for suggesting a meal menu based on the user's current health status, A means for sharing information about the aforementioned food and beverage establishment or food provider and food menu with other users, A means for optimizing the meal menu using a generative AI model, A system that includes this.

2. The system according to claim 1, further comprising means for communicating with an external sensor device in order to obtain the health status of the user.

3. The system according to claim 1, further comprising means for periodically obtaining and updating information on the aforementioned food and beverage establishment or food provider from an external database.

Images