system

Abstract

We provide the system. [Solution] Means for obtaining the user's location information, An artificial intelligence means for analyzing a user's dietary history and health information, A means of suggesting a meal menu suitable for the user based on the results of the analysis, A means of providing restaurant reservation information based on the proposed menu, A means of collecting user feedback to improve the accuracy of suggestions, 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] Modern consumers struggle to select an optimal diet from a vast array of meal options in their busy lives, especially considering health and allergies. Also, it is difficult to quickly and easily find a suitable restaurant based on the current location. Furthermore, there is a lack of means to seamlessly manage the meal reservation and ordering process. It is necessary to solve these problems.

Means for Solving the Problems

[0005] This invention is a system that identifies the user's current location and uses artificial intelligence to analyze their eating history and health information to suggest the optimal meal menu. It also includes a means for providing real-time restaurant reservation information based on the suggested menu. Furthermore, if delivery is selected, it provides a means for tracking the order process and delivery status, allowing for the collection of user feedback to improve the system's accuracy.

[0006] "Location information" refers to data that indicates the user's current geographical location.

[0007] "Meal history" refers to data about the food and dishes a user has consumed in the past.

[0008] "Health information" refers to data about the user's health status, allergy information, nutritional restrictions, etc.

[0009] "Artificial intelligence tools" are systems that analyze data and support pattern recognition and decision-making.

[0010] "The proposed method" refers to a mechanism that provides users with the optimal choice based on the analysis results.

[0011] "Reservation information" refers to data used to secure seats and times at a restaurant selected by the user in advance.

[0012] "Feedback" refers to information based on user experiences and opinions, which is used to improve system performance.

[0013] A "system" is a totality formed by the combination of related elements in order to achieve a specific function. [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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) 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 labeled storage 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 labeled communication I / F (Interface) 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 is a system that utilizes location information, meal history, and health information to optimize an individual's dining experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0036] The server has the ability to determine the user's current location based on the received location information. This allows the server to easily obtain information about nearby restaurants accessible to the user. The server also analyzes the user's eating history and health information, and generates appropriate meal menus considering the user's preferences and allergy information. For example, if a user has previously preferred a lot of Japanese food and has allergies to certain ingredients, new menu suggestions will take this information into account.

[0037] The terminal functions as a platform that presents information transmitted from the server to the user. Users can review the suggested menus through the terminal and make adjustments as needed. After selecting a menu, the terminal displays reservation information to the user in real time, allowing them to efficiently check restaurant availability and make reservations.

[0038] Users select suggested menu items using their devices and provide feedback. For example, they can provide feedback to the system by entering their satisfaction level after the meal and details of any newly discovered allergies. This feedback is collected on the server and used as training data for the AI. This improves the accuracy of future suggestions and enables the provision of more personalized services.

[0039] This invention supports users in selecting restaurants and deciding on menus based on their individual needs, thereby realizing an efficient and healthy dining experience.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server retrieves the user's location information and compares it with coordinate data in the database to determine the user's current location. Based on this information, it lists nearby partner restaurants.

[0043] Step 2:

[0044] The server retrieves the user's dietary history and health information from a database and analyzes it. Based on the analysis results, it generates a list of foods the user likes and ingredients to avoid.

[0045] Step 3:

[0046] Based on the restaurant menu information identified in Step 1, the server uses AI to generate the optimal menu that matches the analyzed user profile.

[0047] Step 4:

[0048] The terminal displays the optimal menu and restaurant information received from the server to the user. The user reviews the suggested menu and makes adjustments to the dishes and allergy information as needed.

[0049] Step 5:

[0050] Users select their preferred menu items and use the terminal to make reservations and pre-order their meals at the corresponding restaurant. The terminal also allows users to check real-time availability information.

[0051] Step 6:

[0052] If the user selects home delivery, the server integrates with the delivery service and provides a mechanism to order the selected menu items. The terminal tracks the delivery status in real time and notifies the user of the estimated delivery arrival time.

[0053] Step 7:

[0054] Users provide feedback using a device after their meal. They input their satisfaction level with the meal and any new health information, and send it to the server.

[0055] Step 8:

[0056] The server stores user feedback information in a database and uses it as training data for the AI ​​model to improve the accuracy of future suggestions.

[0057] (Example 1)

[0058] 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."

[0059] In modern society, suggesting meals based on individual user preferences and health conditions, and selecting appropriate restaurants, is crucial for maintaining health and improving dining satisfaction. However, conventional systems have been unable to effectively utilize individual health and location information, making it difficult to suggest optimal meals. Furthermore, there has been a challenge in accumulating feedback on selected menus and incorporating it into future suggestions.

[0060] 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.

[0061] In this invention, the server includes means for acquiring data to confirm the user's current location, machine learning means for analyzing the user's meal history and health status data, and recommendation means for suggesting meal menus suitable for the user based on the analysis results. This makes it possible to provide optimal meal menus tailored to the individual needs of each user and to improve the accuracy of suggestions based on feedback.

[0062] "Data used to determine the user's current location" refers to digital data that includes location information such as latitude and longitude to identify the user's location.

[0063] "Machine learning methods" are data analysis techniques that use large amounts of data to enable algorithms to identify patterns and make predictions.

[0064] A "recommended method" is a method or system for presenting the user with the optimal choice based on the analyzed results.

[0065] An "information generation means" is a function for creating appropriate information based on specific circumstances and providing it to the user.

[0066] A "data collection method" is a system that efficiently collects and stores user feedback and other inputs to be used for subsequent analysis.

[0067] An "electronic system" is an aggregate of electronic devices and related software that provides functionality.

[0068] A "delivery service" is a logistics and management system for delivering selected products to a location specified by the user.

[0069] "Nutritional composition" refers to information about the types and amounts of each nutrient contained in a particular food or menu item.

[0070] "Display means" refers to devices or technologies for visually presenting information to a user in digital or analog format.

[0071] This invention is an electronic system that optimizes the dining experience using the user's location information, meal history, and health information. The system consists of a server, a terminal, and a user.

[0072] The server utilizes a digital device equipped with a GPS module to obtain location information from the user's terminal. Based on this location information, the server uses a database management system (DBMS) to search for restaurant information around the user's current location. Furthermore, it applies machine learning algorithms and uses data analysis tools such as Python and R to comprehensively analyze the user's past eating history and health information. Based on this analysis, a generative AI model is used to generate the most suitable meal menu for each individual user in real time.

[0073] The terminal visually displays suggested meal menus sent from the server to the user. This display uses an application that runs on a smartphone or tablet. The user reviews the suggested menu through the terminal and makes adjustments based on allergy information and preferences if necessary. After the menu is finalized, the terminal provides the user with information on available seats and reservation status at the restaurant. This allows the user to make reservations efficiently.

[0074] After the meal, users input their satisfaction level and any newly discovered allergies using a feedback form provided. This feedback is sent to the server via the device and stored again as training data for the algorithm. This allows the server to improve the accuracy of future suggestions and propose more individually optimized menus.

[0075] As a concrete example of its use, users can enter prompt messages. For instance, if a user uses the prompt, "I'd like Japanese food for our next date. I don't like spicy food, so please suggest menu items that exclude it," the system will suggest Japanese restaurants that meet those criteria, improving the user's dining experience.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server receives location information from the user's terminal. This input data is latitude and longitude information indicating the user's current location. The server uses this information to query a database management system (DBMS) and outputs information about restaurants near the user.

[0079] Step 2:

[0080] The server retrieves the user's meal history and health information. This data includes past eating habits and allergy information. The server utilizes machine learning models to analyze this input data, generating meal menus based on the user's preferences and listing suggested options as output.

[0081] Step 3:

[0082] The server sends the generated menu suggestions to the device. The device displays the suggested menus to the user on the application screen. The user scrolls and taps on the device to view detailed menu information. This improves the convenience of selecting from the suggested options.

[0083] Step 4:

[0084] The user reviews the suggested menu through their terminal and selects their desired menu item. This input is sent to the server, which retrieves the restaurant's reservation information in real time and outputs available reservation dates and times.

[0085] Step 5:

[0086] The server registers restaurant reservations based on the menu selected by the user. This involves updating a database that manages reservation confirmations and seating availability. Reservation completion information is sent to the terminal and displayed for the user to visually confirm.

[0087] Step 6:

[0088] After a meal, users provide feedback on their satisfaction level and any new allergy information using a device. The user's input data is sent to a server and stored as training data for the generated AI model. The goal is to improve the accuracy of future menu suggestions.

[0089] In this way, a series of processes are realized that provide the optimal dining experience tailored to the individual needs of each user.

[0090] (Application Example 1)

[0091] 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."

[0092] Traditional food delivery services have struggled to provide optimal meal suggestions based on individual users' health information and dietary preferences, failing to offer an efficient and healthy consumption experience. Furthermore, the reservation and ordering processes were complex and cumbersome for users. In addition, insufficient tracking of delivery status meant that users could not wait for their orders with peace of mind.

[0093] 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.

[0094] In this invention, the server includes means for acquiring the user's location information, means for a computational model for analyzing the user's meal history and health information, and means for making meal suggestions suitable for the user based on the analysis results. This makes it possible to efficiently provide menus optimized for each user, suggest meals that match the user's health condition and preferences, and enable efficient ordering.

[0095] "Means for obtaining user location information" refers to the technology or device necessary to determine the user's current location, and generally involves using GPS or Wi-Fi signals to determine the location.

[0096] "Computational model means for analyzing a user's dietary history and health information" refers to algorithms and software for collecting and analyzing data on a user's past dietary choices and health status, and is used to generate optimal dietary suggestions for the user.

[0097] "Means of providing meal suggestions suitable for the user based on analysis results" refers to technologies and devices that present meal options that match the user's preferences and health condition based on data obtained through analysis.

[0098] "Means of providing reservation information for dining establishments" refers to technology or devices that query reservation availability information for dining establishments selected by the user and communicate the availability and reservation status to the user.

[0099] "Means for collecting user feedback and improving the accuracy of suggestions" refers to technologies and devices used to acquire user feedback as data, improve the system's learning process based on that information, and enhance the quality of future suggestions.

[0100] "Methods for quickly ordering optimized menus through delivery services" refer to technologies and devices used to efficiently order suggested meal menus using delivery services and to have meals delivered at the time desired by the user.

[0101] "Methods for tracking delivery status in real time" refer to technologies and devices that allow users to check the current location of their ordered meals and provide them with the latest delivery information.

[0102] "Means for checking nutritional information and allergy information" refers to technologies and devices that allow users to easily check the nutrients included in suggested menus and information related to their own allergies.

[0103] To realize this invention, we will specifically explain how each element—the server, terminal, and user—functions.

[0104] The server is equipped with hardware to acquire the user's location information using GPS or Wi-Fi signals. Next, the server utilizes artificial intelligence as a computational model to analyze the user's past eating history and health information. This AI model is built using Python libraries and data analysis tools. Based on the analyzed data, it generates meal suggestions tailored to the user. As an example of a suggestion, the server will find restaurants that offer low-calorie Japanese food for a user who prefers such menus.

[0105] The terminal displays information about meal menus and dining locations sent from the server to the user via a dedicated mobile application. This application is developed using Android® and iOS frameworks (e.g., Flutter® and React Native). The terminal displays real-time tracking information for the reservation status and delivery status of the dining location selected by the user. Users can also check nutritional information and allergy information for the menu via the terminal.

[0106] Users select meal options and provide feedback based on information provided through their devices. This data is sent to the server to improve the system's accuracy. User feedback plays a crucial role in suggesting new meal options. For example, if a user particularly likes a menu item, that information will be reflected in future suggestions.

[0107] As a concrete example, a user might make a request via a prompt message such as, "I'm currently in Shibuya, and I'm on a low-sugar diet, so please prioritize displaying low-carb menus. I prefer Japanese food, so please suggest restaurants that match that." Based on this, the server analyzes the request and suggests menus that meet the criteria.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server obtains the user's location information using GPS. The input is location data transmitted from the user's smartphone. This data is analyzed to determine the user's current location. The output is the identified geographic coordinates.

[0111] Step 2:

[0112] The server retrieves the user's dietary history and health information from a database. This data is then used as input for analysis using a computational model. This analysis allows for an understanding of the user's dietary preferences and health status. The output is a user profile based on this information.

[0113] Step 3:

[0114] The server uses the above location information and profile as input to refer to a database of nearby restaurants and selects a suitable meal menu. An AI model is used here to list options that match the user's preferences and restrictions. The output is a list of several suitable meal menus and the restaurants that offer them.

[0115] Step 4:

[0116] The server transmits information about the selected menu and dining location to the terminal. The terminal displays this information on its user interface. The input consists of the menu and dining location information received from the server. The output is a visual presentation of this information to the user. The user can check menu details and reservation status.

[0117] Step 5:

[0118] The user selects a menu item based on the presented information and sends feedback from the terminal. The input consists of the user's menu selection and feedback. This data is sent to the server and used to improve the accuracy of future meal recommendations. The output generates newly learned user preference data.

[0119] Step 6:

[0120] The server makes restaurant reservations or delivery arrangements based on the user's selection. Input consists of the user's menu selection and reservation request. The server then refers to a database to make the reservation or delivery arrangement. The output generates reservation confirmation information or delivery tracking information.

[0121] Step 7:

[0122] The terminal notifies the user of reservation confirmation information or delivery status. The input is reservation or delivery information received from the server. The terminal displays this on the user interface and provides output that allows the user to visually confirm this information.

[0123] 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.

[0124] This invention is a system that utilizes location information, meal history, health information, and emotional information to optimize the user's dining experience. This system consists of three elements: a server, a terminal, and a user, and is equipped with an emotional engine.

[0125] The server first obtains the user's current location from location services and identifies nearby partner restaurants. Based on this location information, it creates a list of restaurants the user can access. Furthermore, it retrieves past dining history and health information from a database and analyzes it using artificial intelligence. The results of this analysis allow the server to understand the user's food preferences and any dietary restrictions they should avoid.

[0126] Next, the emotion engine analyzes the user's emotional data acquired by the device (for example, data obtained from a smartwatch or the device's built-in camera). The emotion engine then works with a server to suggest the most suitable menu or restaurant based on the user's emotional state. For example, if the user is feeling stressed, the system can select a menu that includes beverages and dishes with relaxing effects.

[0127] The terminal functions as a platform that presents users with menu suggestions and restaurant information sent from the server. Users can review and adjust suggested menus, make reservations, and place orders through the terminal. If a delivery option is selected, real-time tracking information of the delivery status is also provided.

[0128] Users can input feedback into their terminal after selecting, booking, and ordering a meal, reporting their satisfaction level and changes in their emotions regarding the meal. Based on the collected feedback and emotional data, the server can improve the accuracy of the system and further personalize future recommendations.

[0129] Thus, the present invention realizes meticulous meal suggestions that respond to the user's emotions, and provides a service that is attentive to the user's health and emotional needs.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server obtains the user's location information and determines their current location. Based on this location information, it lists nearby partner restaurants.

[0133] Step 2:

[0134] The server retrieves the user's dietary history and health information from a database and analyzes it using artificial intelligence. Through this analysis, it profiles the user's preferences and limitations.

[0135] Step 3:

[0136] The device uses cameras and sensors to measure the user's emotions in real time. The obtained emotion data is then input into an emotion engine.

[0137] Step 4:

[0138] The emotion engine analyzes the user's emotional state and sends that information to the server. The server integrates the emotional information with the user's dietary profile and suggests the optimal meal plan.

[0139] Step 5:

[0140] The terminal displays menus and restaurant information provided by the server to the user. The user reviews and adjusts the menu items via the terminal and approves their selection.

[0141] Step 6:

[0142] The user makes a reservation at their chosen restaurant and orders their menu through the terminal. The terminal checks and displays availability and reservation status to the user.

[0143] Step 7:

[0144] If home delivery is selected, the server will coordinate with the delivery service to enable completion of the delivery process and tracking of the delivery status. The terminal will provide the user with real-time information on the delivery time.

[0145] Step 8:

[0146] After the meal, users use their device to input feedback, sending their satisfaction level, emotional changes, and new health information to the server.

[0147] Step 9:

[0148] The server stores the collected feedback and sentiment data in a database and optimizes future suggestions by updating the AI ​​model.

[0149] (Example 2)

[0150] 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".

[0151] Conventional meal recommendation systems often only consider the user's past eating history and health information, making it difficult to provide appropriate recommendations that reflect the user's emotional state. Furthermore, users often experience stress and wasted time when choosing which restaurant to eat at. Additionally, the limited use of feedback hindered improvements in the system's recommendation accuracy.

[0152] 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.

[0153] In this invention, the server includes means for acquiring the user's location information and identifying nearby affiliated restaurants and bars, information processing technology means for analyzing the user's meal history and health information, and emotion recognition technology means for collecting and analyzing the user's emotional data. This enables detailed meal suggestions that take into account the user's emotional state, and helps the user select restaurants and menus more quickly and appropriately.

[0154] "Location information" refers to geographical data that indicates the user's current location and is fundamental information for identifying food and beverage establishments that the user can access.

[0155] "Meal history" refers to data about meals a user has consumed in the past, and serves as basic information for analyzing the user's food preferences and tastes.

[0156] "Health information" refers to data about the user's health status and serves as basic information for providing dietary suggestions that take into account restrictions on the intake of specific nutrients and foods.

[0157] "Emotional data" refers to data that indicates the user's current emotional state and serves as fundamental information for providing meal suggestions that meet the user's psychological needs.

[0158] "Proposed means" refers to processes and technologies for presenting meal menus and dining facilities suitable for the user based on analyzed data.

[0159] "Reservation information" refers to data used to secure a seat and time for a meal at a restaurant selected by the user, and is a service designed to improve the user experience.

[0160] "Feedback" refers to information about user satisfaction with meals and suggestions, as well as changes in emotions, and is important data for improving the accuracy of the system's suggestions.

[0161] This invention is a system that integrates and analyzes various data to optimize the user's dining experience. The system consists of three elements: a server, a terminal, and the user.

[0162] The server uses common location services to obtain the user's location information. For example, it uses a map service API to obtain the user's current latitude and longitude. Based on this information, it identifies nearby affiliated restaurants and bars from its database. The server also refers to a database that holds the user's eating history and health information, and uses information processing technology to analyze the user's preferences and dietary restrictions. This analysis uses machine learning algorithms, for example, to predict past menu preferences based on the user's eating history.

[0163] Furthermore, the device incorporates emotion recognition technology, collecting user emotional data through the smartwatch and built-in camera. This data is sent to a server and analyzed by an emotion engine. The identified emotional state is reflected in meal suggestions, offering menus tailored to situations where the user wants to relax or needs energy.

[0164] For example, if a user is looking for something to relax after work, the menu displayed on the device might include "herbal tea and risotto." Based on this suggestion, the user can book a restaurant through the device. If a delivery service is selected, the device tracks the delivery status in real time and displays it to the user.

[0165] Through this process, the server and terminal provide the user with the optimal dining experience. Users can report their satisfaction with the suggestions and changes in their feelings through feedback. This feedback is used by the server to improve the suggestion algorithm and further personalize future suggestions.

[0166] An example of a prompt for a generative AI model is, "Please suggest relaxing dishes that would be suitable for a user who is feeling stressed." By using this prompt, the AI ​​model generates suggestions that meet the user's needs.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server obtains the user's location information. It receives a location request from the device as input and uses a map service API to obtain the current latitude and longitude. This allows it to identify nearby affiliated restaurants and bars from its database. Specifically, the server issues a request to the API and performs a database search based on the received latitude and longitude.

[0170] Step 2:

[0171] The server retrieves the user's meal history and health information from a database. It uses the user ID as input to access the information from the database. The retrieved data is analyzed using information processing technology, such as machine learning algorithms, to predict the user's preferences and dietary restrictions. Specifically, the server generates a model based on the user's history to identify previously preferred menu items and ingredients to avoid.

[0172] Step 3:

[0173] The device collects user emotional data from the smartwatch and built-in camera. It senses vital data (heart rate, facial expression analysis data, etc.) as input and analyzes the user's emotional state using emotion recognition technology. As output, it sends the analysis results to a server. Specifically, the device locally analyzes the data collected from sensors, determines a specific emotional state (e.g., stress, exhilaration), and sends the result to the server.

[0174] Step 4:

[0175] The server integrates location information, meal history, health information, and sentiment data, and uses a generative AI model to suggest the optimal meal menu and dining establishment. Using this data as input, it provides prompts to the generative AI model, which then generates a list of the most appropriate menus and dining establishments as output. Specifically, the server activates the AI ​​based on the prompts, generates multiple candidates, and sends them to the terminal.

[0176] Step 5:

[0177] The terminal presents the user with suggestions received from the server. It receives data from the server as input and displays it to the user through the terminal's user interface. As output, it enables the user to make selections and reservations. Specifically, the terminal lists menus and dining establishments on the screen, and the reservation process begins when a selection button is pressed.

[0178] Step 6:

[0179] The user enters feedback into the device after the meal. The input includes reporting specific satisfaction levels and emotional changes, and the device sends this feedback to the server. The output is that this feedback is used to improve the proposed algorithm. Specifically, the user enters their evaluation through a feedback form, and the device sends and stores this information on the server.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] In modern society, users need to make a wide variety of food choices, but managing their eating history, health information, and emotional state while selecting the optimal meal is not easy. Furthermore, there are many challenges in efficiently selecting appropriate restaurants and managing the entire process from ordering to delivery. In response, there is a need for a means to optimize the dining experience by providing detailed meal suggestions tailored to each user's individual circumstances.

[0183] 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.

[0184] In this invention, the server includes means for acquiring the user's location information, artificial intelligence means for analyzing the user's eating history and health information, means for suggesting a meal menu suitable for the user based on the analysis results, and means for further optimizing the meal menu based on the user's emotional state. This makes it possible to provide meal suggestions tailored to the user's individual health and emotional state, as well as an efficient dining experience.

[0185] "Means for obtaining user location information" refers to technologies that determine the geographical location of a user.

[0186] "Artificial intelligence methods" refer to technologies that use computers to mimic human intellectual activity, analyzing dietary history and health information to identify patterns and trends.

[0187] "Methods for suggesting meal menus" refers to a function that presents meal options suitable for the user based on analysis results.

[0188] "Methods for further optimizing meal menus based on emotional state" refers to a function that analyzes the user's emotional data and selects the meal content that is most appropriate for that state.

[0189] "Methods for collecting user feedback and improving the accuracy of suggestions" refers to the process of compiling user feedback and opinions to improve the quality of future meal suggestions.

[0190] "Means of tracking delivery status" refers to a feature that allows you to check in real time where your ordered meal is currently located and when it will arrive.

[0191] "Means of checking nutritional and allergy information" refers to technology that allows users to view information about the nutritional components contained in the meals provided and allergens they should avoid.

[0192] This invention is an advanced system for optimizing the user's dining experience. It mainly consists of three components: a server, a terminal, and the user. This system focuses on efficiently integrating the user's location information, emotional state, meal history, and health information to provide personalized meals.

[0193] First, the server uses location services to determine the user's current location and lists partner restaurants near the user. Next, it analyzes past dining history and health information using artificial intelligence to recognize the user's preferences and health constraints. To achieve this, the server uses a cloud environment with advanced data processing and analysis capabilities to perform rapid processing.

[0194] Subsequently, an emotion engine is used to acquire the user's emotional state from the device, and this data is analyzed to generate the optimal menu. This emotion engine is responsible for collecting emotional data using sensors on devices such as smartphones and smartwatches, and transmitting it to the server.

[0195] The terminal visually displays meal suggestions from the server to the user, allowing them to reserve or order the suggested menu items. Furthermore, for those requesting delivery, the service improves efficiency by tracking the delivery status of orders in real time.

[0196] A concrete example of this system is suggesting relaxing herbal tea or a healthy salad to users who are feeling stressed after work. This allows users to receive necessary nutrition while also receiving emotional support.

[0197] A concrete example of a prompt message for a generative AI model would be, "I'm tired from work, so please suggest a relaxing meal. I've been paying attention to my health lately, so I'd like a menu with less salt." By responding to such requests from users, the system can continuously provide personalized services.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server obtains the user's location information. Using location services, it receives GPS data as input to determine the user's current location. Based on this information, it generates a list of nearby restaurants accessible to the user.

[0201] Step 2:

[0202] The server retrieves past meal history and health information from a database. This information is then used as input and analyzed using artificial intelligence (AI). The analysis identifies foods that should be avoided based on the user's food preferences and health, and prepares for meal suggestions.

[0203] Step 3:

[0204] The device uses an emotion engine to detect the user's emotional state. It receives heart rate and facial expression data from the smart device's sensors as input, and uses an emotion analysis model to determine the user's current emotional state.

[0205] Step 4:

[0206] The server takes the analysis results from step 2 and the emotional state from step 3 as input, integrates the data, and optimizes the meal menu. Using an AI model, it selects the meal menu best suited to the user's current health condition and emotions, and creates a list of suggestions.

[0207] Step 5:

[0208] The terminal displays a list of suggestions sent from the server to the user. Through the user interface, detailed information about each menu item is visually displayed, allowing the user to select, adjust, or order their desired meal.

[0209] Step 6:

[0210] Users provide feedback on their selected menu items via their device. This feedback, including changes in emotions and satisfaction levels, is recorded and sent to the server to improve the accuracy of future recommendations. This process enhances personalized meal recommendations tailored to each user.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention is a system that utilizes location information, meal history, and health information to optimize an individual's dining experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0228] The server has the ability to determine the user's current location based on the received location information. This allows the server to easily obtain information about nearby restaurants accessible to the user. The server also analyzes the user's eating history and health information, and generates appropriate meal menus considering the user's preferences and allergy information. For example, if a user has previously preferred a lot of Japanese food and has allergies to certain ingredients, new menu suggestions will take this information into account.

[0229] The terminal functions as a platform that presents information transmitted from the server to the user. Users can review the suggested menus through the terminal and make adjustments as needed. After selecting a menu, the terminal displays reservation information to the user in real time, allowing them to efficiently check restaurant availability and make reservations.

[0230] Users select suggested menu items using their devices and provide feedback. For example, they can provide feedback to the system by entering their satisfaction level after the meal and details of any newly discovered allergies. This feedback is collected on the server and used as training data for the AI. This improves the accuracy of future suggestions and enables the provision of more personalized services.

[0231] This invention supports users in selecting restaurants and deciding on menus based on their individual needs, thereby realizing an efficient and healthy dining experience.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The server retrieves the user's location information and compares it with coordinate data in the database to determine the user's current location. Based on this information, it lists nearby partner restaurants.

[0235] Step 2:

[0236] The server retrieves the user's dietary history and health information from a database and analyzes it. Based on the analysis results, it generates a list of foods the user likes and ingredients to avoid.

[0237] Step 3:

[0238] Based on the restaurant menu information identified in Step 1, the server uses AI to generate the optimal menu that matches the analyzed user profile.

[0239] Step 4:

[0240] The terminal displays the optimal menu and restaurant information received from the server to the user. The user reviews the suggested menu and makes adjustments to the dishes and allergy information as needed.

[0241] Step 5:

[0242] Users select their preferred menu items and use the terminal to make reservations and pre-order their meals at the corresponding restaurant. The terminal also allows users to check real-time availability information.

[0243] Step 6:

[0244] If the user selects home delivery, the server integrates with the delivery service and provides a mechanism to order the selected menu items. The terminal tracks the delivery status in real time and notifies the user of the estimated delivery arrival time.

[0245] Step 7:

[0246] Users provide feedback using a device after their meal. They input their satisfaction level with the meal and any new health information, and send it to the server.

[0247] Step 8:

[0248] The server stores user feedback information in a database and uses it as training data for the AI ​​model to improve the accuracy of future suggestions.

[0249] (Example 1)

[0250] 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."

[0251] In modern society, suggesting meals based on individual user preferences and health conditions, and selecting appropriate restaurants, is crucial for maintaining health and improving dining satisfaction. However, conventional systems have been unable to effectively utilize individual health and location information, making it difficult to suggest optimal meals. Furthermore, there has been a challenge in accumulating feedback on selected menus and incorporating it into future suggestions.

[0252] 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.

[0253] In this invention, the server includes means for acquiring data to confirm the user's current location, machine learning means for analyzing the user's meal history and health status data, and recommendation means for suggesting meal menus suitable for the user based on the analysis results. This makes it possible to provide optimal meal menus tailored to the individual needs of each user and to improve the accuracy of suggestions based on feedback.

[0254] "Data used to determine the user's current location" refers to digital data that includes location information such as latitude and longitude to identify the user's location.

[0255] "Machine learning methods" are data analysis techniques that use large amounts of data to enable algorithms to identify patterns and make predictions.

[0256] A "recommended method" is a method or system for presenting the user with the optimal choice based on the analyzed results.

[0257] An "information generation means" is a function for creating appropriate information based on specific circumstances and providing it to the user.

[0258] A "data collection method" is a system that efficiently collects and stores user feedback and other inputs to be used for subsequent analysis.

[0259] An "electronic system" is an aggregate of electronic devices and related software that provides functionality.

[0260] A "delivery service" is a logistics and management system for delivering selected products to a location specified by the user.

[0261] "Nutritional composition" refers to information about the types and amounts of each nutrient contained in a particular food or menu item.

[0262] "Display means" refers to devices or technologies for visually presenting information to a user in digital or analog format.

[0263] This invention is an electronic system that optimizes the dining experience using the user's location information, meal history, and health information. The system consists of a server, a terminal, and a user.

[0264] The server utilizes a digital device equipped with a GPS module to obtain location information from the user's terminal. Based on this location information, the server uses a database management system (DBMS) to search for restaurant information around the user's current location. Furthermore, it applies machine learning algorithms and uses data analysis tools such as Python and R to comprehensively analyze the user's past eating history and health information. Based on this analysis, a generative AI model is used to generate the most suitable meal menu for each individual user in real time.

[0265] The terminal visually displays suggested meal menus sent from the server to the user. This display uses an application that runs on a smartphone or tablet. The user reviews the suggested menu through the terminal and makes adjustments based on allergy information and preferences if necessary. After the menu is finalized, the terminal provides the user with information on available seats and reservation status at the restaurant. This allows the user to make reservations efficiently.

[0266] After the meal, users input their satisfaction level and any newly discovered allergies using a feedback form provided. This feedback is sent to the server via the device and stored again as training data for the algorithm. This allows the server to improve the accuracy of future suggestions and propose more individually optimized menus.

[0267] As a concrete example of its use, users can enter prompt messages. For instance, if a user uses the prompt, "I'd like Japanese food for our next date. I don't like spicy food, so please suggest menu items that exclude it," the system will suggest Japanese restaurants that meet those criteria, improving the user's dining experience.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The server receives location information from the user's terminal. This input data is latitude and longitude information indicating the user's current location. The server uses this information to query a database management system (DBMS) and outputs information about restaurants near the user.

[0271] Step 2:

[0272] The server retrieves the user's meal history and health information. This data includes past eating habits and allergy information. The server utilizes machine learning models to analyze this input data, generating meal menus based on the user's preferences and listing suggested options as output.

[0273] Step 3:

[0274] The server sends the generated menu suggestions to the device. The device displays the suggested menus to the user on the application screen. The user scrolls and taps on the device to view detailed menu information. This improves the convenience of selecting from the suggested options.

[0275] Step 4:

[0276] The user reviews the suggested menu through their terminal and selects their desired menu item. This input is sent to the server, which retrieves the restaurant's reservation information in real time and outputs available reservation dates and times.

[0277] Step 5:

[0278] The server registers restaurant reservations based on the menu selected by the user. This involves updating a database that manages reservation confirmations and seating availability. Reservation completion information is sent to the terminal and displayed for the user to visually confirm.

[0279] Step 6:

[0280] After a meal, users provide feedback on their satisfaction level and any new allergy information using a device. The user's input data is sent to a server and stored as training data for the generated AI model. The goal is to improve the accuracy of future menu suggestions.

[0281] In this way, a series of processes are realized that provide the optimal dining experience tailored to the individual needs of each user.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] In the conventional food delivery service, it is difficult to provide an optimal meal proposal based on individual health information and dietary preferences of users, and it is impossible to provide an efficient and healthy consumption experience. Also, the reservation procedure and the ordering procedure are complicated and cumbersome for users. Furthermore, the tracking of the delivery status is insufficient, and the environment in which users can wait for orders with confidence is not established.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for acquiring the position information of the user, calculation model means for analyzing the meal history and health information of the user, and means for making a meal proposal suitable for the user based on the analyzed result. As a result, it becomes possible to efficiently provide an optimized menu for each user, and to enable the proposal of meals that match the health status and preferences of the user and efficient ordering.

[0287] The "means for acquiring the position information of the user" is a technology or device necessary for specifying the current location of the user, and generally uses GPS or Wi-Fi signals to grasp the position.

[0288] The "calculation model means for analyzing the meal history and health information of the user" is an algorithm or software for collecting and analyzing data on the past meal selections and health status of the user, and is used to generate an optimal meal proposal for the user.

[0289] "Means of providing meal suggestions suitable for the user based on analysis results" refers to technologies and devices that present meal options that match the user's preferences and health condition based on data obtained through analysis.

[0290] "Means of providing reservation information for dining establishments" refers to technology or devices that query reservation availability information for dining establishments selected by the user and communicate the availability and reservation status to the user.

[0291] "Means for collecting user feedback and improving the accuracy of suggestions" refers to technologies and devices used to acquire user feedback as data, improve the system's learning process based on that information, and enhance the quality of future suggestions.

[0292] "Methods for quickly ordering optimized menus through delivery services" refer to technologies and devices used to efficiently order suggested meal menus using delivery services and to have meals delivered at the time desired by the user.

[0293] "Methods for tracking delivery status in real time" refer to technologies and devices that allow users to check the current location of their ordered meals and provide them with the latest delivery information.

[0294] "Means for checking nutritional information and allergy information" refers to technologies and devices that allow users to easily check the nutrients included in suggested menus and information related to their own allergies.

[0295] To realize this invention, we will specifically explain how each element—the server, terminal, and user—functions.

[0296] The server is equipped with hardware to acquire the user's location information using GPS or Wi-Fi signals. Next, the server utilizes artificial intelligence as a computational model to analyze the user's past eating history and health information. This AI model is built using Python libraries and data analysis tools. Based on the analyzed data, it generates meal suggestions tailored to the user. As an example of a suggestion, the server will find restaurants that offer low-calorie Japanese food for a user who prefers such menus.

[0297] The terminal displays information about meal menus and dining locations sent from the server to the user via a dedicated mobile application. This application is developed using Android and iOS frameworks (e.g., Flutter and React Native). The terminal displays real-time tracking information on the reservation status and delivery status of the dining location selected by the user. Users can also check nutritional information and allergy information for the menu via the terminal.

[0298] Users select meal options and provide feedback based on information provided through their devices. This data is sent to the server to improve the system's accuracy. User feedback plays a crucial role in suggesting new meal options. For example, if a user particularly likes a menu item, that information will be reflected in future suggestions.

[0299] As a concrete example, a user might make a request via a prompt message such as, "I'm currently in Shibuya, and I'm on a low-sugar diet, so please prioritize displaying low-carb menus. I prefer Japanese food, so please suggest restaurants that match that." Based on this, the server analyzes the request and suggests menus that meet the criteria.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The server obtains the user's location information using GPS. As input, there is location data sent from the user's smartphone. This data is analyzed to identify the user's current location. As output, the identified geographical coordinates are obtained.

[0303] Step 2:

[0304] The server obtains the user's dietary history and health information from the database. Using this data as input, analysis is performed using a calculation model. Through this analysis, the user's dietary preferences and health status can be grasped. As output, a user profile based on this information is generated.

[0305] Step 3:

[0306] The server uses the above location information and profile as input, refers to the database of nearby food and beverage places, and selects a suitable meal menu. Here, an AI model is used to list options that suit the user's preferences and restrictions. As output, a list of multiple meal menus suitable for the user and the food and beverage places that offer them is obtained.

[0307] Step 4:

[0308] The server sends the selected menu and food and beverage place information to the terminal. The terminal displays this on the user interface. As input, it is the menu information and food and beverage place information received from the server. As output, this information is visually presented to the user. The user can check the details of the menu and the reservation status.

[0309] Step 5:

[0310] The user selects a menu item based on the presented information and sends feedback from the terminal. The input consists of the user's menu selection and feedback. This data is sent to the server and used to improve the accuracy of future meal recommendations. The output generates newly learned user preference data.

[0311] Step 6:

[0312] The server makes restaurant reservations or delivery arrangements based on the user's selection. Input consists of the user's menu selection and reservation request. The server then refers to a database to make the reservation or delivery arrangement. The output generates reservation confirmation information or delivery tracking information.

[0313] Step 7:

[0314] The terminal notifies the user of reservation confirmation information or delivery status. The input is reservation or delivery information received from the server. The terminal displays this on the user interface and provides output that allows the user to visually confirm this information.

[0315] 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.

[0316] This invention is a system that utilizes location information, meal history, health information, and emotional information to optimize the user's dining experience. This system consists of three elements: a server, a terminal, and a user, and is equipped with an emotional engine.

[0317] The server first obtains the user's current location from location services and identifies nearby partner restaurants. Based on this location information, it creates a list of restaurants the user can access. Furthermore, it retrieves past dining history and health information from a database and analyzes it using artificial intelligence. The results of this analysis allow the server to understand the user's food preferences and any dietary restrictions they should avoid.

[0318] Next, the emotion engine analyzes the user's emotional data acquired by the device (for example, data obtained from a smartwatch or the device's built-in camera). The emotion engine then works with a server to suggest the most suitable menu or restaurant based on the user's emotional state. For example, if the user is feeling stressed, the system can select a menu that includes beverages and dishes with relaxing effects.

[0319] The terminal functions as a platform that presents users with menu suggestions and restaurant information sent from the server. Users can review and adjust suggested menus, make reservations, and place orders through the terminal. If a delivery option is selected, real-time tracking information of the delivery status is also provided.

[0320] Users can input feedback into their terminal after selecting, booking, and ordering a meal, reporting their satisfaction level and changes in their emotions regarding the meal. Based on the collected feedback and emotional data, the server can improve the accuracy of the system and further personalize future recommendations.

[0321] Thus, the present invention realizes meticulous meal suggestions that respond to the user's emotions, and provides a service that is attentive to the user's health and emotional needs.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The server obtains the user's location information and determines their current location. Based on this location information, it lists nearby partner restaurants.

[0325] Step 2:

[0326] The server retrieves the user's dietary history and health information from a database and analyzes it using artificial intelligence. Through this analysis, it profiles the user's preferences and limitations.

[0327] Step 3:

[0328] The device uses cameras and sensors to measure the user's emotions in real time. The obtained emotion data is then input into an emotion engine.

[0329] Step 4:

[0330] The emotion engine analyzes the user's emotional state and sends that information to the server. The server integrates the emotional information with the user's dietary profile and suggests the optimal meal plan.

[0331] Step 5:

[0332] The terminal displays menus and restaurant information provided by the server to the user. The user reviews and adjusts the menu items via the terminal and approves their selection.

[0333] Step 6:

[0334] The user makes a reservation at their chosen restaurant and orders their menu through the terminal. The terminal checks and displays availability and reservation status to the user.

[0335] Step 7:

[0336] If home delivery is selected, the server will coordinate with the delivery service to enable completion of the delivery process and tracking of the delivery status. The terminal will provide the user with real-time information on the delivery time.

[0337] Step 8:

[0338] After the meal, users use their device to input feedback, sending their satisfaction level, emotional changes, and new health information to the server.

[0339] Step 9:

[0340] The server stores the collected feedback and sentiment data in a database and optimizes future suggestions by updating the AI ​​model.

[0341] (Example 2)

[0342] 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".

[0343] Conventional meal recommendation systems often only consider the user's past eating history and health information, making it difficult to provide appropriate recommendations that reflect the user's emotional state. Furthermore, users often experience stress and wasted time when choosing which restaurant to eat at. Additionally, the limited use of feedback hindered improvements in the system's recommendation accuracy.

[0344] 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.

[0345] In this invention, the server includes means for acquiring the user's location information and identifying nearby affiliated restaurants and bars, information processing technology means for analyzing the user's meal history and health information, and emotion recognition technology means for collecting and analyzing the user's emotional data. This enables detailed meal suggestions that take into account the user's emotional state, and helps the user select restaurants and menus more quickly and appropriately.

[0346] "Location information" refers to geographical data that indicates the user's current location and is fundamental information for identifying food and beverage establishments that the user can access.

[0347] "Meal history" refers to data about meals a user has consumed in the past, and serves as basic information for analyzing the user's food preferences and tastes.

[0348] "Health information" refers to data about the user's health status and serves as basic information for providing dietary suggestions that take into account restrictions on the intake of specific nutrients and foods.

[0349] "Emotional data" refers to data that indicates the user's current emotional state and serves as fundamental information for providing meal suggestions that meet the user's psychological needs.

[0350] "Proposed means" refers to processes and technologies for presenting meal menus and dining facilities suitable for the user based on analyzed data.

[0351] "Reservation information" refers to data used to secure a seat and time for a meal at a restaurant selected by the user, and is a service designed to improve the user experience.

[0352] "Feedback" refers to information about user satisfaction with meals and suggestions, as well as changes in emotions, and is important data for improving the accuracy of the system's suggestions.

[0353] This invention is a system that integrates and analyzes various data to optimize the user's dining experience. The system consists of three elements: a server, a terminal, and the user.

[0354] The server uses common location services to obtain the user's location information. For example, it uses a map service API to obtain the user's current latitude and longitude. Based on this information, it identifies nearby affiliated restaurants and bars from its database. The server also refers to a database that holds the user's eating history and health information, and uses information processing technology to analyze the user's preferences and dietary restrictions. This analysis uses machine learning algorithms, for example, to predict past menu preferences based on the user's eating history.

[0355] Furthermore, the device incorporates emotion recognition technology, collecting user emotional data through the smartwatch and built-in camera. This data is sent to a server and analyzed by an emotion engine. The identified emotional state is reflected in meal suggestions, offering menus tailored to situations where the user wants to relax or needs energy.

[0356] For example, if a user is looking for something to relax after work, the menu displayed on the device might include "herbal tea and risotto." Based on this suggestion, the user can book a restaurant through the device. If a delivery service is selected, the device tracks the delivery status in real time and displays it to the user.

[0357] Through this process, the server and terminal provide the user with the optimal dining experience. Users can report their satisfaction with the suggestions and changes in their feelings through feedback. This feedback is used by the server to improve the suggestion algorithm and further personalize future suggestions.

[0358] An example of a prompt for a generative AI model is, "Please suggest relaxing dishes that would be suitable for a user who is feeling stressed." By using this prompt, the AI ​​model generates suggestions that meet the user's needs.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The server obtains the user's location information. It receives a location request from the device as input and uses a map service API to obtain the current latitude and longitude. This allows it to identify nearby affiliated restaurants and bars from its database. Specifically, the server issues a request to the API and performs a database search based on the received latitude and longitude.

[0362] Step 2:

[0363] The server retrieves the user's meal history and health information from a database. It uses the user ID as input to access the information from the database. The retrieved data is analyzed using information processing technology, such as machine learning algorithms, to predict the user's preferences and dietary restrictions. Specifically, the server generates a model based on the user's history to identify previously preferred menu items and ingredients to avoid.

[0364] Step 3:

[0365] The device collects user emotional data from the smartwatch and built-in camera. It senses vital data (heart rate, facial expression analysis data, etc.) as input and analyzes the user's emotional state using emotion recognition technology. As output, it sends the analysis results to a server. Specifically, the device locally analyzes the data collected from sensors, determines a specific emotional state (e.g., stress, exhilaration), and sends the result to the server.

[0366] Step 4:

[0367] The server integrates location information, meal history, health information, and sentiment data, and uses a generative AI model to suggest the optimal meal menu and dining establishment. Using this data as input, it provides prompts to the generative AI model, which then generates a list of the most appropriate menus and dining establishments as output. Specifically, the server activates the AI ​​based on the prompts, generates multiple candidates, and sends them to the terminal.

[0368] Step 5:

[0369] The terminal presents the user with suggestions received from the server. It receives data from the server as input and displays it to the user through the terminal's user interface. As output, it enables the user to make selections and reservations. Specifically, the terminal lists menus and dining establishments on the screen, and the reservation process begins when a selection button is pressed.

[0370] Step 6:

[0371] The user enters feedback into the device after the meal. The input includes reporting specific satisfaction levels and emotional changes, and the device sends this feedback to the server. The output is that this feedback is used to improve the proposed algorithm. Specifically, the user enters their evaluation through a feedback form, and the device sends and stores this information on the server.

[0372] (Application Example 2)

[0373] 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."

[0374] In modern society, users need to make a wide variety of food choices, but managing their eating history, health information, and emotional state while selecting the optimal meal is not easy. Furthermore, there are many challenges in efficiently selecting appropriate restaurants and managing the entire process from ordering to delivery. In response, there is a need for a means to optimize the dining experience by providing detailed meal suggestions tailored to each user's individual circumstances.

[0375] 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.

[0376] In this invention, the server includes means for acquiring the user's location information, artificial intelligence means for analyzing the user's eating history and health information, means for suggesting a meal menu suitable for the user based on the analysis results, and means for further optimizing the meal menu based on the user's emotional state. This makes it possible to provide meal suggestions tailored to the user's individual health and emotional state, as well as an efficient dining experience.

[0377] "Means for obtaining user location information" refers to technologies that determine the geographical location of a user.

[0378] "Artificial intelligence methods" refer to technologies that use computers to mimic human intellectual activity, analyzing dietary history and health information to identify patterns and trends.

[0379] "Methods for suggesting meal menus" refers to a function that presents meal options suitable for the user based on analysis results.

[0380] "Methods for further optimizing meal menus based on emotional state" refers to a function that analyzes the user's emotional data and selects the meal content that is most appropriate for that state.

[0381] "Methods for collecting user feedback and improving the accuracy of suggestions" refers to the process of compiling user feedback and opinions to improve the quality of future meal suggestions.

[0382] "Means of tracking delivery status" refers to a feature that allows you to check in real time where your ordered meal is currently located and when it will arrive.

[0383] "Means of checking nutritional and allergy information" refers to technology that allows users to view information about the nutritional components contained in the meals provided and allergens they should avoid.

[0384] This invention is an advanced system for optimizing the user's dining experience. It mainly consists of three components: a server, a terminal, and the user. This system focuses on efficiently integrating the user's location information, emotional state, meal history, and health information to provide personalized meals.

[0385] First, the server uses location services to determine the user's current location and lists partner restaurants near the user. Next, it analyzes past dining history and health information using artificial intelligence to recognize the user's preferences and health constraints. To achieve this, the server uses a cloud environment with advanced data processing and analysis capabilities to perform rapid processing.

[0386] Subsequently, an emotion engine is used to acquire the user's emotional state from the device, and this data is analyzed to generate the optimal menu. This emotion engine is responsible for collecting emotional data using sensors on devices such as smartphones and smartwatches, and transmitting it to the server.

[0387] The terminal visually displays meal suggestions from the server to the user, allowing them to reserve or order the suggested menu items. Furthermore, for those requesting delivery, the service improves efficiency by tracking the delivery status of orders in real time.

[0388] A concrete example of this system is suggesting relaxing herbal tea or a healthy salad to users who are feeling stressed after work. This allows users to receive necessary nutrition while also receiving emotional support.

[0389] A concrete example of a prompt message for a generative AI model would be, "I'm tired from work, so please suggest a relaxing meal. I've been paying attention to my health lately, so I'd like a menu with less salt." By responding to such requests from users, the system can continuously provide personalized services.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The server obtains the user's location information. Using location services, it receives GPS data as input to determine the user's current location. Based on this information, it generates a list of nearby restaurants accessible to the user.

[0393] Step 2:

[0394] The server retrieves past meal history and health information from a database. This information is then used as input and analyzed using artificial intelligence (AI). The analysis identifies foods that should be avoided based on the user's food preferences and health, and prepares for meal suggestions.

[0395] Step 3:

[0396] The device uses an emotion engine to detect the user's emotional state. It receives heart rate and facial expression data from the smart device's sensors as input, and uses an emotion analysis model to determine the user's current emotional state.

[0397] Step 4:

[0398] The server takes the analysis results from step 2 and the emotional state from step 3 as input, integrates the data, and optimizes the meal menu. Using an AI model, it selects the meal menu best suited to the user's current health condition and emotions, and creates a list of suggestions.

[0399] Step 5:

[0400] The terminal displays a list of suggestions sent from the server to the user. Through the user interface, detailed information about each menu item is visually displayed, allowing the user to select, adjust, or order their desired meal.

[0401] Step 6:

[0402] Users provide feedback on their selected menu items via their device. This feedback, including changes in emotions and satisfaction levels, is recorded and sent to the server to improve the accuracy of future recommendations. This process enhances personalized meal recommendations tailored to each user.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This invention is a system that utilizes location information, meal history, and health information to optimize an individual's dining experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0420] The server has the ability to determine the user's current location based on the received location information. This allows the server to easily obtain information about nearby restaurants accessible to the user. The server also analyzes the user's eating history and health information, and generates appropriate meal menus considering the user's preferences and allergy information. For example, if a user has previously preferred a lot of Japanese food and has allergies to certain ingredients, new menu suggestions will take this information into account.

[0421] The terminal functions as a platform that presents information transmitted from the server to the user. Users can review the suggested menus through the terminal and make adjustments as needed. After selecting a menu, the terminal displays reservation information to the user in real time, allowing them to efficiently check restaurant availability and make reservations.

[0422] Users select suggested menu items using their devices and provide feedback. For example, they can provide feedback to the system by entering their satisfaction level after the meal and details of any newly discovered allergies. This feedback is collected on the server and used as training data for the AI. This improves the accuracy of future suggestions and enables the provision of more personalized services.

[0423] This invention supports users in selecting restaurants and deciding on menus based on their individual needs, thereby realizing an efficient and healthy dining experience.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The server retrieves the user's location information and compares it with coordinate data in the database to determine the user's current location. Based on this information, it lists nearby partner restaurants.

[0427] Step 2:

[0428] The server retrieves the user's dietary history and health information from a database and analyzes it. Based on the analysis results, it generates a list of foods the user likes and ingredients to avoid.

[0429] Step 3:

[0430] Based on the restaurant menu information identified in Step 1, the server uses AI to generate the optimal menu that matches the analyzed user profile.

[0431] Step 4:

[0432] The terminal displays the optimal menu and restaurant information received from the server to the user. The user reviews the suggested menu and makes adjustments to the dishes and allergy information as needed.

[0433] Step 5:

[0434] Users select their preferred menu items and use the terminal to make reservations and pre-order their meals at the corresponding restaurant. The terminal also allows users to check real-time availability information.

[0435] Step 6:

[0436] If the user selects home delivery, the server integrates with the delivery service and provides a mechanism to order the selected menu items. The terminal tracks the delivery status in real time and notifies the user of the estimated delivery arrival time.

[0437] Step 7:

[0438] Users provide feedback using a device after their meal. They input their satisfaction level with the meal and any new health information, and send it to the server.

[0439] Step 8:

[0440] The server stores user feedback information in a database and uses it as training data for the AI ​​model to improve the accuracy of future suggestions.

[0441] (Example 1)

[0442] 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."

[0443] In modern society, suggesting meals based on individual user preferences and health conditions, and selecting appropriate restaurants, is crucial for maintaining health and improving dining satisfaction. However, conventional systems have been unable to effectively utilize individual health and location information, making it difficult to suggest optimal meals. Furthermore, there has been a challenge in accumulating feedback on selected menus and incorporating it into future suggestions.

[0444] 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.

[0445] In this invention, the server includes means for acquiring data to confirm the user's current location, machine learning means for analyzing the user's meal history and health status data, and recommendation means for suggesting meal menus suitable for the user based on the analysis results. This makes it possible to provide optimal meal menus tailored to the individual needs of each user and to improve the accuracy of suggestions based on feedback.

[0446] "Data used to determine the user's current location" refers to digital data that includes location information such as latitude and longitude to identify the user's location.

[0447] "Machine learning methods" are data analysis techniques that use large amounts of data to enable algorithms to identify patterns and make predictions.

[0448] A "recommended method" is a method or system for presenting the user with the optimal choice based on the analyzed results.

[0449] An "information generation means" is a function for creating appropriate information based on specific circumstances and providing it to the user.

[0450] A "data collection method" is a system that efficiently collects and stores user feedback and other inputs to be used for subsequent analysis.

[0451] An "electronic system" is an aggregate of electronic devices and related software that provides functionality.

[0452] A "delivery service" is a logistics and management system for delivering selected products to a location specified by the user.

[0453] "Nutritional composition" refers to information about the types and amounts of each nutrient contained in a particular food or menu item.

[0454] "Display means" refers to devices or technologies for visually presenting information to a user in digital or analog format.

[0455] This invention is an electronic system that optimizes the dining experience using the user's location information, meal history, and health information. The system consists of a server, a terminal, and a user.

[0456] The server utilizes a digital device equipped with a GPS module to obtain location information from the user's terminal. Based on this location information, the server uses a database management system (DBMS) to search for restaurant information around the user's current location. Furthermore, it applies machine learning algorithms and uses data analysis tools such as Python and R to comprehensively analyze the user's past eating history and health information. Based on this analysis, a generative AI model is used to generate the most suitable meal menu for each individual user in real time.

[0457] The terminal visually displays suggested meal menus sent from the server to the user. This display uses an application that runs on a smartphone or tablet. The user reviews the suggested menu through the terminal and makes adjustments based on allergy information and preferences if necessary. After the menu is finalized, the terminal provides the user with information on available seats and reservation status at the restaurant. This allows the user to make reservations efficiently.

[0458] After the meal, users input their satisfaction level and any newly discovered allergies using a feedback form provided. This feedback is sent to the server via the device and stored again as training data for the algorithm. This allows the server to improve the accuracy of future suggestions and propose more individually optimized menus.

[0459] As a concrete example of its use, users can enter prompt messages. For instance, if a user uses the prompt, "I'd like Japanese food for our next date. I don't like spicy food, so please suggest menu items that exclude it," the system will suggest Japanese restaurants that meet those criteria, improving the user's dining experience.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] The server receives location information from the user's terminal. This input data is latitude and longitude information indicating the user's current location. The server uses this information to query a database management system (DBMS) and outputs information about restaurants near the user.

[0463] Step 2:

[0464] The server retrieves the user's meal history and health information. This data includes past eating habits and allergy information. The server utilizes machine learning models to analyze this input data, generating meal menus based on the user's preferences and listing suggested options as output.

[0465] Step 3:

[0466] The server sends the generated menu suggestions to the device. The device displays the suggested menus to the user on the application screen. The user scrolls and taps on the device to view detailed menu information. This improves the convenience of selecting from the suggested options.

[0467] Step 4:

[0468] The user reviews the suggested menu through their terminal and selects their desired menu item. This input is sent to the server, which retrieves the restaurant's reservation information in real time and outputs available reservation dates and times.

[0469] Step 5:

[0470] The server registers restaurant reservations based on the menu selected by the user. This involves updating a database that manages reservation confirmations and seating availability. Reservation completion information is sent to the terminal and displayed for the user to visually confirm.

[0471] Step 6:

[0472] After a meal, users provide feedback on their satisfaction level and any new allergy information using a device. The user's input data is sent to a server and stored as training data for the generated AI model. The goal is to improve the accuracy of future menu suggestions.

[0473] In this way, a series of processes are realized that provide the optimal dining experience tailored to the individual needs of each user.

[0474] (Application Example 1)

[0475] 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."

[0476] Traditional food delivery services have struggled to provide optimal meal suggestions based on individual users' health information and dietary preferences, failing to offer an efficient and healthy consumption experience. Furthermore, the reservation and ordering processes were complex and cumbersome for users. In addition, insufficient tracking of delivery status meant that users could not wait for their orders with peace of mind.

[0477] 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.

[0478] In this invention, the server includes means for acquiring the user's location information, means for a computational model for analyzing the user's meal history and health information, and means for making meal suggestions suitable for the user based on the analysis results. This makes it possible to efficiently provide menus optimized for each user, suggest meals that match the user's health condition and preferences, and enable efficient ordering.

[0479] "Means for obtaining user location information" refers to the technology or device necessary to determine the user's current location, and generally involves using GPS or Wi-Fi signals to determine the location.

[0480] "Computational model means for analyzing a user's dietary history and health information" refers to algorithms and software for collecting and analyzing data on a user's past dietary choices and health status, and is used to generate optimal dietary suggestions for the user.

[0481] "Means of providing meal suggestions suitable for the user based on analysis results" refers to technologies and devices that present meal options that match the user's preferences and health condition based on data obtained through analysis.

[0482] "Means of providing reservation information for dining establishments" refers to technology or devices that query reservation availability information for dining establishments selected by the user and communicate the availability and reservation status to the user.

[0483] "Means for collecting user feedback and improving the accuracy of suggestions" refers to technologies and devices used to acquire user feedback as data, improve the system's learning process based on that information, and enhance the quality of future suggestions.

[0484] "Methods for quickly ordering optimized menus through delivery services" refer to technologies and devices used to efficiently order suggested meal menus using delivery services and to have meals delivered at the time desired by the user.

[0485] "Methods for tracking delivery status in real time" refer to technologies and devices that allow users to check the current location of their ordered meals and provide them with the latest delivery information.

[0486] "Means for checking nutritional information and allergy information" refers to technologies and devices that allow users to easily check the nutrients included in suggested menus and information related to their own allergies.

[0487] To realize this invention, we will specifically explain how each element—the server, terminal, and user—functions.

[0488] The server is equipped with hardware to acquire the user's location information using GPS or Wi-Fi signals. Next, the server utilizes artificial intelligence as a computational model to analyze the user's past eating history and health information. This AI model is built using Python libraries and data analysis tools. Based on the analyzed data, it generates meal suggestions tailored to the user. As an example of a suggestion, the server will find restaurants that offer low-calorie Japanese food for a user who prefers such menus.

[0489] The terminal displays information about meal menus and dining locations sent from the server to the user via a dedicated mobile application. This application is developed using Android and iOS frameworks (e.g., Flutter and React Native). The terminal displays real-time tracking information on the reservation status and delivery status of the dining location selected by the user. Users can also check nutritional information and allergy information for the menu via the terminal.

[0490] Users select meal options and provide feedback based on information provided through their devices. This data is sent to the server to improve the system's accuracy. User feedback plays a crucial role in suggesting new meal options. For example, if a user particularly likes a menu item, that information will be reflected in future suggestions.

[0491] As a concrete example, a user might make a request via a prompt message such as, "I'm currently in Shibuya, and I'm on a low-sugar diet, so please prioritize displaying low-carb menus. I prefer Japanese food, so please suggest restaurants that match that." Based on this, the server analyzes the request and suggests menus that meet the criteria.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The server obtains the user's location information using GPS. The input is location data transmitted from the user's smartphone. This data is analyzed to determine the user's current location. The output is the identified geographic coordinates.

[0495] Step 2:

[0496] The server retrieves the user's dietary history and health information from a database. This data is then used as input for analysis using a computational model. This analysis allows for an understanding of the user's dietary preferences and health status. The output is a user profile based on this information.

[0497] Step 3:

[0498] The server uses the above location information and profile as input to refer to a database of nearby restaurants and selects a suitable meal menu. An AI model is used here to list options that match the user's preferences and restrictions. The output is a list of several suitable meal menus and the restaurants that offer them.

[0499] Step 4:

[0500] The server transmits information about the selected menu and dining location to the terminal. The terminal displays this information on its user interface. The input consists of the menu and dining location information received from the server. The output is a visual presentation of this information to the user. The user can check menu details and reservation status.

[0501] Step 5:

[0502] The user selects a menu item based on the presented information and sends feedback from the terminal. The input consists of the user's menu selection and feedback. This data is sent to the server and used to improve the accuracy of future meal recommendations. The output generates newly learned user preference data.

[0503] Step 6:

[0504] The server makes restaurant reservations or delivery arrangements based on the user's selection. Input consists of the user's menu selection and reservation request. The server then refers to a database to make the reservation or delivery arrangement. The output generates reservation confirmation information or delivery tracking information.

[0505] Step 7:

[0506] The terminal notifies the user of reservation confirmation information or delivery status. The input is reservation or delivery information received from the server. The terminal displays this on the user interface and provides output that allows the user to visually confirm this information.

[0507] 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.

[0508] This invention is a system that utilizes location information, meal history, health information, and emotional information to optimize the user's dining experience. This system consists of three elements: a server, a terminal, and a user, and is equipped with an emotional engine.

[0509] The server first obtains the user's current location from location services and identifies nearby partner restaurants. Based on this location information, it creates a list of restaurants the user can access. Furthermore, it retrieves past dining history and health information from a database and analyzes it using artificial intelligence. The results of this analysis allow the server to understand the user's food preferences and any dietary restrictions they should avoid.

[0510] Next, the emotion engine analyzes the user's emotional data acquired by the device (for example, data obtained from a smartwatch or the device's built-in camera). The emotion engine then works with a server to suggest the most suitable menu or restaurant based on the user's emotional state. For example, if the user is feeling stressed, the system can select a menu that includes beverages and dishes with relaxing effects.

[0511] The terminal functions as a platform that presents users with menu suggestions and restaurant information sent from the server. Users can review and adjust suggested menus, make reservations, and place orders through the terminal. If a delivery option is selected, real-time tracking information of the delivery status is also provided.

[0512] Users can input feedback into their terminal after selecting, booking, and ordering a meal, reporting their satisfaction level and changes in their emotions regarding the meal. Based on the collected feedback and emotional data, the server can improve the accuracy of the system and further personalize future recommendations.

[0513] Thus, the present invention realizes meticulous meal suggestions that respond to the user's emotions, and provides a service that is attentive to the user's health and emotional needs.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The server obtains the user's location information and determines their current location. Based on this location information, it lists nearby partner restaurants.

[0517] Step 2:

[0518] The server retrieves the user's dietary history and health information from a database and analyzes it using artificial intelligence. Through this analysis, it profiles the user's preferences and limitations.

[0519] Step 3:

[0520] The device uses cameras and sensors to measure the user's emotions in real time. The obtained emotion data is then input into an emotion engine.

[0521] Step 4:

[0522] The emotion engine analyzes the user's emotional state and sends that information to the server. The server integrates the emotional information with the user's dietary profile and suggests the optimal meal plan.

[0523] Step 5:

[0524] The terminal displays menus and restaurant information provided by the server to the user. The user reviews and adjusts the menu items via the terminal and approves their selection.

[0525] Step 6:

[0526] The user makes a reservation at their chosen restaurant and orders their menu through the terminal. The terminal checks and displays availability and reservation status to the user.

[0527] Step 7:

[0528] If home delivery is selected, the server will coordinate with the delivery service to enable completion of the delivery process and tracking of the delivery status. The terminal will provide the user with real-time information on the delivery time.

[0529] Step 8:

[0530] After the meal, users use their device to input feedback, sending their satisfaction level, emotional changes, and new health information to the server.

[0531] Step 9:

[0532] The server stores the collected feedback and sentiment data in a database and optimizes future suggestions by updating the AI ​​model.

[0533] (Example 2)

[0534] 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."

[0535] Conventional meal recommendation systems often only consider the user's past eating history and health information, making it difficult to provide appropriate recommendations that reflect the user's emotional state. Furthermore, users often experience stress and wasted time when choosing which restaurant to eat at. Additionally, the limited use of feedback hindered improvements in the system's recommendation accuracy.

[0536] 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.

[0537] In this invention, the server includes means for acquiring the user's location information and identifying nearby affiliated restaurants and bars, information processing technology means for analyzing the user's meal history and health information, and emotion recognition technology means for collecting and analyzing the user's emotional data. This enables detailed meal suggestions that take into account the user's emotional state, and helps the user select restaurants and menus more quickly and appropriately.

[0538] "Location information" refers to geographical data that indicates the user's current location and is fundamental information for identifying food and beverage establishments that the user can access.

[0539] "Meal history" refers to data about meals a user has consumed in the past, and serves as basic information for analyzing the user's food preferences and tastes.

[0540] "Health information" refers to data about the user's health status and serves as basic information for providing dietary suggestions that take into account restrictions on the intake of specific nutrients and foods.

[0541] "Emotional data" refers to data that indicates the user's current emotional state and serves as fundamental information for providing meal suggestions that meet the user's psychological needs.

[0542] "Proposed means" refers to processes and technologies for presenting meal menus and dining facilities suitable for the user based on analyzed data.

[0543] "Reservation information" refers to data used to secure a seat and time for a meal at a restaurant selected by the user, and is a service designed to improve the user experience.

[0544] "Feedback" refers to information about user satisfaction with meals and suggestions, as well as changes in emotions, and is important data for improving the accuracy of the system's suggestions.

[0545] This invention is a system that integrates and analyzes various data to optimize the user's dining experience. The system consists of three elements: a server, a terminal, and the user.

[0546] The server uses common location services to obtain the user's location information. For example, it uses a map service API to obtain the user's current latitude and longitude. Based on this information, it identifies nearby affiliated restaurants and bars from its database. The server also refers to a database that holds the user's eating history and health information, and uses information processing technology to analyze the user's preferences and dietary restrictions. This analysis uses machine learning algorithms, for example, to predict past menu preferences based on the user's eating history.

[0547] Furthermore, the device incorporates emotion recognition technology, collecting user emotional data through the smartwatch and built-in camera. This data is sent to a server and analyzed by an emotion engine. The identified emotional state is reflected in meal suggestions, offering menus tailored to situations where the user wants to relax or needs energy.

[0548] For example, if a user is looking for something to relax after work, the menu displayed on the device might include "herbal tea and risotto." Based on this suggestion, the user can book a restaurant through the device. If a delivery service is selected, the device tracks the delivery status in real time and displays it to the user.

[0549] Through this process, the server and terminal provide the user with the optimal dining experience. Users can report their satisfaction with the suggestions and changes in their feelings through feedback. This feedback is used by the server to improve the suggestion algorithm and further personalize future suggestions.

[0550] An example of a prompt for a generative AI model is, "Please suggest relaxing dishes that would be suitable for a user who is feeling stressed." By using this prompt, the AI ​​model generates suggestions that meet the user's needs.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The server obtains the user's location information. It receives a location request from the device as input and uses a map service API to obtain the current latitude and longitude. This allows it to identify nearby affiliated restaurants and bars from its database. Specifically, the server issues a request to the API and performs a database search based on the received latitude and longitude.

[0554] Step 2:

[0555] The server retrieves the user's meal history and health information from a database. It uses the user ID as input to access the information from the database. The retrieved data is analyzed using information processing technology, such as machine learning algorithms, to predict the user's preferences and dietary restrictions. Specifically, the server generates a model based on the user's history to identify previously preferred menu items and ingredients to avoid.

[0556] Step 3:

[0557] The device collects user emotional data from the smartwatch and built-in camera. It senses vital data (heart rate, facial expression analysis data, etc.) as input and analyzes the user's emotional state using emotion recognition technology. As output, it sends the analysis results to a server. Specifically, the device locally analyzes the data collected from sensors, determines a specific emotional state (e.g., stress, exhilaration), and sends the result to the server.

[0558] Step 4:

[0559] The server integrates location information, meal history, health information, and sentiment data, and uses a generative AI model to suggest the optimal meal menu and dining establishment. Using this data as input, it provides prompts to the generative AI model, which then generates a list of the most appropriate menus and dining establishments as output. Specifically, the server activates the AI ​​based on the prompts, generates multiple candidates, and sends them to the terminal.

[0560] Step 5:

[0561] The terminal presents the user with suggestions received from the server. It receives data from the server as input and displays it to the user through the terminal's user interface. As output, it enables the user to make selections and reservations. Specifically, the terminal lists menus and dining establishments on the screen, and the reservation process begins when a selection button is pressed.

[0562] Step 6:

[0563] The user enters feedback into the device after the meal. The input includes reporting specific satisfaction levels and emotional changes, and the device sends this feedback to the server. The output is that this feedback is used to improve the proposed algorithm. Specifically, the user enters their evaluation through a feedback form, and the device sends and stores this information on the server.

[0564] (Application Example 2)

[0565] 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."

[0566] In modern society, users need to make a wide variety of food choices, but managing their eating history, health information, and emotional state while selecting the optimal meal is not easy. Furthermore, there are many challenges in efficiently selecting appropriate restaurants and managing the entire process from ordering to delivery. In response, there is a need for a means to optimize the dining experience by providing detailed meal suggestions tailored to each user's individual circumstances.

[0567] 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.

[0568] In this invention, the server includes means for acquiring the user's location information, artificial intelligence means for analyzing the user's eating history and health information, means for suggesting a meal menu suitable for the user based on the analysis results, and means for further optimizing the meal menu based on the user's emotional state. This makes it possible to provide meal suggestions tailored to the user's individual health and emotional state, as well as an efficient dining experience.

[0569] "Means for obtaining user location information" refers to technologies that determine the geographical location of a user.

[0570] "Artificial intelligence methods" refer to technologies that use computers to mimic human intellectual activity, analyzing dietary history and health information to identify patterns and trends.

[0571] "Methods for suggesting meal menus" refers to a function that presents meal options suitable for the user based on analysis results.

[0572] "Methods for further optimizing meal menus based on emotional state" refers to a function that analyzes the user's emotional data and selects the meal content that is most appropriate for that state.

[0573] "Methods for collecting user feedback and improving the accuracy of suggestions" refers to the process of compiling user feedback and opinions to improve the quality of future meal suggestions.

[0574] "Means of tracking delivery status" refers to a feature that allows you to check in real time where your ordered meal is currently located and when it will arrive.

[0575] "Means of checking nutritional and allergy information" refers to technology that allows users to view information about the nutritional components contained in the meals provided and allergens they should avoid.

[0576] This invention is an advanced system for optimizing the user's dining experience. It mainly consists of three components: a server, a terminal, and the user. This system focuses on efficiently integrating the user's location information, emotional state, meal history, and health information to provide personalized meals.

[0577] First, the server uses location services to determine the user's current location and lists partner restaurants near the user. Next, it analyzes past dining history and health information using artificial intelligence to recognize the user's preferences and health constraints. To achieve this, the server uses a cloud environment with advanced data processing and analysis capabilities to perform rapid processing.

[0578] Subsequently, an emotion engine is used to acquire the user's emotional state from the device, and this data is analyzed to generate the optimal menu. This emotion engine is responsible for collecting emotional data using sensors on devices such as smartphones and smartwatches, and transmitting it to the server.

[0579] The terminal visually displays meal suggestions from the server to the user, allowing them to reserve or order the suggested menu items. Furthermore, for those requesting delivery, the service improves efficiency by tracking the delivery status of orders in real time.

[0580] A concrete example of this system is suggesting relaxing herbal tea or a healthy salad to users who are feeling stressed after work. This allows users to receive necessary nutrition while also receiving emotional support.

[0581] A concrete example of a prompt message for a generative AI model would be, "I'm tired from work, so please suggest a relaxing meal. I've been paying attention to my health lately, so I'd like a menu with less salt." By responding to such requests from users, the system can continuously provide personalized services.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The server obtains the user's location information. Using location services, it receives GPS data as input to determine the user's current location. Based on this information, it generates a list of nearby restaurants accessible to the user.

[0585] Step 2:

[0586] The server retrieves past meal history and health information from a database. This information is then used as input and analyzed using artificial intelligence (AI). The analysis identifies foods that should be avoided based on the user's food preferences and health, and prepares for meal suggestions.

[0587] Step 3:

[0588] The device uses an emotion engine to detect the user's emotional state. It receives heart rate and facial expression data from the smart device's sensors as input, and uses an emotion analysis model to determine the user's current emotional state.

[0589] Step 4:

[0590] The server takes the analysis results from step 2 and the emotional state from step 3 as input, integrates the data, and optimizes the meal menu. Using an AI model, it selects the meal menu best suited to the user's current health condition and emotions, and creates a list of suggestions.

[0591] Step 5:

[0592] The terminal displays a list of suggestions sent from the server to the user. Through the user interface, detailed information about each menu item is visually displayed, allowing the user to select, adjust, or order their desired meal.

[0593] Step 6:

[0594] Users provide feedback on their selected menu items via their device. This feedback, including changes in emotions and satisfaction levels, is recorded and sent to the server to improve the accuracy of future recommendations. This process enhances personalized meal recommendations tailored to each user.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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".

[0612] This invention is a system that utilizes location information, meal history, and health information to optimize an individual's dining experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0613] The server has the ability to determine the user's current location based on the received location information. This allows the server to easily obtain information about nearby restaurants accessible to the user. The server also analyzes the user's eating history and health information, and generates appropriate meal menus considering the user's preferences and allergy information. For example, if a user has previously preferred a lot of Japanese food and has allergies to certain ingredients, new menu suggestions will take this information into account.

[0614] The terminal functions as a platform that presents information transmitted from the server to the user. Users can review the suggested menus through the terminal and make adjustments as needed. After selecting a menu, the terminal displays reservation information to the user in real time, allowing them to efficiently check restaurant availability and make reservations.

[0615] Users select suggested menu items using their devices and provide feedback. For example, they can provide feedback to the system by entering their satisfaction level after the meal and details of any newly discovered allergies. This feedback is collected on the server and used as training data for the AI. This improves the accuracy of future suggestions and enables the provision of more personalized services.

[0616] This invention supports users in selecting restaurants and deciding on menus based on their individual needs, thereby realizing an efficient and healthy dining experience.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The server retrieves the user's location information and compares it with coordinate data in the database to determine the user's current location. Based on this information, it lists nearby partner restaurants.

[0620] Step 2:

[0621] The server retrieves the user's dietary history and health information from a database and analyzes it. Based on the analysis results, it generates a list of foods the user likes and ingredients to avoid.

[0622] Step 3:

[0623] Based on the restaurant menu information identified in Step 1, the server uses AI to generate the optimal menu that matches the analyzed user profile.

[0624] Step 4:

[0625] The terminal displays the optimal menu and restaurant information received from the server to the user. The user reviews the suggested menu and makes adjustments to the dishes and allergy information as needed.

[0626] Step 5:

[0627] Users select their preferred menu items and use the terminal to make reservations and pre-order their meals at the corresponding restaurant. The terminal also allows users to check real-time availability information.

[0628] Step 6:

[0629] If the user selects home delivery, the server integrates with the delivery service and provides a mechanism to order the selected menu items. The terminal tracks the delivery status in real time and notifies the user of the estimated delivery arrival time.

[0630] Step 7:

[0631] Users provide feedback using a device after their meal. They input their satisfaction level with the meal and any new health information, and send it to the server.

[0632] Step 8:

[0633] The server stores user feedback information in a database and uses it as training data for the AI ​​model to improve the accuracy of future suggestions.

[0634] (Example 1)

[0635] 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".

[0636] In modern society, suggesting meals based on individual user preferences and health conditions, and selecting appropriate restaurants, is crucial for maintaining health and improving dining satisfaction. However, conventional systems have been unable to effectively utilize individual health and location information, making it difficult to suggest optimal meals. Furthermore, there has been a challenge in accumulating feedback on selected menus and incorporating it into future suggestions.

[0637] 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.

[0638] In this invention, the server includes means for acquiring data to confirm the user's current location, machine learning means for analyzing the user's meal history and health status data, and recommendation means for suggesting meal menus suitable for the user based on the analysis results. This makes it possible to provide optimal meal menus tailored to the individual needs of each user and to improve the accuracy of suggestions based on feedback.

[0639] "Data used to determine the user's current location" refers to digital data that includes location information such as latitude and longitude to identify the user's location.

[0640] "Machine learning methods" are data analysis techniques that use large amounts of data to enable algorithms to identify patterns and make predictions.

[0641] A "recommended method" is a method or system for presenting the user with the optimal choice based on the analyzed results.

[0642] An "information generation means" is a function for creating appropriate information based on specific circumstances and providing it to the user.

[0643] A "data collection method" is a system that efficiently collects and stores user feedback and other inputs to be used for subsequent analysis.

[0644] An "electronic system" is an aggregate of electronic devices and related software that provides functionality.

[0645] A "delivery service" is a logistics and management system for delivering selected products to a location specified by the user.

[0646] "Nutritional composition" refers to information about the types and amounts of each nutrient contained in a particular food or menu item.

[0647] "Display means" refers to devices or technologies for visually presenting information to a user in digital or analog format.

[0648] This invention is an electronic system that optimizes the dining experience using the user's location information, meal history, and health information. The system consists of a server, a terminal, and a user.

[0649] The server utilizes a digital device equipped with a GPS module to obtain location information from the user's terminal. Based on this location information, the server uses a database management system (DBMS) to search for restaurant information around the user's current location. Furthermore, it applies machine learning algorithms and uses data analysis tools such as Python and R to comprehensively analyze the user's past eating history and health information. Based on this analysis, a generative AI model is used to generate the most suitable meal menu for each individual user in real time.

[0650] The terminal visually displays suggested meal menus sent from the server to the user. This display uses an application that runs on a smartphone or tablet. The user reviews the suggested menu through the terminal and makes adjustments based on allergy information and preferences if necessary. After the menu is finalized, the terminal provides the user with information on available seats and reservation status at the restaurant. This allows the user to make reservations efficiently.

[0651] After the meal, users input their satisfaction level and any newly discovered allergies using a feedback form provided. This feedback is sent to the server via the device and stored again as training data for the algorithm. This allows the server to improve the accuracy of future suggestions and propose more individually optimized menus.

[0652] As a concrete example of its use, users can enter prompt messages. For instance, if a user uses the prompt, "I'd like Japanese food for our next date. I don't like spicy food, so please suggest menu items that exclude it," the system will suggest Japanese restaurants that meet those criteria, improving the user's dining experience.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The server receives location information from the user's terminal. This input data is latitude and longitude information indicating the user's current location. The server uses this information to query a database management system (DBMS) and outputs information about restaurants near the user.

[0656] Step 2:

[0657] The server retrieves the user's meal history and health information. This data includes past eating habits and allergy information. The server utilizes machine learning models to analyze this input data, generating meal menus based on the user's preferences and listing suggested options as output.

[0658] Step 3:

[0659] The server sends the generated menu suggestions to the device. The device displays the suggested menus to the user on the application screen. The user scrolls and taps on the device to view detailed menu information. This improves the convenience of selecting from the suggested options.

[0660] Step 4:

[0661] The user reviews the suggested menu through their terminal and selects their desired menu item. This input is sent to the server, which retrieves the restaurant's reservation information in real time and outputs available reservation dates and times.

[0662] Step 5:

[0663] The server registers restaurant reservations based on the menu selected by the user. This involves updating a database that manages reservation confirmations and seating availability. Reservation completion information is sent to the terminal and displayed for the user to visually confirm.

[0664] Step 6:

[0665] After a meal, users provide feedback on their satisfaction level and any new allergy information using a device. The user's input data is sent to a server and stored as training data for the generated AI model. The goal is to improve the accuracy of future menu suggestions.

[0666] In this way, a series of processes are realized that provide the optimal dining experience tailored to the individual needs of each user.

[0667] (Application Example 1)

[0668] 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".

[0669] Traditional food delivery services have struggled to provide optimal meal suggestions based on individual users' health information and dietary preferences, failing to offer an efficient and healthy consumption experience. Furthermore, the reservation and ordering processes were complex and cumbersome for users. In addition, insufficient tracking of delivery status meant that users could not wait for their orders with peace of mind.

[0670] 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.

[0671] In this invention, the server includes means for acquiring the user's location information, means for a computational model for analyzing the user's meal history and health information, and means for making meal suggestions suitable for the user based on the analysis results. This makes it possible to efficiently provide menus optimized for each user, suggest meals that match the user's health condition and preferences, and enable efficient ordering.

[0672] "Means for obtaining user location information" refers to the technology or device necessary to determine the user's current location, and generally involves using GPS or Wi-Fi signals to determine the location.

[0673] "Computational model means for analyzing a user's dietary history and health information" refers to algorithms and software for collecting and analyzing data on a user's past dietary choices and health status, and is used to generate optimal dietary suggestions for the user.

[0674] "Means of providing meal suggestions suitable for the user based on analysis results" refers to technologies and devices that present meal options that match the user's preferences and health condition based on data obtained through analysis.

[0675] "Means of providing reservation information for dining establishments" refers to technology or devices that query reservation availability information for dining establishments selected by the user and communicate the availability and reservation status to the user.

[0676] "Means for collecting user feedback and improving the accuracy of suggestions" refers to technologies and devices used to acquire user feedback as data, improve the system's learning process based on that information, and enhance the quality of future suggestions.

[0677] "Methods for quickly ordering optimized menus through delivery services" refer to technologies and devices used to efficiently order suggested meal menus using delivery services and to have meals delivered at the time desired by the user.

[0678] "Methods for tracking delivery status in real time" refer to technologies and devices that allow users to check the current location of their ordered meals and provide them with the latest delivery information.

[0679] "Means for checking nutritional information and allergy information" refers to technologies and devices that allow users to easily check the nutrients included in suggested menus and information related to their own allergies.

[0680] To realize this invention, we will specifically explain how each element—the server, terminal, and user—functions.

[0681] The server is equipped with hardware to acquire the user's location information using GPS or Wi-Fi signals. Next, the server utilizes artificial intelligence as a computational model to analyze the user's past eating history and health information. This AI model is built using Python libraries and data analysis tools. Based on the analyzed data, it generates meal suggestions tailored to the user. As an example of a suggestion, the server will find restaurants that offer low-calorie Japanese food for a user who prefers such menus.

[0682] The terminal displays information about meal menus and dining locations sent from the server to the user via a dedicated mobile application. This application is developed using Android and iOS frameworks (e.g., Flutter and React Native). The terminal displays real-time tracking information on the reservation status and delivery status of the dining location selected by the user. Users can also check nutritional information and allergy information for the menu via the terminal.

[0683] Users select meal options and provide feedback based on information provided through their devices. This data is sent to the server to improve the system's accuracy. User feedback plays a crucial role in suggesting new meal options. For example, if a user particularly likes a menu item, that information will be reflected in future suggestions.

[0684] As a concrete example, a user might make a request via a prompt message such as, "I'm currently in Shibuya, and I'm on a low-sugar diet, so please prioritize displaying low-carb menus. I prefer Japanese food, so please suggest restaurants that match that." Based on this, the server analyzes the request and suggests menus that meet the criteria.

[0685] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0686] Step 1:

[0687] The server obtains the user's location information using GPS. The input is location data transmitted from the user's smartphone. This data is analyzed to determine the user's current location. The output is the identified geographic coordinates.

[0688] Step 2:

[0689] The server retrieves the user's dietary history and health information from a database. This data is then used as input for analysis using a computational model. This analysis allows for an understanding of the user's dietary preferences and health status. The output is a user profile based on this information.

[0690] Step 3:

[0691] The server uses the above location information and profile as input to refer to a database of nearby restaurants and selects a suitable meal menu. An AI model is used here to list options that match the user's preferences and restrictions. The output is a list of several suitable meal menus and the restaurants that offer them.

[0692] Step 4:

[0693] The server transmits information about the selected menu and dining location to the terminal. The terminal displays this information on its user interface. The input consists of the menu and dining location information received from the server. The output is a visual presentation of this information to the user. The user can check menu details and reservation status.

[0694] Step 5:

[0695] The user selects a menu item based on the presented information and sends feedback from the terminal. The input consists of the user's menu selection and feedback. This data is sent to the server and used to improve the accuracy of future meal recommendations. The output generates newly learned user preference data.

[0696] Step 6:

[0697] The server makes restaurant reservations or delivery arrangements based on the user's selection. Input consists of the user's menu selection and reservation request. The server then refers to a database to make the reservation or delivery arrangement. The output generates reservation confirmation information or delivery tracking information.

[0698] Step 7:

[0699] The terminal notifies the user of reservation confirmation information or delivery status. The input is reservation or delivery information received from the server. The terminal displays this on the user interface and provides output that allows the user to visually confirm this information.

[0700] 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.

[0701] This invention is a system that utilizes location information, meal history, health information, and emotional information to optimize the user's dining experience. This system consists of three elements: a server, a terminal, and a user, and is equipped with an emotional engine.

[0702] The server first obtains the user's current location from location services and identifies nearby partner restaurants. Based on this location information, it creates a list of restaurants the user can access. Furthermore, it retrieves past dining history and health information from a database and analyzes it using artificial intelligence. The results of this analysis allow the server to understand the user's food preferences and any dietary restrictions they should avoid.

[0703] Next, the emotion engine analyzes the user's emotional data acquired by the device (for example, data obtained from a smartwatch or the device's built-in camera). The emotion engine then works with a server to suggest the most suitable menu or restaurant based on the user's emotional state. For example, if the user is feeling stressed, the system can select a menu that includes beverages and dishes with relaxing effects.

[0704] The terminal functions as a platform that presents users with menu suggestions and restaurant information sent from the server. Users can review and adjust suggested menus, make reservations, and place orders through the terminal. If a delivery option is selected, real-time tracking information of the delivery status is also provided.

[0705] Users can input feedback into their terminal after selecting, booking, and ordering a meal, reporting their satisfaction level and changes in their emotions regarding the meal. Based on the collected feedback and emotional data, the server can improve the accuracy of the system and further personalize future recommendations.

[0706] Thus, the present invention realizes meticulous meal suggestions that respond to the user's emotions, and provides a service that is attentive to the user's health and emotional needs.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The server obtains the user's location information and determines their current location. Based on this location information, it lists nearby partner restaurants.

[0710] Step 2:

[0711] The server retrieves the user's dietary history and health information from a database and analyzes it using artificial intelligence. Through this analysis, it profiles the user's preferences and limitations.

[0712] Step 3:

[0713] The device uses cameras and sensors to measure the user's emotions in real time. The obtained emotion data is then input into an emotion engine.

[0714] Step 4:

[0715] The emotion engine analyzes the user's emotional state and sends that information to the server. The server integrates the emotional information with the user's dietary profile and suggests the optimal meal plan.

[0716] Step 5:

[0717] The terminal displays menus and restaurant information provided by the server to the user. The user reviews and adjusts the menu items via the terminal and approves their selection.

[0718] Step 6:

[0719] The user makes a reservation at their chosen restaurant and orders their menu through the terminal. The terminal checks and displays availability and reservation status to the user.

[0720] Step 7:

[0721] If home delivery is selected, the server will coordinate with the delivery service to enable completion of the delivery process and tracking of the delivery status. The terminal will provide the user with real-time information on the delivery time.

[0722] Step 8:

[0723] After the meal, users use their device to input feedback, sending their satisfaction level, emotional changes, and new health information to the server.

[0724] Step 9:

[0725] The server stores the collected feedback and sentiment data in a database and optimizes future suggestions by updating the AI ​​model.

[0726] (Example 2)

[0727] 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".

[0728] Conventional meal recommendation systems often only consider the user's past eating history and health information, making it difficult to provide appropriate recommendations that reflect the user's emotional state. Furthermore, users often experience stress and wasted time when choosing which restaurant to eat at. Additionally, the limited use of feedback hindered improvements in the system's recommendation accuracy.

[0729] 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.

[0730] In this invention, the server includes means for acquiring the user's location information and identifying nearby affiliated restaurants and bars, information processing technology means for analyzing the user's meal history and health information, and emotion recognition technology means for collecting and analyzing the user's emotional data. This enables detailed meal suggestions that take into account the user's emotional state, and helps the user select restaurants and menus more quickly and appropriately.

[0731] "Location information" refers to geographical data that indicates the user's current location and is fundamental information for identifying food and beverage establishments that the user can access.

[0732] "Meal history" refers to data about meals a user has consumed in the past, and serves as basic information for analyzing the user's food preferences and tastes.

[0733] "Health information" refers to data about the user's health status and serves as basic information for providing dietary suggestions that take into account restrictions on the intake of specific nutrients and foods.

[0734] "Emotional data" refers to data that indicates the user's current emotional state and serves as fundamental information for providing meal suggestions that meet the user's psychological needs.

[0735] "Proposed means" refers to processes and technologies for presenting meal menus and dining facilities suitable for the user based on analyzed data.

[0736] "Reservation information" refers to data used to secure a seat and time for a meal at a restaurant selected by the user, and is a service designed to improve the user experience.

[0737] "Feedback" refers to information about user satisfaction with meals and suggestions, as well as changes in emotions, and is important data for improving the accuracy of the system's suggestions.

[0738] This invention is a system that integrates and analyzes various data to optimize the user's dining experience. The system consists of three elements: a server, a terminal, and the user.

[0739] The server uses common location services to obtain the user's location information. For example, it uses a map service API to obtain the user's current latitude and longitude. Based on this information, it identifies nearby affiliated restaurants and bars from its database. The server also refers to a database that holds the user's eating history and health information, and uses information processing technology to analyze the user's preferences and dietary restrictions. This analysis uses machine learning algorithms, for example, to predict past menu preferences based on the user's eating history.

[0740] Furthermore, the device incorporates emotion recognition technology, collecting user emotional data through the smartwatch and built-in camera. This data is sent to a server and analyzed by an emotion engine. The identified emotional state is reflected in meal suggestions, offering menus tailored to situations where the user wants to relax or needs energy.

[0741] For example, if a user is looking for something to relax after work, the menu displayed on the device might include "herbal tea and risotto." Based on this suggestion, the user can book a restaurant through the device. If a delivery service is selected, the device tracks the delivery status in real time and displays it to the user.

[0742] Through this process, the server and terminal provide the user with the optimal dining experience. Users can report their satisfaction with the suggestions and changes in their feelings through feedback. This feedback is used by the server to improve the suggestion algorithm and further personalize future suggestions.

[0743] An example of a prompt for a generative AI model is, "Please suggest relaxing dishes that would be suitable for a user who is feeling stressed." By using this prompt, the AI ​​model generates suggestions that meet the user's needs.

[0744] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0745] Step 1:

[0746] The server obtains the user's location information. It receives a location request from the device as input and uses a map service API to obtain the current latitude and longitude. This allows it to identify nearby affiliated restaurants and bars from its database. Specifically, the server issues a request to the API and performs a database search based on the received latitude and longitude.

[0747] Step 2:

[0748] The server retrieves the user's meal history and health information from a database. It uses the user ID as input to access the information from the database. The retrieved data is analyzed using information processing technology, such as machine learning algorithms, to predict the user's preferences and dietary restrictions. Specifically, the server generates a model based on the user's history to identify previously preferred menu items and ingredients to avoid.

[0749] Step 3:

[0750] The device collects user emotional data from the smartwatch and built-in camera. It senses vital data (heart rate, facial expression analysis data, etc.) as input and analyzes the user's emotional state using emotion recognition technology. As output, it sends the analysis results to a server. Specifically, the device locally analyzes the data collected from sensors, determines a specific emotional state (e.g., stress, exhilaration), and sends the result to the server.

[0751] Step 4:

[0752] The server integrates location information, meal history, health information, and sentiment data, and uses a generative AI model to suggest the optimal meal menu and dining establishment. Using this data as input, it provides prompts to the generative AI model, which then generates a list of the most appropriate menus and dining establishments as output. Specifically, the server activates the AI ​​based on the prompts, generates multiple candidates, and sends them to the terminal.

[0753] Step 5:

[0754] The terminal presents the user with suggestions received from the server. It receives data from the server as input and displays it to the user through the terminal's user interface. As output, it enables the user to make selections and reservations. Specifically, the terminal lists menus and dining establishments on the screen, and the reservation process begins when a selection button is pressed.

[0755] Step 6:

[0756] The user enters feedback into the device after the meal. The input includes reporting specific satisfaction levels and emotional changes, and the device sends this feedback to the server. The output is that this feedback is used to improve the proposed algorithm. Specifically, the user enters their evaluation through a feedback form, and the device sends and stores this information on the server.

[0757] (Application Example 2)

[0758] 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".

[0759] In modern society, users need to make a wide variety of food choices, but managing their eating history, health information, and emotional state while selecting the optimal meal is not easy. Furthermore, there are many challenges in efficiently selecting appropriate restaurants and managing the entire process from ordering to delivery. In response, there is a need for a means to optimize the dining experience by providing detailed meal suggestions tailored to each user's individual circumstances.

[0760] 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.

[0761] In this invention, the server includes means for acquiring the user's location information, artificial intelligence means for analyzing the user's eating history and health information, means for suggesting a meal menu suitable for the user based on the analysis results, and means for further optimizing the meal menu based on the user's emotional state. This makes it possible to provide meal suggestions tailored to the user's individual health and emotional state, as well as an efficient dining experience.

[0762] "Means for obtaining user location information" refers to technologies that determine the geographical location of a user.

[0763] "Artificial intelligence methods" refer to technologies that use computers to mimic human intellectual activity, analyzing dietary history and health information to identify patterns and trends.

[0764] "Methods for suggesting meal menus" refers to a function that presents meal options suitable for the user based on analysis results.

[0765] "Methods for further optimizing meal menus based on emotional state" refers to a function that analyzes the user's emotional data and selects the meal content that is most appropriate for that state.

[0766] "Methods for collecting user feedback and improving the accuracy of suggestions" refers to the process of compiling user feedback and opinions to improve the quality of future meal suggestions.

[0767] "Means of tracking delivery status" refers to a feature that allows you to check in real time where your ordered meal is currently located and when it will arrive.

[0768] "Means of checking nutritional and allergy information" refers to technology that allows users to view information about the nutritional components contained in the meals provided and allergens they should avoid.

[0769] This invention is an advanced system for optimizing the user's dining experience. It mainly consists of three components: a server, a terminal, and the user. This system focuses on efficiently integrating the user's location information, emotional state, meal history, and health information to provide personalized meals.

[0770] First, the server uses location services to determine the user's current location and lists partner restaurants near the user. Next, it analyzes past dining history and health information using artificial intelligence to recognize the user's preferences and health constraints. To achieve this, the server uses a cloud environment with advanced data processing and analysis capabilities to perform rapid processing.

[0771] Subsequently, an emotion engine is used to acquire the user's emotional state from the device, and this data is analyzed to generate the optimal menu. This emotion engine is responsible for collecting emotional data using sensors on devices such as smartphones and smartwatches, and transmitting it to the server.

[0772] The terminal visually displays meal suggestions from the server to the user, allowing them to reserve or order the suggested menu items. Furthermore, for those requesting delivery, the service improves efficiency by tracking the delivery status of orders in real time.

[0773] A concrete example of this system is suggesting relaxing herbal tea or a healthy salad to users who are feeling stressed after work. This allows users to receive necessary nutrition while also receiving emotional support.

[0774] A concrete example of a prompt message for a generative AI model would be, "I'm tired from work, so please suggest a relaxing meal. I've been paying attention to my health lately, so I'd like a menu with less salt." By responding to such requests from users, the system can continuously provide personalized services.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The server obtains the user's location information. Using location services, it receives GPS data as input to determine the user's current location. Based on this information, it generates a list of nearby restaurants accessible to the user.

[0778] Step 2:

[0779] The server retrieves past meal history and health information from a database. This information is then used as input and analyzed using artificial intelligence (AI). The analysis identifies foods that should be avoided based on the user's food preferences and health, and prepares for meal suggestions.

[0780] Step 3:

[0781] The device uses an emotion engine to detect the user's emotional state. It receives heart rate and facial expression data from the smart device's sensors as input, and uses an emotion analysis model to determine the user's current emotional state.

[0782] Step 4:

[0783] The server takes the analysis results from step 2 and the emotional state from step 3 as input, integrates the data, and optimizes the meal menu. Using an AI model, it selects the meal menu best suited to the user's current health condition and emotions, and creates a list of suggestions.

[0784] Step 5:

[0785] The terminal displays a list of suggestions sent from the server to the user. Through the user interface, detailed information about each menu item is visually displayed, allowing the user to select, adjust, or order their desired meal.

[0786] Step 6:

[0787] Users provide feedback on their selected menu items via their device. This feedback, including changes in emotions and satisfaction levels, is recorded and sent to the server to improve the accuracy of future recommendations. This process enhances personalized meal recommendations tailored to each user.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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 based, for example, 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.

[0796] 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."

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] Means for obtaining the user's location information,

[0812] An artificial intelligence means for analyzing a user's dietary history and health information,

[0813] A means of suggesting a meal menu suitable for the user based on the results of the analysis,

[0814] A means of providing restaurant reservation information based on the proposed menu,

[0815] A means of collecting user feedback to improve the accuracy of suggestions,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, which includes means for ordering a suggested menu through a delivery service and tracking the delivery status.

[0819] (Claim 3)

[0820] The system according to claim 1, which includes means for checking nutritional information and allergy information of a proposed menu.

[0821] "Example 1"

[0822] (Claim 1)

[0823] A means of obtaining data to confirm the user's current location,

[0824] A machine learning method for analyzing users' dietary history and health status data,

[0825] Based on the analysis results, a recommendation system is used to suggest meal menus suitable for the user.

[0826] An information generation means that provides restaurant reservation information based on the proposed menu,

[0827] A data collection method to improve the accuracy of suggestions by collecting feedback on user satisfaction and allergy information,

[0828] Electronic systems including

[0829] (Claim 2)

[0830] The electronic system according to claim 1, which includes means for ordering a proposed menu through a delivery service and managing the delivery status.

[0831] (Claim 3)

[0832] The electronic system according to claim 1, which includes a display means for checking the nutritional composition and allergy information of a proposed menu.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] Means for obtaining the user's location information,

[0836] A computational model means for analyzing a user's dietary history and health information,

[0837] A means of providing meal suggestions suitable for the user based on the results of the analysis,

[0838] A means of providing reservation information for dining venues based on the proposed meal,

[0839] A means of collecting user feedback to improve the accuracy of suggestions,

[0840] A means of quickly ordering an optimized menu through a delivery service,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, which includes means for tracking the delivery status of ordered meals in real time.

[0844] (Claim 3)

[0845] The system according to claim 1, which includes means for checking nutritional information and allergy information of a proposed meal.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A means of obtaining the user's location information and identifying nearby affiliated restaurants and bars,

[0849] Information processing technology means for analyzing a user's dietary history and health information,

[0850] A means of emotion recognition technology for collecting and analyzing user emotion data,

[0851] A means of suggesting suitable meal menus and dining facilities to the user based on the analysis results and emotional state,

[0852] A means of providing reservation information for dining establishments based on the proposed menu,

[0853] A means of collecting user feedback to improve the accuracy of suggestions,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which includes means for ordering a suggested menu through a delivery service and tracking the delivery status.

[0857] (Claim 3)

[0858] The system according to claim 1, which includes means for checking nutritional information and restricted food information of a proposed menu.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] Means for obtaining the user's location information,

[0862] An artificial intelligence means for analyzing a user's dietary history and health information,

[0863] A means of suggesting a meal menu suitable for the user based on the results of the analysis,

[0864] A means to further optimize meal menus based on the user's emotional state,

[0865] A means of providing restaurant reservation information based on the proposed menu,

[0866] A means of collecting user feedback to improve the accuracy of suggestions,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which includes means for ordering a suggested menu through a product delivery service and tracking the delivery status.

[0870] (Claim 3)

[0871] The system according to claim 1, which includes means for checking nutritional information and allergy information of a proposed menu. [Explanation of Symbols]

[0872] 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. Means for obtaining the user's location information, An artificial intelligence means for analyzing a user's dietary history and health information, A means of suggesting a meal menu suitable for the user based on the results of the analysis, A means of providing restaurant reservation information based on the proposed menu, A means of collecting user feedback to improve the accuracy of suggestions, A system that includes this.

2. The system according to claim 1, which includes means for ordering a suggested menu through a delivery service and tracking the delivery status.

3. The system according to claim 1, which includes means for checking nutritional information and allergy information of the proposed menu.

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