system

Abstract

We provide the system. [Solution] Information processing means that collects preference information and health information and generates personalized food menus based on this information, Information processing means that collects information on preferences and health, and generates an individualized meal plan based on this information, An interactive interface means that engages with the user in a conversational format and supports their choices using natural language, Route optimization methods to optimize paths and reduce travel time by means of transportation, A means of linking with smart devices to understand the user's exercise and nutritional intake and manage health information, A means of simplifying operations that allows food selection to be repeated with a single instruction, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In the conventional food delivery service, it is difficult for users to select an appropriate menu considering health, and there is a lack of service provision that matches the preferences of users. Furthermore, there is a problem that the process from ordering to delivery is complicated and time-consuming. For this reason, there is a problem that the user experience is uniform and does not sufficiently meet individual needs.

Means for Solving the Problems

[0005] This invention collects user preference and health information using information processing means and generates personalized food menus based on this information. Furthermore, it supports ordering through natural dialogue with the user using an interactive interface means. In addition, it optimizes delivery routes and shortens delivery times using route optimization means, thereby streamlining the entire ordering and delivery process. In this way, it enables a healthy and prompt food delivery service tailored to the individual needs of users.

[0006] "Preference information" refers to information about the types of foods, flavors, and nutrients that users prefer to consume.

[0007] "Health information" refers to information related to the user's health status, such as allergies, nutritional goals, and calorie intake.

[0008] "Information processing means" refers to the functions of a computer system that collect and analyze data and generate information as needed.

[0009] A "personalized food menu" is a menu specially created for a particular user based on their preferences and health information.

[0010] An "interactive interface" is a means by which a user and a system communicate bidirectionally using natural language.

[0011] A "route optimization method" is a system function that analyzes delivery routes and calculates and proposes the most efficient route.

[0012] "Delivery time" refers to the time from when an order is placed until the food is delivered to the customer.

[0013] A "wearable electronic device" is an electronic device that can be worn by a user to continuously acquire biological and motor information.

[0014] "Order simplification measures" refer to functions that simplify the user's ordering process and enable quick reordering. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0016] 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.

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

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

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

[0020] In the following embodiments, a numbered 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, etc.

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

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

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0036] This invention is a system that provides highly accurate food menu suggestions based on user preference and health information, offers a natural ordering experience through an interactive interface, and achieves efficient delivery through route optimization means.

[0037] Management of preference and health information

[0038] The server collects preference and health information when a user first uses the system and stores it in a database. Users can register their preferences, allergies, and nutritional goals through their device. The system also integrates with wearable electronic devices, periodically sending data to the server to track the user's daily exercise and health status. This data is used to support the user's health maintenance.

[0039] Proposal for personalized menus

[0040] The server generates personalized menus based on each user's stored information. Specifically, it proposes menus designed to optimize the nutritional balance for the day, taking into account preferences and health goals. In addition, past order history is also included in the calculations, enabling even more personalized suggestions.

[0041] Order support through an interactive interface

[0042] The terminal enables natural interaction with the user. Specifically, when the user makes comments or asks questions about the suggested menu, it immediately provides corresponding answers to help them make the best choice. Once the user completes their selection, the order details are sent to the server. Furthermore, for repeat orders, users can easily do so with a single click from their past order history.

[0043] Optimization and efficiency of delivery routes

[0044] Once an order is confirmed, the server calculates the optimal delivery route based on that information and issues instructions to the delivery partner. It analyzes traffic information and order congestion in real time to achieve the shortest possible delivery time. This allows users to receive their goods quickly.

[0045] Specific example

[0046] For example, if a user prefers vegan food, the server searches for new vegan menu items that suit their preference and makes suggestions based on the user's nutritional goals. Once the order is complete, the server selects the nearest delivery partner from a vegan-friendly restaurant and provides the shortest route. This entire process enables the delivery of healthy and efficient meals.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user logs into the system via their device, enters their preferences, allergy information, and nutritional goals, and completes the initial setup. The device then sends the entered information to the server.

[0050] Step 2:

[0051] The server stores the received user information in a database and generates profiles of preference patterns and health goals. If necessary, it starts collecting data from wearable electronic devices.

[0052] Step 3:

[0053] When a user attempts to order food, a request is made from their device and sent to the server. The server utilizes the user profile to generate personalized suggestions from the available menu.

[0054] Step 4:

[0055] The server generates a personalized menu and sends it to the terminal, which then displays it to the user. The user reviews the suggested menu and asks questions or confirms details through an interactive interface.

[0056] Step 5:

[0057] Once the user confirms their order, the terminal sends the details to the server. Based on the order information, the server begins calculations to determine the optimal delivery route and assign a delivery person.

[0058] Step 6:

[0059] The server communicates optimized routes and instructions to delivery partners and initiates the delivery process. Users receive immediate notification of the estimated delivery time.

[0060] Step 7:

[0061] A delivery partner picks up the food and heads to the destination along the designated route. Users can check the current delivery status in real time via their terminal.

[0062] Step 8:

[0063] Users receive their products and provide feedback on quality and service on their devices. The server stores the received feedback in a database and uses it to improve services in the future.

[0064] (Example 1)

[0065] 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."

[0066] The challenge lies in providing appropriate food selection suggestions based on users' individual preferences and health information, along with an efficient ordering process and prompt delivery. In particular, it is necessary to manage information, optimize delivery routes, and eliminate the hassle of reordering, thereby providing users with a consistent, convenient, and effective service.

[0067] 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.

[0068] In this invention, the server includes information processing means for collecting preference information and health information and generating personalized food menus based on this information; interactive interface means for interacting with the user in natural language and assisting with ordering; route optimization means for calculating the optimal delivery route after order confirmation and shortening delivery time; means for making menu suggestions that take past order history into consideration; and means for making food suggestions using a generative model. This enables optimal food suggestions that take into account the user's health condition and an efficient ordering and delivery process.

[0069] "Preference information" refers to information that includes individual users' food preferences, allergies, and special requests regarding meals.

[0070] "Health information" refers to information including the user's current health status, nutritional goals, and any medical restrictions or goals.

[0071] An "information processing means" is a process that has the function of generating personalized food menus based on preference information and health information collected from users.

[0072] An "interactive interface means" is an interface function that interacts with users via natural language and assists them in placing orders.

[0073] A "route optimization method" is a function that calculates the most efficient route for delivery, thereby minimizing delivery time as much as possible.

[0074] A "generative model" is a computational model that uses machine learning or other advanced algorithms to generate new proposals.

[0075] "Order history" refers to a record of orders placed by a user in the past, and this information is retained to help with future suggestions.

[0076] The embodiments for carrying out the present invention are as follows.

[0077] The server is equipped with information processing means to efficiently collect preference and health information and generate personalized food menus. This means stores the user's preference and health information using a database and analyzes this data using a generation AI model to propose the most suitable menu for the user. Specifically, the server takes the user's past order history into consideration and generates menus that align with their preferences and nutritional goals.

[0078] The terminal is equipped with an interactive interface to support natural conversations with users. This interface uses natural language processing technology to respond to user questions and feedback in real time and assist in making appropriate orders. For example, if a user asks, "What are today's recommendations?", the terminal will, based on that inquiry, present today's recommendations from the suggested menu.

[0079] Once an order is confirmed, the server uses route optimization techniques to optimize the delivery route and ensure fast delivery. This involves analyzing traffic information in real time to reduce delivery times. For example, if a user who prefers vegan food orders a vegan-only menu item, the server calculates the shortest delivery route from a vegan-friendly restaurant.

[0080] Furthermore, by linking with wearable electronic devices, the server can track the user's daily exercise levels and calorie consumption, and store this information as health data. This information can be used to help users maintain their health and, for example, reflects in menu suggestions that take into account the user's health goals, such as "I want a high-protein diet."

[0081] A concrete example of a prompt message is when a user inputs "Please suggest a vegan, high-protein breakfast menu" to the AI ​​generation model, allowing the server to suggest a menu suitable for the user. This process improves the user experience and supports healthy and efficient food delivery.

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

[0083] Step 1: Gathering information on preferences and health.

[0084] Users input their preferences and health information through a terminal. Specific information includes food preferences, allergy information, and nutritional goals. The terminal sends this input information to a server, which stores it in a database. The input is information from the user, and the output is storage in the database.

[0085] Step 2: Obtain health data

[0086] The server receives user health data from wearable electronic devices. This health data includes activity levels, calories burned, heart rate, etc. The server analyzes the received data and records it in a database as health information. The input is data from the wearable electronic device, and the output is the analyzed health information.

[0087] Step 3: Generating a personalized menu

[0088] The server generates personalized menus based on accumulated preference information, health information, and past order history. Using a generation AI model, it creates prompt messages and suggests menus suitable for the user. The input consists of various database entries, and the output is a personalized suggested menu.

[0089] Step 4: Order support via interactive interface

[0090] The user makes inquiries and selections regarding the suggested menu items through the terminal. For example, they can ask, "Does this dish contain nuts?" The terminal analyzes the input and provides an appropriate answer. The input is the user's question, and the output is the answer information.

[0091] Step 5: Order Confirmation and Optimization of Delivery Routes

[0092] Once an order is confirmed, the server calculates the optimal delivery route. During delivery, it analyzes traffic information in real time, selects the fastest route, and instructs the delivery partner accordingly. The input is the confirmed order information, and the output is the optimized delivery route.

[0093] Step 6: Update Order History

[0094] The server updates the user's order history in the database after delivery is complete. This historical data is used to make future suggestions. The input is the order data associated with the completed delivery, and the output is the updated order history.

[0095] (Application Example 1)

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

[0097] There is a need to more effectively suggest meals tailored to users' preferences and health conditions, and a challenge to reduce the effort involved in selecting ingredients and ordering, as well as to expedite delivery. Furthermore, given the demand for health management and efficient time management in modern society, a system that addresses these issues is necessary.

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

[0099] This invention includes a server comprising: information processing means for collecting information on preferences and health and generating personalized meal plans based on this information; interactive interface means for engaging in conversational dialogue with the user and assisting with selections using natural language; route optimization means for optimizing paths and shortening travel time by means of transportation; cooperation means for understanding the user's exercise and nutritional intake in conjunction with a smart device and managing health information; and operation omission means for enabling food selection to be repeated with a single instruction. This enables the suggestion of healthy meals optimized for the user and rapid and efficient delivery.

[0100] "Information regarding preferences" refers to data that shows the types of ingredients and dishes that each user personally likes, as well as their taste preferences.

[0101] "Health-related information" refers to data related to each user's health status, and specifically includes allergy information, nutritional indicators, and health goals.

[0102] "Information processing means" refers to systems or devices that have the function of generating personalized meal plans based on collected information on preferences and health.

[0103] "Interactive interface means" refers to technologies and methods for supporting ordering and selection through natural conversation with the user.

[0104] A "route optimization method" is a technology that has the ability to calculate and propose the optimal route in order to reduce travel time for delivery.

[0105] "Integration means" refers to a mechanism that communicates with smart devices to manage and incorporate users' health information, such as exercise levels and nutritional intake, into the system.

[0106] A "simplification of operation" is a function that allows users to repeat food selection and ordering with a single instruction, thereby reducing the effort required of the user.

[0107] In this invention, the server utilizes a series of information processing means to provide users with food menus optimized for them, thereby realizing an efficient delivery service. Specifically, the server collects preference and health information and generates personalized meal plans based on this information. The program uses a MySQL® database as its main backend technology for data storage and management. Furthermore, it collaborates with wearable devices to collect information, using APIs such as Fitbit to acquire data on exercise levels and health status, and reflects this data in health management directed to the server.

[0108] The terminal uses a speech recognition system such as Dialogflow to provide an interactive interface through natural conversation with the user. This allows users to ask questions about food and nutrition in natural language and receive appropriate answers and menu suggestions. Users can view the menu on the system and confirm their order with a single click. For repeat orders, the process is streamlined based on past selections using a simplified procedure.

[0109] For delivery, the server utilizes map information such as the Google® Maps API and employs route optimization techniques to analyze real-time traffic information and select the route that minimizes delivery time by mode of transport. This ensures that products are delivered to users quickly.

[0110] A concrete example of a prompt would be, "Based on the user's health data, suggest a suitable vegan menu for Monday's lunch." Through such prompts, the system can respond and provide the user with optimal food suggestions.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server receives preference and health information from the user as input. This information is registered by the user via a terminal and stored in a database. Based on the input information, the server updates the database and optimizes it according to the user's health status and nutritional goals. This forms the basis for subsequent meal plan suggestions.

[0114] Step 2:

[0115] The server periodically collects health-related data from smart devices, including exercise data from wearable devices such as Fitbit. The server takes health information as input, analyzes the data, and monitors the user's health status by comparing it to past data. As a result, it can provide nutritional suggestions tailored to the user's health fluctuations.

[0116] Step 3:

[0117] The server generates personalized meal plans based on collected preference, health, and exercise data. Using a generation AI model, it leverages prompts to derive optimal menus, for example, following instructions such as "Please suggest vegan menus." The generated plan is sent to the user's terminal in a format that the user can review.

[0118] Step 4:

[0119] The terminal uses an interactive interface to receive additional questions and requests from the user. It receives user questions in natural language as input, analyzes them using Dialogflow, and generates appropriate answers. This process resolves any uncertainties the user may have regarding ingredients.

[0120] Step 5:

[0121] The user reviews the menu suggested by the terminal and decides on their order. The user's selection is collected as input, received by the server, and the order information is processed. This prepares the server to select the most suitable delivery partner.

[0122] Step 6:

[0123] The server uses the Google Maps API to collect traffic information and optimize routes. Together with selected delivery partners, it calculates the shortest and most optimal delivery route. This process outputs the fastest possible delivery time, establishing a plan for quick delivery to the user.

[0124] 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.

[0125] This invention combines a system that provides personalized menus based on user preference and health information with an emotion engine that recognizes the user's emotional state. This system enables real-time order support and delivery optimization through natural language dialogue with the user.

[0126] Emotional engine integration

[0127] The server features an emotion engine that analyzes voice and text data to recognize the user's emotional state in real time. While the user is browsing the menu, the terminal monitors the conversation and detects changes in emotion. This information is sent to the server and used to adjust the suggested menu. For example, if the user is feeling stressed, the system will prioritize suggesting foods with relaxing effects.

[0128] Emotion-based menu suggestions

[0129] Based on feedback from the emotion engine, the server adjusts menu selections to match the user's psychological state. This adjustment allows for a dining experience that takes the user's emotional state into consideration. If the user is excited, the server enhances the positive experience by suggesting new dishes or exotic options.

[0130] Adjusting the response of interactive interfaces

[0131] The interactive interface adapts its responses based on the user's emotions. If the user is feeling down, the conversation shifts to a more friendly and encouraging tone to lift their spirits. In this way, the user gains an experience that goes beyond simply placing an order.

[0132] Delivery optimization and feedback analysis

[0133] Once an order is confirmed, the server selects the optimal delivery route and delivery person, taking into account the user's emotional state. If the user is in a hurry, the system prioritizes the fastest delivery route, ensuring a quick response to user needs. Furthermore, user feedback is analyzed by an emotion engine, and actions that reinforce positive emotions are incorporated into future suggestions.

[0134] Specific example

[0135] When a user tells their device, "Today was a really busy day," the system detects stress through its emotion engine. The server then quickly responds to the user's needs by suggesting an energy-restoring menu for busy days and offering the shortest delivery route option.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user activates the device and logs into the system. The device provides an interface for collecting the user's preferences, health information, and emotional state.

[0139] Step 2:

[0140] The server receives the login information and retrieves the corresponding user profile from the database. This profile includes information related to past order history, preferences, and health status.

[0141] Step 3:

[0142] The device activates its emotion engine and analyzes emotions from the user's voice or text messages. The device then sends this emotion data to the server.

[0143] Step 4:

[0144] The server integrates emotional data with existing user profiles to generate personalized menus. The suggested menus are adjusted to reflect the user's current emotional state.

[0145] Step 5:

[0146] The terminal displays a personalized menu received from the server to the user. It utilizes an interactive interface that allows the user to review the menu and ask questions or place orders using natural language.

[0147] Step 6:

[0148] Once the user confirms their order, the terminal sends that information to the server. The server receives the order and begins the delivery process, taking into account the user's emotional state.

[0149] Step 7:

[0150] The server calculates the optimal delivery route and arranges expedited delivery based on analysis by the emotion engine. If necessary, it sends instructions to delivery partners.

[0151] Step 8:

[0152] Users receive their delivered goods and provide emotionally charged feedback on their devices. The server analyzes this feedback and uses it to improve future services.

[0153] (Example 2)

[0154] 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".

[0155] In today's diverse dietary landscape, there is a demand for personalized food products that take into account the health and emotional states of consumers. However, current systems lack effective means of reflecting emotional states. Furthermore, methods for expediting delivery and utilizing user feedback to improve future recommendations are limited.

[0156] 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.

[0157] In this invention, the server includes information processing means for collecting preference information and health information to generate personalized food menus, emotion recognition means for analyzing voice and text data to recognize emotional states, and information feedback means for analyzing user feedback and reflecting it in future suggestions. This enables the provision of personalized menus that take into account the user's psychological state and the improvement of the cycle based on feedback.

[0158] "Information processing means" refers to a device or program that has the function of collecting user preference information and health information and generating personalized menus based on this information.

[0159] An "interactive interface means" is a component of a system that interacts with users using natural language to assist with ordering.

[0160] "Emotion recognition means" refers to a technology that analyzes the user's voice and text data to recognize their emotional state in real time and reflect it in menu suggestions.

[0161] "Route optimization means" refers to route calculation algorithms that effectively calculate delivery routes and shorten delivery times.

[0162] An "information feedback mechanism" is a function that analyzes user feedback, incorporates it into future proposals, and aims to improve the service.

[0163] A "wearable electronic device" is an electronic device that can be directly attached to the user's body and is used to manage energy consumption and nutrient intake.

[0164] "Order simplification means" refers to a system function that enables reordering with a single operation.

[0165] This invention is a system that provides personalized menus based on the user's preferences and health status, and offers a dining experience that takes the user's emotional state into consideration. Users access the system using a terminal and engage in dialogue about their meals using natural language. The server is equipped with an emotion recognition function that analyzes voice and text data, and determines the user's emotional state in real time.

[0166] Specifically, the server collects user input data and uses natural language processing technology to detect changes in emotion. The software used here includes speech data processing technology for speech recognition and a natural language processing engine. If the user is feeling down, the emotion recognition system suggests a corresponding menu. For example, relaxing beverages or warming foods might be offered as menu items.

[0167] Furthermore, the interactive interface adjusts its responses according to the user's emotional state. The terminal provides polite and warm dialogue tailored to the user's condition, making the ordering process smooth. For example, if the user inputs "I'm tired today," the server will suggest foods suitable for nutritional replenishment.

[0168] Furthermore, the server is equipped with algorithms to optimize delivery routes, resulting in the most efficient delivery system. This makes it possible to significantly reduce delivery times.

[0169] As a concrete example, the following prompt statement can be used:

[0170] "I'm so busy today, I want a relaxing drink."

[0171] "I want to try some new dishes. Do you have any recommendations?"

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user accesses the system through their terminal and performs initial input.

[0175] In terms of specific operations, the terminal receives text or voice input from the user and converts it into digital data. This input includes information about preferences and the user's mood for the day. Examples of input data include natural language phrases such as "How are you feeling today?" or "I'm tired." As output, this data is sent to the server.

[0176] Step 2:

[0177] The server analyzes the received data using emotion recognition technology to determine the user's emotional state.

[0178] Specifically, the server uses a natural language processing engine to convert speech data into text data. Furthermore, it analyzes the text data to determine the emotional state. For example, if the user inputs "tired," the emotional data "fatigue" will be output.

[0179] Step 3:

[0180] The server generates personalized menus based on the emotional state.

[0181] In terms of specific operations, the server uses information processing tools to analyze the user's health and preference information in combination with emotional data. An algorithm is then applied to create the optimal menu, and specific recommended menu items such as "protein-rich salad" or "herbal tea" are output.

[0182] Step 4:

[0183] The server sends the generated menu information to the terminal and presents it to the user through an interactive interface.

[0184] In terms of specific actions, the device displays suggestions to the user in a natural and friendly tone. For example, a message like, "If you're feeling tired today, why not recharge your energy with this recommended salad?" might be displayed.

[0185] Step 5:

[0186] The user selects a menu item from the suggestions and confirms their order.

[0187] In terms of specific operations, the terminal receives the user's selection and sends that information to the server. The input is a specific menu item being selected, and the output is the order information being processed on the server.

[0188] Step 6:

[0189] After an order is confirmed, the server optimizes the delivery route and arranges for delivery.

[0190] Specifically, the server uses route optimization techniques to calculate the most appropriate delivery route. The output is a delivery plan showing the shortest distance and fastest time, which is then shared with the delivery team.

[0191] (Application Example 2)

[0192] 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".

[0193] In today's world, a challenge exists in that the food selection and ordering processes do not adequately consider the emotional and health states of individual users, resulting in an inability to provide the optimal user experience. Furthermore, the optimization of the delivery process is insufficient, leading to problems with fast and effective delivery. To address these challenges, a system is needed that can recognize user emotions in real time and provide suggestions and support based on those emotions.

[0194] 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.

[0195] In this invention, the server includes data processing means for collecting preference information and health information and generating personalized food choices based on this information; emotion recognition means for interacting with the user, assisting with orders using natural language, and analyzing the user's emotional state; route optimization means for optimizing delivery routes and shortening delivery times; and suggestion adjustment means for adjusting food choices according to the emotional state. This makes it possible to adapt to the user's emotional state and achieve personalized food selection and rapid delivery.

[0196] "Preference information" refers to data that shows users' preferences and selection tendencies regarding food.

[0197] "Health information" refers to data about the user's health status and nutrition.

[0198] "Data processing means" refers to a function that performs processing to generate food options based on the collected information.

[0199] "Natural language" refers to the linguistic forms that humans use on a daily basis, and the forms that computers use for analysis and generation.

[0200] An "interactive interface means" is a function that allows users and systems to exchange information with each other via natural language.

[0201] "Emotion recognition means" refers to a function that analyzes the user's emotional state from voice and text data.

[0202] A "route optimization method" is a function that calculates the optimal route to maximize time efficiency in the delivery process.

[0203] A "suggestion adjustment mechanism" is a function that adjusts the food options suggested based on the user's emotional state.

[0204] To realize this invention, the system implements a program that includes the following elements: The server collects preference information and health information and processes it to generate personalized food choices based on this information. As a data processing means, for example, a machine learning framework such as TENSORFLOW® is used to perform optimal food suggestions according to the user's preferences and health condition.

[0205] Interaction with the user takes place through an interactive interface built into the device. This utilizes mobile application development frameworks such as Flutter® and React Native, and employs Dialogflow or Azure® Cognitive Services for natural language processing. This enables emotion recognition through natural conversations with the user, and further allows for the adjustment of responses and suggestions based on the user's emotional state using the emotion recognition tools.

[0206] Route optimization in the delivery process is performed by the server using the Google Maps API. This reflects real-time traffic conditions and selects the best route based on emotional state and whether the customer is in a hurry.

[0207] For example, if a user says to their device, "Today was a really busy day," the device analyzes their stress level using emotion recognition technology. Based on this, it suggests a food menu that can quickly restore energy and delivers it via the shortest route.

[0208] An example of a prompt for a generative AI model is: "If the user is tired, generate a menu suggestion for relaxation. Also, generate emotionally responsive dialogue and suggest the fastest delivery route."

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The user inputs information into the terminal using natural language. This input data, including the user's voice and text information, is collected by an interactive interface. Specifically, the terminal receives data from the microphone or keyboard.

[0212] Step 2:

[0213] The device sends collected voice or text data to the server, where it is analyzed using emotion recognition technology. The input information is processed using Dialogflow or Azure Cognitive Services to analyze the user's emotional state. In this process, the server processes the data and extracts emotions (e.g., stress, joy, depression) from the text and voice data.

[0214] Step 3:

[0215] The server uses the analyzed emotional state and data processing tools to generate personalized food choices. TensorFlow is used to suggest menus based on the user's preferences, health information, and emotional state. In this step, emotional and health information is input, and personalized food choices are output.

[0216] Step 4:

[0217] The terminal presents the user with a list of generated food options. The user selects their desired items from these options and confirms their order. Throughout this process, the interactive interface visualizes and presents the options to the user, and performs interactions to accept their selection.

[0218] Step 5:

[0219] The server uses route optimization techniques to create a delivery plan based on confirmed order information. It uses the Google Maps API to calculate the optimal delivery route, taking real-time traffic information into account. The inputs in this step are order information and the user's emotional state, and the output is the optimized delivery route.

[0220] Step 6:

[0221] The terminal notifies the user of the estimated delivery time upon order confirmation. Specifically, the terminal displays the generated delivery information on the screen and provides real-time feedback to the user on the status.

[0222] 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.

[0223] Data generation model 58 is a type of 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] This invention is a system that provides highly accurate food menu suggestions based on user preference and health information, offers a natural ordering experience through an interactive interface, and achieves efficient delivery through route optimization means.

[0239] Management of preference and health information

[0240] The server collects preference and health information when a user first uses the system and stores it in a database. Users can register their preferences, allergies, and nutritional goals through their device. The system also integrates with wearable electronic devices, periodically sending data to the server to track the user's daily exercise and health status. This data is used to support the user's health maintenance.

[0241] Proposal for personalized menus

[0242] The server generates personalized menus based on each user's stored information. Specifically, it proposes menus designed to optimize the nutritional balance for the day, taking into account preferences and health goals. In addition, past order history is also included in the calculations, enabling even more personalized suggestions.

[0243] Order support through an interactive interface

[0244] The terminal enables natural interaction with the user. Specifically, when the user makes comments or asks questions about the suggested menu, it immediately provides corresponding answers to help them make the best choice. Once the user completes their selection, the order details are sent to the server. Furthermore, for repeat orders, users can easily do so with a single click from their past order history.

[0245] Optimization and efficiency of delivery routes

[0246] Once an order is confirmed, the server calculates the optimal delivery route based on that information and issues instructions to the delivery partner. It analyzes traffic information and order congestion in real time to achieve the shortest possible delivery time. This allows users to receive their goods quickly.

[0247] Specific example

[0248] For example, if a user prefers vegan food, the server searches for new vegan menu items that suit their preference and makes suggestions based on the user's nutritional goals. Once the order is complete, the server selects the nearest delivery partner from a vegan-friendly restaurant and provides the shortest route. This entire process enables the delivery of healthy and efficient meals.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user logs into the system via their device, enters their preferences, allergy information, and nutritional goals, and completes the initial setup. The device then sends the entered information to the server.

[0252] Step 2:

[0253] The server stores the received user information in a database and generates profiles of preference patterns and health goals. If necessary, it starts collecting data from wearable electronic devices.

[0254] Step 3:

[0255] When a user attempts to order food, a request is made from their device and sent to the server. The server utilizes the user profile to generate personalized suggestions from the available menu.

[0256] Step 4:

[0257] The server generates a personalized menu and sends it to the terminal, which then displays it to the user. The user reviews the suggested menu and asks questions or confirms details through an interactive interface.

[0258] Step 5:

[0259] Once the user confirms their order, the terminal sends the details to the server. Based on the order information, the server begins calculations to determine the optimal delivery route and assign a delivery person.

[0260] Step 6:

[0261] The server communicates optimized routes and instructions to delivery partners and initiates the delivery process. Users receive immediate notification of the estimated delivery time.

[0262] Step 7:

[0263] A delivery partner picks up the food and heads to the destination along the designated route. Users can check the current delivery status in real time via their terminal.

[0264] Step 8:

[0265] Users receive their products and provide feedback on quality and service on their devices. The server stores the received feedback in a database and uses it to improve services in the future.

[0266] (Example 1)

[0267] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] The challenge lies in providing appropriate food selection suggestions based on users' individual preferences and health information, along with an efficient ordering process and prompt delivery. In particular, it is necessary to manage information, optimize delivery routes, and eliminate the hassle of reordering, thereby providing users with a consistent, convenient, and effective service.

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

[0270] In this invention, the server includes information processing means for collecting preference information and health information and generating personalized food menus based on this information; interactive interface means for interacting with the user in natural language and assisting with ordering; route optimization means for calculating the optimal delivery route after order confirmation and shortening delivery time; means for making menu suggestions that take past order history into consideration; and means for making food suggestions using a generative model. This enables optimal food suggestions that take into account the user's health condition and an efficient ordering and delivery process.

[0271] "Preference information" refers to information that includes individual users' food preferences, allergies, and special requests regarding meals.

[0272] "Health information" refers to information including the user's current health status, nutritional goals, and any medical restrictions or goals.

[0273] An "information processing means" is a process that has the function of generating personalized food menus based on preference information and health information collected from users.

[0274] An "interactive interface means" is an interface function that interacts with users via natural language and assists them in placing orders.

[0275] A "route optimization method" is a function that calculates the most efficient route for delivery, thereby minimizing delivery time as much as possible.

[0276] A "generative model" is a computational model that uses machine learning or other advanced algorithms to generate new proposals.

[0277] "Order history" refers to a record of orders placed by a user in the past, and this information is retained to help with future suggestions.

[0278] The embodiments for carrying out the present invention are as follows.

[0279] The server is equipped with information processing means to efficiently collect preference and health information and generate personalized food menus. This means stores the user's preference and health information using a database and analyzes this data using a generation AI model to propose the most suitable menu for the user. Specifically, the server takes the user's past order history into consideration and generates menus that align with their preferences and nutritional goals.

[0280] The terminal is equipped with an interactive interface to support natural conversations with users. This interface uses natural language processing technology to respond to user questions and feedback in real time and assist in making appropriate orders. For example, if a user asks, "What are today's recommendations?", the terminal will, based on that inquiry, present today's recommendations from the suggested menu.

[0281] Once an order is confirmed, the server uses route optimization techniques to optimize the delivery route and ensure fast delivery. This involves analyzing traffic information in real time to reduce delivery times. For example, if a user who prefers vegan food orders a vegan-only menu item, the server calculates the shortest delivery route from a vegan-friendly restaurant.

[0282] Furthermore, by linking with wearable electronic devices, the server can track the user's daily exercise levels and calorie consumption, and store this information as health data. This information can be used to help users maintain their health and, for example, reflects in menu suggestions that take into account the user's health goals, such as "I want a high-protein diet."

[0283] A concrete example of a prompt message is when a user inputs "Please suggest a vegan, high-protein breakfast menu" to the AI ​​generation model, allowing the server to suggest a menu suitable for the user. This process improves the user experience and supports healthy and efficient food delivery.

[0284] The process of the specific processing in Example 1 will be described using FIG. 11.

[0285] Step 1: Collection of preference information and health information

[0286] The user inputs their preference information and health information through the terminal. Specific information includes food preferences, allergy information, nutritional goals, etc. The terminal transmits this input information to the server, and the server saves it in the database. The input is the information from the user, and the output is the saving to the database.

[0287] Step 2: Acquisition of health data

[0288] The server receives the user's health data from the wearable electronic device. This health data includes amount of exercise, calories consumed, heart rate, etc. The server analyzes the received data and records it in the database as health information. The input is the data from the wearable electronic device, and the output is the analyzed health information.

[0289] Step 3: Generation of individualized menu

[0290] The server generates an individualized menu based on the accumulated preference information, health information, and past order history. Using a generation AI model, a prompt sentence is created to propose a menu suitable for the user. The input is various information in the database, and the output is the individualized proposed menu.

[0291] Step 4: Order assistance through an interactive interface

[0292] The user makes inquiries and selections regarding the proposed menu through the terminal. For example, they can ask "Does this dish contain nuts?" The terminal analyzes the input and provides an appropriate answer. The input is the user's question, and the output is the answer information.

[0293] Step 5: Order confirmation and optimization of delivery route

[0294] Once an order is confirmed, the server calculates the optimal delivery route. During delivery, it analyzes traffic information in real time, selects the fastest route, and instructs the delivery partner accordingly. The input is the confirmed order information, and the output is the optimized delivery route.

[0295] Step 6: Update Order History

[0296] The server updates the user's order history in the database after delivery is complete. This historical data is used to make future suggestions. The input is the order data associated with the completed delivery, and the output is the updated order history.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] There is a need to more effectively suggest meals tailored to users' preferences and health conditions, and a challenge to reduce the effort involved in selecting ingredients and ordering, as well as to expedite delivery. Furthermore, given the demand for health management and efficient time management in modern society, a system that addresses these issues is necessary.

[0300] 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.

[0301] In this invention, the server includes information processing means for collecting information related to preferences and health information, and generating an individualized diet plan based on this; dialogue interface means for interacting with the user in a conversation format and assisting in selections using natural language; route optimization means for optimizing the route to shorten the travel time by means of transportation; cooperation means for cooperating with a smart device to grasp the user's exercise volume and nutrition volume and manage health information; and operation omission means for making food selection and re-implementation possible with a single instruction. As a result, it becomes possible to propose an optimized and healthy diet for the user and provide prompt and efficient delivery.

[0302] The "information related to preferences" is data indicating the types of ingredients and dishes that each user personally prefers and the taste trends.

[0303] The "health information" is data related to the health status of each user, specifically including allergy information, nutritional indicators, health goals, and the like.

[0304] The "information processing means" is a system or device having a function of generating an individualized diet plan based on the collected information related to preferences and health.

[0305] The "dialogue interface means" is a technology or method for supporting orders and selections through natural conversations with users.

[0306] The "route optimization means" is a technology having the ability to calculate and propose an optimal route to shorten the travel time for delivery.

[0307] The "cooperation means" is a mechanism for communicating with a smart device, managing health information such as the user's exercise volume and nutrition volume, and incorporating it into the system.

[0308] The "operation omission means" is a function for enabling food selection and re-implementation with a single instruction and reducing the user's effort.

[0309] In this invention, the server utilizes a series of information processing means to provide users with food menus optimized for them, thereby realizing an efficient delivery service. Specifically, the server collects preference and health information and generates personalized meal plans based on this information. The program uses a MySQL database as its main backend technology for data storage and management. Furthermore, it collaborates with wearable devices to collect information, using APIs such as Fitbit to acquire data on exercise levels and health status, and reflects this data in health management directed to the server.

[0310] The terminal uses a speech recognition system such as Dialogflow to provide an interactive interface through natural conversation with the user. This allows users to ask questions about food and nutrition in natural language and receive appropriate answers and menu suggestions. Users can view the menu on the system and confirm their order with a single click. For repeat orders, the process is streamlined based on past selections using a simplified procedure.

[0311] For delivery, the server utilizes map information such as the Google Maps API and employs route optimization techniques to analyze real-time traffic information and select the route that minimizes delivery time by mode of transport. This ensures that products are delivered to the user quickly.

[0312] A concrete example of a prompt would be, "Based on the user's health data, suggest a suitable vegan menu for Monday's lunch." Through such prompts, the system can respond and provide the user with optimal food suggestions.

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server receives preference and health information from the user as input. This information is registered by the user via a terminal and stored in a database. Based on the input information, the server updates the database and optimizes it according to the user's health status and nutritional goals. This forms the basis for subsequent meal plan suggestions.

[0316] Step 2:

[0317] The server periodically collects health-related data from smart devices, including exercise data from wearable devices such as Fitbit. The server takes health information as input, analyzes the data, and monitors the user's health status by comparing it to past data. As a result, it can provide nutritional suggestions tailored to the user's health fluctuations.

[0318] Step 3:

[0319] The server generates personalized meal plans based on collected preference, health, and exercise data. Using a generation AI model, it leverages prompts to derive optimal menus, for example, following instructions such as "Please suggest vegan menus." The generated plan is sent to the user's terminal in a format that the user can review.

[0320] Step 4:

[0321] The terminal uses an interactive interface to receive additional questions and requests from the user. It receives user questions in natural language as input, analyzes them using Dialogflow, and generates appropriate answers. This process resolves any uncertainties the user may have regarding ingredients.

[0322] Step 5:

[0323] The user reviews the menu suggested by the terminal and decides on their order. The user's selection is collected as input, received by the server, and the order information is processed. This prepares the server to select the most suitable delivery partner.

[0324] Step 6:

[0325] The server uses the Google Maps API to collect traffic information and optimize routes. Together with selected delivery partners, it calculates the shortest and most optimal delivery route. This process outputs the fastest possible delivery time, establishing a plan for quick delivery to the user.

[0326] 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.

[0327] This invention combines a system that provides personalized menus based on user preference and health information with an emotion engine that recognizes the user's emotional state. This system enables real-time order support and delivery optimization through natural language dialogue with the user.

[0328] Emotional engine integration

[0329] The server features an emotion engine that analyzes voice and text data to recognize the user's emotional state in real time. While the user is browsing the menu, the terminal monitors the conversation and detects changes in emotion. This information is sent to the server and used to adjust the suggested menu. For example, if the user is feeling stressed, the system will prioritize suggesting foods with relaxing effects.

[0330] Emotion-based menu suggestions

[0331] Based on feedback from the emotion engine, the server adjusts menu selections to match the user's psychological state. This adjustment allows for a dining experience that takes the user's emotional state into consideration. If the user is excited, the server enhances the positive experience by suggesting new dishes or exotic options.

[0332] Adjusting the response of interactive interfaces

[0333] The interactive interface adapts its responses based on the user's emotions. If the user is feeling down, the conversation shifts to a more friendly and encouraging tone to lift their spirits. In this way, the user gains an experience that goes beyond simply placing an order.

[0334] Delivery optimization and feedback analysis

[0335] Once an order is confirmed, the server selects the optimal delivery route and delivery person, taking into account the user's emotional state. If the user is in a hurry, the system prioritizes the fastest delivery route, ensuring a quick response to user needs. Furthermore, user feedback is analyzed by an emotion engine, and actions that reinforce positive emotions are incorporated into future suggestions.

[0336] Specific example

[0337] When a user tells their device, "Today was a really busy day," the system detects stress through its emotion engine. The server then quickly responds to the user's needs by suggesting an energy-restoring menu for busy days and offering the shortest delivery route option.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The user activates the device and logs into the system. The device provides an interface for collecting the user's preferences, health information, and emotional state.

[0341] Step 2:

[0342] The server receives the login information and retrieves the corresponding user profile from the database. This profile includes information related to past order history, preferences, and health status.

[0343] Step 3:

[0344] The device activates its emotion engine and analyzes emotions from the user's voice or text messages. The device then sends this emotion data to the server.

[0345] Step 4:

[0346] The server integrates emotional data with existing user profiles to generate personalized menus. The suggested menus are adjusted to reflect the user's current emotional state.

[0347] Step 5:

[0348] The terminal displays a personalized menu received from the server to the user. It utilizes an interactive interface that allows the user to review the menu and ask questions or place orders using natural language.

[0349] Step 6:

[0350] Once the user confirms their order, the terminal sends that information to the server. The server receives the order and begins the delivery process, taking into account the user's emotional state.

[0351] Step 7:

[0352] The server calculates the optimal delivery route and arranges expedited delivery based on analysis by the emotion engine. If necessary, it sends instructions to delivery partners.

[0353] Step 8:

[0354] Users receive their delivered goods and provide emotionally charged feedback on their devices. The server analyzes this feedback and uses it to improve future services.

[0355] (Example 2)

[0356] 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".

[0357] In today's diverse dietary landscape, there is a demand for personalized food products that take into account the health and emotional states of consumers. However, current systems lack effective means of reflecting emotional states. Furthermore, methods for expediting delivery and utilizing user feedback to improve future recommendations are limited.

[0358] 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.

[0359] In this invention, the server includes information processing means for collecting preference information and health information to generate personalized food menus, emotion recognition means for analyzing voice and text data to recognize emotional states, and information feedback means for analyzing user feedback and reflecting it in future suggestions. This enables the provision of personalized menus that take into account the user's psychological state and the improvement of the cycle based on feedback.

[0360] "Information processing means" refers to a device or program that has the function of collecting user preference information and health information and generating personalized menus based on this information.

[0361] An "interactive interface means" is a component of a system that interacts with users using natural language to assist with ordering.

[0362] "Emotion recognition means" refers to a technology that analyzes the user's voice and text data to recognize their emotional state in real time and reflect it in menu suggestions.

[0363] "Route optimization means" refers to route calculation algorithms that effectively calculate delivery routes and shorten delivery times.

[0364] An "information feedback mechanism" is a function that analyzes user feedback, incorporates it into future proposals, and aims to improve the service.

[0365] A "wearable electronic device" is an electronic device that can be directly attached to the user's body and is used to manage energy consumption and nutrient intake.

[0366] "Order simplification means" refers to a system function that enables reordering with a single operation.

[0367] This invention is a system that provides personalized menus based on the user's preferences and health status, and offers a dining experience that takes the user's emotional state into consideration. Users access the system using a terminal and engage in dialogue about their meals using natural language. The server is equipped with an emotion recognition function that analyzes voice and text data, and determines the user's emotional state in real time.

[0368] Specifically, the server collects user input data and uses natural language processing technology to detect changes in emotion. The software used here includes speech data processing technology for speech recognition and a natural language processing engine. If the user is feeling down, the emotion recognition system suggests a corresponding menu. For example, relaxing beverages or warming foods might be offered as menu items.

[0369] Furthermore, the interactive interface adjusts its responses according to the user's emotional state. The terminal provides polite and warm dialogue tailored to the user's condition, making the ordering process smooth. For example, if the user inputs "I'm tired today," the server will suggest foods suitable for nutritional replenishment.

[0370] Furthermore, the server is equipped with algorithms to optimize delivery routes, resulting in the most efficient delivery system. This makes it possible to significantly reduce delivery times.

[0371] As a concrete example, the following prompt statement can be used:

[0372] "I'm so busy today, I want a relaxing drink."

[0373] "I want to try some new dishes. Do you have any recommendations?"

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The user accesses the system through their terminal and performs initial input.

[0377] In terms of specific operations, the terminal receives text or voice input from the user and converts it into digital data. This input includes information about preferences and the user's mood for the day. Examples of input data include natural language phrases such as "How are you feeling today?" or "I'm tired." As output, this data is sent to the server.

[0378] Step 2:

[0379] The server analyzes the received data using emotion recognition technology to determine the user's emotional state.

[0380] Specifically, the server uses a natural language processing engine to convert speech data into text data. Furthermore, it analyzes the text data to determine the emotional state. For example, if the user inputs "tired," the emotional data "fatigue" will be output.

[0381] Step 3:

[0382] The server generates personalized menus based on the emotional state.

[0383] In terms of specific operations, the server uses information processing tools to analyze the user's health and preference information in combination with emotional data. An algorithm is then applied to create the optimal menu, and specific recommended menu items such as "protein-rich salad" or "herbal tea" are output.

[0384] Step 4:

[0385] The server sends the generated menu information to the terminal and presents it to the user through an interactive interface.

[0386] In terms of specific actions, the device displays suggestions to the user in a natural and friendly tone. For example, a message like, "If you're feeling tired today, why not recharge your energy with this recommended salad?" might be displayed.

[0387] Step 5:

[0388] The user selects a menu item from the suggestions and confirms their order.

[0389] In terms of specific operations, the terminal receives the user's selection and sends that information to the server. The input is a specific menu item being selected, and the output is the order information being processed on the server.

[0390] Step 6:

[0391] After an order is confirmed, the server optimizes the delivery route and arranges for delivery.

[0392] Specifically, the server uses route optimization techniques to calculate the most appropriate delivery route. The output is a delivery plan showing the shortest distance and fastest time, which is then shared with the delivery team.

[0393] (Application Example 2)

[0394] 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."

[0395] In today's world, a challenge exists in that the food selection and ordering processes do not adequately consider the emotional and health states of individual users, resulting in an inability to provide the optimal user experience. Furthermore, the optimization of the delivery process is insufficient, leading to problems with fast and effective delivery. To address these challenges, a system is needed that can recognize user emotions in real time and provide suggestions and support based on those emotions.

[0396] 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.

[0397] In this invention, the server includes data processing means for collecting preference information and health information and generating personalized food choices based on this information; emotion recognition means for interacting with the user, assisting with orders using natural language, and analyzing the user's emotional state; route optimization means for optimizing delivery routes and shortening delivery times; and suggestion adjustment means for adjusting food choices according to the emotional state. This makes it possible to adapt to the user's emotional state and achieve personalized food selection and rapid delivery.

[0398] "Preference information" refers to data that shows users' preferences and selection tendencies regarding food.

[0399] "Health information" refers to data about the user's health status and nutrition.

[0400] "Data processing means" refers to a function that performs processing to generate food options based on the collected information.

[0401] "Natural language" refers to the linguistic forms that humans use on a daily basis, and the forms that computers use for analysis and generation.

[0402] An "interactive interface means" is a function that allows users and systems to exchange information with each other via natural language.

[0403] "Emotion recognition means" refers to a function that analyzes the user's emotional state from voice and text data.

[0404] A "route optimization method" is a function that calculates the optimal route to maximize time efficiency in the delivery process.

[0405] A "suggestion adjustment mechanism" is a function that adjusts the food options suggested based on the user's emotional state.

[0406] To realize this invention, the system implements a program that includes the following elements: The server collects preference information and health information and processes it to generate personalized food choices based on this information. As a data processing means, for example, a machine learning framework such as TensorFlow is used to perform optimal food suggestions according to the user's preferences and health condition.

[0407] Interaction with the user takes place through an interactive interface built into the device. This utilizes mobile application development frameworks such as Flutter and React Native, and employs Dialogflow or Azure Cognitive Services for natural language processing. This allows for emotion recognition through natural conversations with the user, and further enables the adjustment of responses and suggestions based on the user's emotional state using emotion recognition tools.

[0408] Route optimization in the delivery process is performed by the server using the Google Maps API. This reflects real-time traffic conditions and selects the best route based on emotional state and whether the customer is in a hurry.

[0409] For example, if a user says to their device, "Today was a really busy day," the device analyzes their stress level using emotion recognition technology. Based on this, it suggests a food menu that can quickly restore energy and delivers it via the shortest route.

[0410] An example of a prompt for a generative AI model is: "If the user is tired, generate a menu suggestion for relaxation. Also, generate emotionally responsive dialogue and suggest the fastest delivery route."

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The user inputs information into the terminal using natural language. This input data, including the user's voice and text information, is collected by an interactive interface. Specifically, the terminal receives data from the microphone or keyboard.

[0414] Step 2:

[0415] The device sends collected voice or text data to the server, where it is analyzed using emotion recognition technology. The input information is processed using Dialogflow or Azure Cognitive Services to analyze the user's emotional state. In this process, the server processes the data and extracts emotions (e.g., stress, joy, depression) from the text and voice data.

[0416] Step 3:

[0417] The server uses the analyzed emotional state and data processing tools to generate personalized food choices. TensorFlow is used to suggest menus based on the user's preferences, health information, and emotional state. In this step, emotional and health information is input, and personalized food choices are output.

[0418] Step 4:

[0419] The terminal presents the user with a list of generated food options. The user selects their desired items from these options and confirms their order. Throughout this process, the interactive interface visualizes and presents the options to the user, and performs interactions to accept their selection.

[0420] Step 5:

[0421] The server uses route optimization techniques to create a delivery plan based on confirmed order information. It uses the Google Maps API to calculate the optimal delivery route, taking real-time traffic information into account. The inputs in this step are order information and the user's emotional state, and the output is the optimized delivery route.

[0422] Step 6:

[0423] The terminal notifies the user of the estimated delivery time upon order confirmation. Specifically, the terminal displays the generated delivery information on the screen and provides real-time feedback to the user on the status.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] This invention is a system that provides highly accurate food menu suggestions based on user preference and health information, offers a natural ordering experience through an interactive interface, and achieves efficient delivery through route optimization means.

[0441] Management of preference and health information

[0442] The server collects preference and health information when a user first uses the system and stores it in a database. Users can register their preferences, allergies, and nutritional goals through their device. The system also integrates with wearable electronic devices, periodically sending data to the server to track the user's daily exercise and health status. This data is used to support the user's health maintenance.

[0443] Proposal for personalized menus

[0444] The server generates personalized menus based on each user's stored information. Specifically, it proposes menus designed to optimize the nutritional balance for the day, taking into account preferences and health goals. In addition, past order history is also included in the calculations, enabling even more personalized suggestions.

[0445] Order support through an interactive interface

[0446] The terminal enables natural interaction with the user. Specifically, when the user makes comments or asks questions about the suggested menu, it immediately provides corresponding answers to help them make the best choice. Once the user completes their selection, the order details are sent to the server. Furthermore, for repeat orders, users can easily do so with a single click from their past order history.

[0447] Optimization and efficiency of delivery routes

[0448] Once an order is confirmed, the server calculates the optimal delivery route based on that information and issues instructions to the delivery partner. It analyzes traffic information and order congestion in real time to achieve the shortest possible delivery time. This allows users to receive their goods quickly.

[0449] Specific example

[0450] For example, if a user prefers vegan food, the server searches for new vegan menu items that suit their preference and makes suggestions based on the user's nutritional goals. Once the order is complete, the server selects the nearest delivery partner from a vegan-friendly restaurant and provides the shortest route. This entire process enables the delivery of healthy and efficient meals.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The user logs into the system via their device, enters their preferences, allergy information, and nutritional goals, and completes the initial setup. The device then sends the entered information to the server.

[0454] Step 2:

[0455] The server stores the received user information in a database and generates profiles of preference patterns and health goals. If necessary, it starts collecting data from wearable electronic devices.

[0456] Step 3:

[0457] When a user attempts to order food, a request is made from their device and sent to the server. The server utilizes the user profile to generate personalized suggestions from the available menu.

[0458] Step 4:

[0459] The server generates a personalized menu and sends it to the terminal, which then displays it to the user. The user reviews the suggested menu and asks questions or confirms details through an interactive interface.

[0460] Step 5:

[0461] Once the user confirms their order, the terminal sends the details to the server. Based on the order information, the server begins calculations to determine the optimal delivery route and assign a delivery person.

[0462] Step 6:

[0463] The server communicates optimized routes and instructions to delivery partners and initiates the delivery process. Users receive immediate notification of the estimated delivery time.

[0464] Step 7:

[0465] A delivery partner picks up the food and heads to the destination along the designated route. Users can check the current delivery status in real time via their terminal.

[0466] Step 8:

[0467] Users receive their products and provide feedback on quality and service on their devices. The server stores the received feedback in a database and uses it to improve services in the future.

[0468] (Example 1)

[0469] 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."

[0470] The challenge lies in providing appropriate food selection suggestions based on users' individual preferences and health information, along with an efficient ordering process and prompt delivery. In particular, it is necessary to manage information, optimize delivery routes, and eliminate the hassle of reordering, thereby providing users with a consistent, convenient, and effective service.

[0471] 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.

[0472] In this invention, the server includes information processing means for collecting preference information and health information and generating personalized food menus based on this information; interactive interface means for interacting with the user in natural language and assisting with ordering; route optimization means for calculating the optimal delivery route after order confirmation and shortening delivery time; means for making menu suggestions that take past order history into consideration; and means for making food suggestions using a generative model. This enables optimal food suggestions that take into account the user's health condition and an efficient ordering and delivery process.

[0473] "Preference information" refers to information that includes individual users' food preferences, allergies, and special requests regarding meals.

[0474] "Health information" refers to information including the user's current health status, nutritional goals, and any medical restrictions or goals.

[0475] An "information processing means" is a process that has the function of generating personalized food menus based on preference information and health information collected from users.

[0476] An "interactive interface means" is an interface function that interacts with users via natural language and assists them in placing orders.

[0477] A "route optimization method" is a function that calculates the most efficient route for delivery, thereby minimizing delivery time as much as possible.

[0478] A "generative model" is a computational model that uses machine learning or other advanced algorithms to generate new proposals.

[0479] "Order history" refers to a record of orders placed by a user in the past, and this information is retained to help with future suggestions.

[0480] The embodiments for carrying out the present invention are as follows.

[0481] The server is equipped with information processing means to efficiently collect preference and health information and generate personalized food menus. This means stores the user's preference and health information using a database and analyzes this data using a generation AI model to propose the most suitable menu for the user. Specifically, the server takes the user's past order history into consideration and generates menus that align with their preferences and nutritional goals.

[0482] The terminal is equipped with an interactive interface to support natural conversations with users. This interface uses natural language processing technology to respond to user questions and feedback in real time and assist in making appropriate orders. For example, if a user asks, "What are today's recommendations?", the terminal will, based on that inquiry, present today's recommendations from the suggested menu.

[0483] Once an order is confirmed, the server uses route optimization techniques to optimize the delivery route and ensure fast delivery. This involves analyzing traffic information in real time to reduce delivery times. For example, if a user who prefers vegan food orders a vegan-only menu item, the server calculates the shortest delivery route from a vegan-friendly restaurant.

[0484] Furthermore, by linking with wearable electronic devices, the server can track the user's daily exercise levels and calorie consumption, and store this information as health data. This information can be used to help users maintain their health and, for example, reflects in menu suggestions that take into account the user's health goals, such as "I want a high-protein diet."

[0485] A concrete example of a prompt message is when a user inputs "Please suggest a vegan, high-protein breakfast menu" to the AI ​​generation model, allowing the server to suggest a menu suitable for the user. This process improves the user experience and supports healthy and efficient food delivery.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1: Gathering information on preferences and health.

[0488] Users input their preferences and health information through a terminal. Specific information includes food preferences, allergy information, and nutritional goals. The terminal sends this input information to a server, which stores it in a database. The input is information from the user, and the output is storage in the database.

[0489] Step 2: Obtain health data

[0490] The server receives user health data from wearable electronic devices. This health data includes activity levels, calories burned, heart rate, etc. The server analyzes the received data and records it in a database as health information. The input is data from the wearable electronic device, and the output is the analyzed health information.

[0491] Step 3: Generating a personalized menu

[0492] The server generates personalized menus based on accumulated preference information, health information, and past order history. Using a generation AI model, it creates prompt messages and suggests menus suitable for the user. The input consists of various database entries, and the output is a personalized suggested menu.

[0493] Step 4: Order support via interactive interface

[0494] The user makes inquiries and selections regarding the suggested menu items through the terminal. For example, they can ask, "Does this dish contain nuts?" The terminal analyzes the input and provides an appropriate answer. The input is the user's question, and the output is the answer information.

[0495] Step 5: Order Confirmation and Optimization of Delivery Routes

[0496] Once an order is confirmed, the server calculates the optimal delivery route. During delivery, it analyzes traffic information in real time, selects the fastest route, and instructs the delivery partner accordingly. The input is the confirmed order information, and the output is the optimized delivery route.

[0497] Step 6: Update Order History

[0498] The server updates the user's order history in the database after delivery is complete. This historical data is used to make future suggestions. The input is the order data associated with the completed delivery, and the output is the updated order history.

[0499] (Application Example 1)

[0500] 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."

[0501] There is a need to more effectively suggest meals tailored to users' preferences and health conditions, and a challenge to reduce the effort involved in selecting ingredients and ordering, as well as to expedite delivery. Furthermore, given the demand for health management and efficient time management in modern society, a system that addresses these issues is necessary.

[0502] 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.

[0503] This invention includes a server comprising: information processing means for collecting information on preferences and health and generating personalized meal plans based on this information; interactive interface means for engaging in conversational dialogue with the user and assisting with selections using natural language; route optimization means for optimizing paths and shortening travel time by means of transportation; cooperation means for understanding the user's exercise and nutritional intake in conjunction with a smart device and managing health information; and operation omission means for enabling food selection to be repeated with a single instruction. This enables the suggestion of healthy meals optimized for the user and rapid and efficient delivery.

[0504] "Information regarding preferences" refers to data that shows the types of ingredients and dishes that each user personally likes, as well as their taste preferences.

[0505] "Health-related information" refers to data related to each user's health status, and specifically includes allergy information, nutritional indicators, and health goals.

[0506] "Information processing means" refers to systems or devices that have the function of generating personalized meal plans based on collected information on preferences and health.

[0507] "Interactive interface means" refers to technologies and methods for supporting ordering and selection through natural conversation with the user.

[0508] A "route optimization method" is a technology that has the ability to calculate and propose the optimal route in order to reduce travel time for delivery.

[0509] "Integration means" refers to a mechanism that communicates with smart devices to manage and incorporate users' health information, such as exercise levels and nutritional intake, into the system.

[0510] A "simplification of operation" is a function that allows users to repeat food selection and ordering with a single instruction, thereby reducing the effort required of the user.

[0511] In this invention, the server utilizes a series of information processing means to provide users with food menus optimized for them, thereby realizing an efficient delivery service. Specifically, the server collects preference and health information and generates personalized meal plans based on this information. The program uses a MySQL database as its main backend technology for data storage and management. Furthermore, it collaborates with wearable devices to collect information, using APIs such as Fitbit to acquire data on exercise levels and health status, and reflects this data in health management directed to the server.

[0512] The terminal uses a speech recognition system such as Dialogflow to provide an interactive interface through natural conversation with the user. This allows users to ask questions about food and nutrition in natural language and receive appropriate answers and menu suggestions. Users can view the menu on the system and confirm their order with a single click. For repeat orders, the process is streamlined based on past selections using a simplified procedure.

[0513] For delivery, the server utilizes map information such as the Google Maps API and employs route optimization techniques to analyze real-time traffic information and select the route that minimizes delivery time by mode of transport. This ensures that products are delivered to the user quickly.

[0514] A concrete example of a prompt would be, "Based on the user's health data, suggest a suitable vegan menu for Monday's lunch." Through such prompts, the system can respond and provide the user with optimal food suggestions.

[0515] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0516] Step 1:

[0517] The server receives preference and health information from the user as input. This information is registered by the user via a terminal and stored in a database. Based on the input information, the server updates the database and optimizes it according to the user's health status and nutritional goals. This forms the basis for subsequent meal plan suggestions.

[0518] Step 2:

[0519] The server periodically collects health-related data from smart devices, including exercise data from wearable devices such as Fitbit. The server takes health information as input, analyzes the data, and monitors the user's health status by comparing it to past data. As a result, it can provide nutritional suggestions tailored to the user's health fluctuations.

[0520] Step 3:

[0521] The server generates personalized meal plans based on collected preference, health, and exercise data. Using a generation AI model, it leverages prompts to derive optimal menus, for example, following instructions such as "Please suggest vegan menus." The generated plan is sent to the user's terminal in a format that the user can review.

[0522] Step 4:

[0523] The terminal uses an interactive interface to receive additional questions and requests from the user. It receives user questions in natural language as input, analyzes them using Dialogflow, and generates appropriate answers. This process resolves any uncertainties the user may have regarding ingredients.

[0524] Step 5:

[0525] The user reviews the menu suggested by the terminal and decides on their order. The user's selection is collected as input, received by the server, and the order information is processed. This prepares the server to select the most suitable delivery partner.

[0526] Step 6:

[0527] The server uses the Google Maps API to collect traffic information and optimize routes. Together with selected delivery partners, it calculates the shortest and most optimal delivery route. This process outputs the fastest possible delivery time, establishing a plan for quick delivery to the user.

[0528] 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.

[0529] This invention combines a system that provides personalized menus based on user preference and health information with an emotion engine that recognizes the user's emotional state. This system enables real-time order support and delivery optimization through natural language dialogue with the user.

[0530] Emotional engine integration

[0531] The server features an emotion engine that analyzes voice and text data to recognize the user's emotional state in real time. While the user is browsing the menu, the terminal monitors the conversation and detects changes in emotion. This information is sent to the server and used to adjust the suggested menu. For example, if the user is feeling stressed, the system will prioritize suggesting foods with relaxing effects.

[0532] Emotion-based menu suggestions

[0533] Based on feedback from the emotion engine, the server adjusts menu selections to match the user's psychological state. This adjustment allows for a dining experience that takes the user's emotional state into consideration. If the user is excited, the server enhances the positive experience by suggesting new dishes or exotic options.

[0534] Adjusting the response of interactive interfaces

[0535] The interactive interface adapts its responses based on the user's emotions. If the user is feeling down, the conversation shifts to a more friendly and encouraging tone to lift their spirits. In this way, the user gains an experience that goes beyond simply placing an order.

[0536] Delivery optimization and feedback analysis

[0537] Once an order is confirmed, the server selects the optimal delivery route and delivery person, taking into account the user's emotional state. If the user is in a hurry, the system prioritizes the fastest delivery route, ensuring a quick response to user needs. Furthermore, user feedback is analyzed by an emotion engine, and actions that reinforce positive emotions are incorporated into future suggestions.

[0538] Specific example

[0539] When a user tells their device, "Today was a really busy day," the system detects stress through its emotion engine. The server then quickly responds to the user's needs by suggesting an energy-restoring menu for busy days and offering the shortest delivery route option.

[0540] The following describes the processing flow.

[0541] Step 1:

[0542] The user activates the device and logs into the system. The device provides an interface for collecting the user's preferences, health information, and emotional state.

[0543] Step 2:

[0544] The server receives the login information and retrieves the corresponding user profile from the database. This profile includes information related to past order history, preferences, and health status.

[0545] Step 3:

[0546] The device activates its emotion engine and analyzes emotions from the user's voice or text messages. The device then sends this emotion data to the server.

[0547] Step 4:

[0548] The server integrates emotional data with existing user profiles to generate personalized menus. The suggested menus are adjusted to reflect the user's current emotional state.

[0549] Step 5:

[0550] The terminal displays a personalized menu received from the server to the user. It utilizes an interactive interface that allows the user to review the menu and ask questions or place orders using natural language.

[0551] Step 6:

[0552] Once the user confirms their order, the terminal sends that information to the server. The server receives the order and begins the delivery process, taking into account the user's emotional state.

[0553] Step 7:

[0554] The server calculates the optimal delivery route and arranges expedited delivery based on analysis by the emotion engine. If necessary, it sends instructions to delivery partners.

[0555] Step 8:

[0556] Users receive their delivered goods and provide emotionally charged feedback on their devices. The server analyzes this feedback and uses it to improve future services.

[0557] (Example 2)

[0558] 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."

[0559] In today's diverse dietary landscape, there is a demand for personalized food products that take into account the health and emotional states of consumers. However, current systems lack effective means of reflecting emotional states. Furthermore, methods for expediting delivery and utilizing user feedback to improve future recommendations are limited.

[0560] 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.

[0561] In this invention, the server includes information processing means for collecting preference information and health information to generate personalized food menus, emotion recognition means for analyzing voice and text data to recognize emotional states, and information feedback means for analyzing user feedback and reflecting it in future suggestions. This enables the provision of personalized menus that take into account the user's psychological state and the improvement of the cycle based on feedback.

[0562] "Information processing means" refers to a device or program that has the function of collecting user preference information and health information and generating personalized menus based on this information.

[0563] An "interactive interface means" is a component of a system that interacts with users using natural language to assist with ordering.

[0564] "Emotion recognition means" refers to a technology that analyzes the user's voice and text data to recognize their emotional state in real time and reflect it in menu suggestions.

[0565] "Route optimization means" refers to route calculation algorithms that effectively calculate delivery routes and shorten delivery times.

[0566] An "information feedback mechanism" is a function that analyzes user feedback, incorporates it into future proposals, and aims to improve the service.

[0567] A "wearable electronic device" is an electronic device that can be directly attached to the user's body and is used to manage energy consumption and nutrient intake.

[0568] "Order simplification means" refers to a system function that enables reordering with a single operation.

[0569] This invention is a system that provides personalized menus based on the user's preferences and health status, and offers a dining experience that takes the user's emotional state into consideration. Users access the system using a terminal and engage in dialogue about their meals using natural language. The server is equipped with an emotion recognition function that analyzes voice and text data, and determines the user's emotional state in real time.

[0570] Specifically, the server collects user input data and uses natural language processing technology to detect changes in emotion. The software used here includes speech data processing technology for speech recognition and a natural language processing engine. If the user is feeling down, the emotion recognition system suggests a corresponding menu. For example, relaxing beverages or warming foods might be offered as menu items.

[0571] Furthermore, the interactive interface adjusts its responses according to the user's emotional state. The terminal provides polite and warm dialogue tailored to the user's condition, making the ordering process smooth. For example, if the user inputs "I'm tired today," the server will suggest foods suitable for nutritional replenishment.

[0572] Furthermore, the server is equipped with algorithms to optimize delivery routes, resulting in the most efficient delivery system. This makes it possible to significantly reduce delivery times.

[0573] As a concrete example, the following prompt statement can be used:

[0574] "I'm so busy today, I want a relaxing drink."

[0575] "I want to try some new dishes. Do you have any recommendations?"

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The user accesses the system through their terminal and performs initial input.

[0579] In terms of specific operations, the terminal receives text or voice input from the user and converts it into digital data. This input includes information about preferences and the user's mood for the day. Examples of input data include natural language phrases such as "How are you feeling today?" or "I'm tired." As output, this data is sent to the server.

[0580] Step 2:

[0581] The server analyzes the received data using emotion recognition technology to determine the user's emotional state.

[0582] Specifically, the server uses a natural language processing engine to convert speech data into text data. Furthermore, it analyzes the text data to determine the emotional state. For example, if the user inputs "tired," the emotional data "fatigue" will be output.

[0583] Step 3:

[0584] The server generates personalized menus based on the emotional state.

[0585] In terms of specific operations, the server uses information processing tools to analyze the user's health and preference information in combination with emotional data. An algorithm is then applied to create the optimal menu, and specific recommended menu items such as "protein-rich salad" or "herbal tea" are output.

[0586] Step 4:

[0587] The server sends the generated menu information to the terminal and presents it to the user through an interactive interface.

[0588] In terms of specific actions, the device displays suggestions to the user in a natural and friendly tone. For example, a message like, "If you're feeling tired today, why not recharge your energy with this recommended salad?" might be displayed.

[0589] Step 5:

[0590] The user selects a menu item from the suggestions and confirms their order.

[0591] In terms of specific operations, the terminal receives the user's selection and sends that information to the server. The input is a specific menu item being selected, and the output is the order information being processed on the server.

[0592] Step 6:

[0593] After an order is confirmed, the server optimizes the delivery route and arranges for delivery.

[0594] Specifically, the server uses route optimization techniques to calculate the most appropriate delivery route. The output is a delivery plan showing the shortest distance and fastest time, which is then shared with the delivery team.

[0595] (Application Example 2)

[0596] 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."

[0597] In today's world, a challenge exists in that the food selection and ordering processes do not adequately consider the emotional and health states of individual users, resulting in an inability to provide the optimal user experience. Furthermore, the optimization of the delivery process is insufficient, leading to problems with fast and effective delivery. To address these challenges, a system is needed that can recognize user emotions in real time and provide suggestions and support based on those emotions.

[0598] 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.

[0599] In this invention, the server includes data processing means for collecting preference information and health information and generating personalized food choices based on this information; emotion recognition means for interacting with the user, assisting with orders using natural language, and analyzing the user's emotional state; route optimization means for optimizing delivery routes and shortening delivery times; and suggestion adjustment means for adjusting food choices according to the emotional state. This makes it possible to adapt to the user's emotional state and achieve personalized food selection and rapid delivery.

[0600] "Preference information" refers to data that shows users' preferences and selection tendencies regarding food.

[0601] "Health information" refers to data about the user's health status and nutrition.

[0602] "Data processing means" refers to a function that performs processing to generate food options based on the collected information.

[0603] "Natural language" refers to the linguistic forms that humans use on a daily basis, and the forms that computers use for analysis and generation.

[0604] An "interactive interface means" is a function that allows users and systems to exchange information with each other via natural language.

[0605] "Emotion recognition means" refers to a function that analyzes the user's emotional state from voice and text data.

[0606] A "route optimization method" is a function that calculates the optimal route to maximize time efficiency in the delivery process.

[0607] A "suggestion adjustment mechanism" is a function that adjusts the food options suggested based on the user's emotional state.

[0608] To realize this invention, the system implements a program that includes the following elements: The server collects preference information and health information and processes it to generate personalized food choices based on this information. As a data processing means, for example, a machine learning framework such as TensorFlow is used to perform optimal food suggestions according to the user's preferences and health condition.

[0609] Interaction with the user takes place through an interactive interface built into the device. This utilizes mobile application development frameworks such as Flutter and React Native, and employs Dialogflow or Azure Cognitive Services for natural language processing. This allows for emotion recognition through natural conversations with the user, and further enables the adjustment of responses and suggestions based on the user's emotional state using emotion recognition tools.

[0610] Route optimization in the delivery process is performed by the server using the Google Maps API. This reflects real-time traffic conditions and selects the best route based on emotional state and whether the customer is in a hurry.

[0611] For example, if a user says to their device, "Today was a really busy day," the device analyzes their stress level using emotion recognition technology. Based on this, it suggests a food menu that can quickly restore energy and delivers it via the shortest route.

[0612] An example of a prompt for a generative AI model is: "If the user is tired, generate a menu suggestion for relaxation. Also, generate emotionally responsive dialogue and suggest the fastest delivery route."

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The user inputs information into the terminal using natural language. This input data, including the user's voice and text information, is collected by an interactive interface. Specifically, the terminal receives data from the microphone or keyboard.

[0616] Step 2:

[0617] The device sends collected voice or text data to the server, where it is analyzed using emotion recognition technology. The input information is processed using Dialogflow or Azure Cognitive Services to analyze the user's emotional state. In this process, the server processes the data and extracts emotions (e.g., stress, joy, depression) from the text and voice data.

[0618] Step 3:

[0619] The server uses the analyzed emotional state and data processing tools to generate personalized food choices. TensorFlow is used to suggest menus based on the user's preferences, health information, and emotional state. In this step, emotional and health information is input, and personalized food choices are output.

[0620] Step 4:

[0621] The terminal presents the user with a list of generated food options. The user selects their desired items from these options and confirms their order. Throughout this process, the interactive interface visualizes and presents the options to the user, and performs interactions to accept their selection.

[0622] Step 5:

[0623] The server uses route optimization techniques to create a delivery plan based on confirmed order information. It uses the Google Maps API to calculate the optimal delivery route, taking real-time traffic information into account. The inputs in this step are order information and the user's emotional state, and the output is the optimized delivery route.

[0624] Step 6:

[0625] The terminal notifies the user of the estimated delivery time upon order confirmation. Specifically, the terminal displays the generated delivery information on the screen and provides real-time feedback to the user on the status.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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".

[0643] This invention is a system that provides highly accurate food menu suggestions based on user preference and health information, offers a natural ordering experience through an interactive interface, and achieves efficient delivery through route optimization means.

[0644] Management of preference and health information

[0645] The server collects preference and health information when a user first uses the system and stores it in a database. Users can register their preferences, allergies, and nutritional goals through their device. The system also integrates with wearable electronic devices, periodically sending data to the server to track the user's daily exercise and health status. This data is used to support the user's health maintenance.

[0646] Proposal for personalized menus

[0647] The server generates personalized menus based on each user's stored information. Specifically, it proposes menus designed to optimize the nutritional balance for the day, taking into account preferences and health goals. In addition, past order history is also included in the calculations, enabling even more personalized suggestions.

[0648] Order support through an interactive interface

[0649] The terminal enables natural interaction with the user. Specifically, when the user makes comments or asks questions about the suggested menu, it immediately provides corresponding answers to help them make the best choice. Once the user completes their selection, the order details are sent to the server. Furthermore, for repeat orders, users can easily do so with a single click from their past order history.

[0650] Optimization and efficiency of delivery routes

[0651] Once an order is confirmed, the server calculates the optimal delivery route based on that information and issues instructions to the delivery partner. It analyzes traffic information and order congestion in real time to achieve the shortest possible delivery time. This allows users to receive their goods quickly.

[0652] Specific example

[0653] For example, if a user prefers vegan food, the server searches for new vegan menu items that suit their preference and makes suggestions based on the user's nutritional goals. Once the order is complete, the server selects the nearest delivery partner from a vegan-friendly restaurant and provides the shortest route. This entire process enables the delivery of healthy and efficient meals.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The user logs into the system via their device, enters their preferences, allergy information, and nutritional goals, and completes the initial setup. The device then sends the entered information to the server.

[0657] Step 2:

[0658] The server stores the received user information in a database and generates profiles of preference patterns and health goals. If necessary, it starts collecting data from wearable electronic devices.

[0659] Step 3:

[0660] When a user attempts to order food, a request is made from their device and sent to the server. The server utilizes the user profile to generate personalized suggestions from the available menu.

[0661] Step 4:

[0662] The server generates a personalized menu and sends it to the terminal, which then displays it to the user. The user reviews the suggested menu and asks questions or confirms details through an interactive interface.

[0663] Step 5:

[0664] Once the user confirms their order, the terminal sends the details to the server. Based on the order information, the server begins calculations to determine the optimal delivery route and assign a delivery person.

[0665] Step 6:

[0666] The server communicates optimized routes and instructions to delivery partners and initiates the delivery process. Users receive immediate notification of the estimated delivery time.

[0667] Step 7:

[0668] A delivery partner picks up the food and heads to the destination along the designated route. Users can check the current delivery status in real time via their terminal.

[0669] Step 8:

[0670] Users receive their products and provide feedback on quality and service on their devices. The server stores the received feedback in a database and uses it to improve services in the future.

[0671] (Example 1)

[0672] 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".

[0673] The challenge lies in providing appropriate food selection suggestions based on users' individual preferences and health information, along with an efficient ordering process and prompt delivery. In particular, it is necessary to manage information, optimize delivery routes, and eliminate the hassle of reordering, thereby providing users with a consistent, convenient, and effective service.

[0674] 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.

[0675] In this invention, the server includes information processing means for collecting preference information and health information and generating personalized food menus based on this information; interactive interface means for interacting with the user in natural language and assisting with ordering; route optimization means for calculating the optimal delivery route after order confirmation and shortening delivery time; means for making menu suggestions that take past order history into consideration; and means for making food suggestions using a generative model. This enables optimal food suggestions that take into account the user's health condition and an efficient ordering and delivery process.

[0676] "Preference information" refers to information that includes individual users' food preferences, allergies, and special requests regarding meals.

[0677] "Health information" refers to information including the user's current health status, nutritional goals, and any medical restrictions or goals.

[0678] An "information processing means" is a process that has the function of generating personalized food menus based on preference information and health information collected from users.

[0679] An "interactive interface means" is an interface function that interacts with users via natural language and assists them in placing orders.

[0680] A "route optimization method" is a function that calculates the most efficient route for delivery, thereby minimizing delivery time as much as possible.

[0681] A "generative model" is a computational model that uses machine learning or other advanced algorithms to generate new proposals.

[0682] "Order history" refers to a record of orders placed by a user in the past, and this information is retained to help with future suggestions.

[0683] The embodiments for carrying out the present invention are as follows.

[0684] The server is equipped with information processing means to efficiently collect preference and health information and generate personalized food menus. This means stores the user's preference and health information using a database and analyzes this data using a generation AI model to propose the most suitable menu for the user. Specifically, the server takes the user's past order history into consideration and generates menus that align with their preferences and nutritional goals.

[0685] The terminal is equipped with an interactive interface to support natural conversations with users. This interface uses natural language processing technology to respond to user questions and feedback in real time and assist in making appropriate orders. For example, if a user asks, "What are today's recommendations?", the terminal will, based on that inquiry, present today's recommendations from the suggested menu.

[0686] Once an order is confirmed, the server uses route optimization techniques to optimize the delivery route and ensure fast delivery. This involves analyzing traffic information in real time to reduce delivery times. For example, if a user who prefers vegan food orders a vegan-only menu item, the server calculates the shortest delivery route from a vegan-friendly restaurant.

[0687] Furthermore, by linking with wearable electronic devices, the server can track the user's daily exercise levels and calorie consumption, and store this information as health data. This information can be used to help users maintain their health and, for example, reflects in menu suggestions that take into account the user's health goals, such as "I want a high-protein diet."

[0688] A concrete example of a prompt message is when a user inputs "Please suggest a vegan, high-protein breakfast menu" to the AI ​​generation model, allowing the server to suggest a menu suitable for the user. This process improves the user experience and supports healthy and efficient food delivery.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1: Gathering information on preferences and health.

[0691] Users input their preferences and health information through a terminal. Specific information includes food preferences, allergy information, and nutritional goals. The terminal sends this input information to a server, which stores it in a database. The input is information from the user, and the output is storage in the database.

[0692] Step 2: Obtain health data

[0693] The server receives user health data from wearable electronic devices. This health data includes activity levels, calories burned, heart rate, etc. The server analyzes the received data and records it in a database as health information. The input is data from the wearable electronic device, and the output is the analyzed health information.

[0694] Step 3: Generating a personalized menu

[0695] The server generates personalized menus based on accumulated preference information, health information, and past order history. Using a generation AI model, it creates prompt messages and suggests menus suitable for the user. The input consists of various database entries, and the output is a personalized suggested menu.

[0696] Step 4: Order support via interactive interface

[0697] The user makes inquiries and selections regarding the suggested menu items through the terminal. For example, they can ask, "Does this dish contain nuts?" The terminal analyzes the input and provides an appropriate answer. The input is the user's question, and the output is the answer information.

[0698] Step 5: Order Confirmation and Optimization of Delivery Routes

[0699] Once an order is confirmed, the server calculates the optimal delivery route. During delivery, it analyzes traffic information in real time, selects the fastest route, and instructs the delivery partner accordingly. The input is the confirmed order information, and the output is the optimized delivery route.

[0700] Step 6: Update Order History

[0701] The server updates the user's order history in the database after delivery is complete. This historical data is used to make future suggestions. The input is the order data associated with the completed delivery, and the output is the updated order history.

[0702] (Application Example 1)

[0703] 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".

[0704] There is a need to more effectively suggest meals tailored to users' preferences and health conditions, and a challenge to reduce the effort involved in selecting ingredients and ordering, as well as to expedite delivery. Furthermore, given the demand for health management and efficient time management in modern society, a system that addresses these issues is necessary.

[0705] 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.

[0706] This invention includes a server comprising: information processing means for collecting information on preferences and health and generating personalized meal plans based on this information; interactive interface means for engaging in conversational dialogue with the user and assisting with selections using natural language; route optimization means for optimizing paths and shortening travel time by means of transportation; cooperation means for understanding the user's exercise and nutritional intake in conjunction with a smart device and managing health information; and operation omission means for enabling food selection to be repeated with a single instruction. This enables the suggestion of healthy meals optimized for the user and rapid and efficient delivery.

[0707] "Information regarding preferences" refers to data that shows the types of ingredients and dishes that each user personally likes, as well as their taste preferences.

[0708] "Health-related information" refers to data related to each user's health status, and specifically includes allergy information, nutritional indicators, and health goals.

[0709] "Information processing means" refers to systems or devices that have the function of generating personalized meal plans based on collected information on preferences and health.

[0710] "Interactive interface means" refers to technologies and methods for supporting ordering and selection through natural conversation with the user.

[0711] A "route optimization method" is a technology that has the ability to calculate and propose the optimal route in order to reduce travel time for delivery.

[0712] "Integration means" refers to a mechanism that communicates with smart devices to manage and incorporate users' health information, such as exercise levels and nutritional intake, into the system.

[0713] A "simplification of operation" is a function that allows users to repeat food selection and ordering with a single instruction, thereby reducing the effort required of the user.

[0714] In this invention, the server utilizes a series of information processing means to provide users with food menus optimized for them, thereby realizing an efficient delivery service. Specifically, the server collects preference and health information and generates personalized meal plans based on this information. The program uses a MySQL database as its main backend technology for data storage and management. Furthermore, it collaborates with wearable devices to collect information, using APIs such as Fitbit to acquire data on exercise levels and health status, and reflects this data in health management directed to the server.

[0715] The terminal uses a speech recognition system such as Dialogflow to provide an interactive interface through natural conversation with the user. This allows users to ask questions about food and nutrition in natural language and receive appropriate answers and menu suggestions. Users can view the menu on the system and confirm their order with a single click. For repeat orders, the process is streamlined based on past selections using a simplified procedure.

[0716] For delivery, the server utilizes map information such as the Google Maps API and employs route optimization techniques to analyze real-time traffic information and select the route that minimizes delivery time by mode of transport. This ensures that products are delivered to the user quickly.

[0717] A concrete example of a prompt would be, "Based on the user's health data, suggest a suitable vegan menu for Monday's lunch." Through such prompts, the system can respond and provide the user with optimal food suggestions.

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] The server receives preference and health information from the user as input. This information is registered by the user via a terminal and stored in a database. Based on the input information, the server updates the database and optimizes it according to the user's health status and nutritional goals. This forms the basis for subsequent meal plan suggestions.

[0721] Step 2:

[0722] The server periodically collects health-related data from smart devices, including exercise data from wearable devices such as Fitbit. The server takes health information as input, analyzes the data, and monitors the user's health status by comparing it to past data. As a result, it can provide nutritional suggestions tailored to the user's health fluctuations.

[0723] Step 3:

[0724] The server generates personalized meal plans based on collected preference, health, and exercise data. Using a generation AI model, it leverages prompts to derive optimal menus, for example, following instructions such as "Please suggest vegan menus." The generated plan is sent to the user's terminal in a format that the user can review.

[0725] Step 4:

[0726] The terminal uses an interactive interface to receive additional questions and requests from the user. It receives user questions in natural language as input, analyzes them using Dialogflow, and generates appropriate answers. This process resolves any uncertainties the user may have regarding ingredients.

[0727] Step 5:

[0728] The user reviews the menu suggested by the terminal and decides on their order. The user's selection is collected as input, received by the server, and the order information is processed. This prepares the server to select the most suitable delivery partner.

[0729] Step 6:

[0730] The server uses the Google Maps API to collect traffic information and optimize routes. Together with selected delivery partners, it calculates the shortest and most optimal delivery route. This process outputs the fastest possible delivery time, establishing a plan for quick delivery to the user.

[0731] 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.

[0732] This invention combines a system that provides personalized menus based on user preference and health information with an emotion engine that recognizes the user's emotional state. This system enables real-time order support and delivery optimization through natural language dialogue with the user.

[0733] Emotional engine integration

[0734] The server features an emotion engine that analyzes voice and text data to recognize the user's emotional state in real time. While the user is browsing the menu, the terminal monitors the conversation and detects changes in emotion. This information is sent to the server and used to adjust the suggested menu. For example, if the user is feeling stressed, the system will prioritize suggesting foods with relaxing effects.

[0735] Emotion-based menu suggestions

[0736] Based on feedback from the emotion engine, the server adjusts menu selections to match the user's psychological state. This adjustment allows for a dining experience that takes the user's emotional state into consideration. If the user is excited, the server enhances the positive experience by suggesting new dishes or exotic options.

[0737] Adjusting the response of interactive interfaces

[0738] The interactive interface adapts its responses based on the user's emotions. If the user is feeling down, the conversation shifts to a more friendly and encouraging tone to lift their spirits. In this way, the user gains an experience that goes beyond simply placing an order.

[0739] Delivery optimization and feedback analysis

[0740] Once an order is confirmed, the server selects the optimal delivery route and delivery person, taking into account the user's emotional state. If the user is in a hurry, the system prioritizes the fastest delivery route, ensuring a quick response to user needs. Furthermore, user feedback is analyzed by an emotion engine, and actions that reinforce positive emotions are incorporated into future suggestions.

[0741] Specific example

[0742] When a user tells their device, "Today was a really busy day," the system detects stress through its emotion engine. The server then quickly responds to the user's needs by suggesting an energy-restoring menu for busy days and offering the shortest delivery route option.

[0743] The following describes the processing flow.

[0744] Step 1:

[0745] The user activates the device and logs into the system. The device provides an interface for collecting the user's preferences, health information, and emotional state.

[0746] Step 2:

[0747] The server receives the login information and retrieves the corresponding user profile from the database. This profile includes information related to past order history, preferences, and health status.

[0748] Step 3:

[0749] The device activates its emotion engine and analyzes emotions from the user's voice or text messages. The device then sends this emotion data to the server.

[0750] Step 4:

[0751] The server integrates emotional data with existing user profiles to generate personalized menus. The suggested menus are adjusted to reflect the user's current emotional state.

[0752] Step 5:

[0753] The terminal displays a personalized menu received from the server to the user. It utilizes an interactive interface that allows the user to review the menu and ask questions or place orders using natural language.

[0754] Step 6:

[0755] Once the user confirms their order, the terminal sends that information to the server. The server receives the order and begins the delivery process, taking into account the user's emotional state.

[0756] Step 7:

[0757] The server calculates the optimal delivery route and arranges expedited delivery based on analysis by the emotion engine. If necessary, it sends instructions to delivery partners.

[0758] Step 8:

[0759] Users receive their delivered goods and provide emotionally charged feedback on their devices. The server analyzes this feedback and uses it to improve future services.

[0760] (Example 2)

[0761] 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".

[0762] In today's diverse dietary landscape, there is a demand for personalized food products that take into account the health and emotional states of consumers. However, current systems lack effective means of reflecting emotional states. Furthermore, methods for expediting delivery and utilizing user feedback to improve future recommendations are limited.

[0763] 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.

[0764] In this invention, the server includes information processing means for collecting preference information and health information to generate personalized food menus, emotion recognition means for analyzing voice and text data to recognize emotional states, and information feedback means for analyzing user feedback and reflecting it in future suggestions. This enables the provision of personalized menus that take into account the user's psychological state and the improvement of the cycle based on feedback.

[0765] "Information processing means" refers to a device or program that has the function of collecting user preference information and health information and generating personalized menus based on this information.

[0766] An "interactive interface means" is a component of a system that interacts with users using natural language to assist with ordering.

[0767] "Emotion recognition means" refers to a technology that analyzes the user's voice and text data to recognize their emotional state in real time and reflect it in menu suggestions.

[0768] "Route optimization means" refers to route calculation algorithms that effectively calculate delivery routes and shorten delivery times.

[0769] An "information feedback mechanism" is a function that analyzes user feedback, incorporates it into future proposals, and aims to improve the service.

[0770] A "wearable electronic device" is an electronic device that can be directly attached to the user's body and is used to manage energy consumption and nutrient intake.

[0771] "Order simplification means" refers to a system function that enables reordering with a single operation.

[0772] This invention is a system that provides personalized menus based on the user's preferences and health status, and offers a dining experience that takes the user's emotional state into consideration. Users access the system using a terminal and engage in dialogue about their meals using natural language. The server is equipped with an emotion recognition function that analyzes voice and text data, and determines the user's emotional state in real time.

[0773] Specifically, the server collects user input data and uses natural language processing technology to detect changes in emotion. The software used here includes speech data processing technology for speech recognition and a natural language processing engine. If the user is feeling down, the emotion recognition system suggests a corresponding menu. For example, relaxing beverages or warming foods might be offered as menu items.

[0774] Furthermore, the interactive interface adjusts its responses according to the user's emotional state. The terminal provides polite and warm dialogue tailored to the user's condition, making the ordering process smooth. For example, if the user inputs "I'm tired today," the server will suggest foods suitable for nutritional replenishment.

[0775] Furthermore, the server is equipped with algorithms to optimize delivery routes, resulting in the most efficient delivery system. This makes it possible to significantly reduce delivery times.

[0776] As a concrete example, the following prompt statement can be used:

[0777] "I'm so busy today, I want a relaxing drink."

[0778] "I want to try some new dishes. Do you have any recommendations?"

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] The user accesses the system through their terminal and performs initial input.

[0782] In terms of specific operations, the terminal receives text or voice input from the user and converts it into digital data. This input includes information about preferences and the user's mood for the day. Examples of input data include natural language phrases such as "How are you feeling today?" or "I'm tired." As output, this data is sent to the server.

[0783] Step 2:

[0784] The server analyzes the received data using emotion recognition technology to determine the user's emotional state.

[0785] Specifically, the server uses a natural language processing engine to convert speech data into text data. Furthermore, it analyzes the text data to determine the emotional state. For example, if the user inputs "tired," the emotional data "fatigue" will be output.

[0786] Step 3:

[0787] The server generates personalized menus based on the emotional state.

[0788] In terms of specific operations, the server uses information processing tools to analyze the user's health and preference information in combination with emotional data. An algorithm is then applied to create the optimal menu, and specific recommended menu items such as "protein-rich salad" or "herbal tea" are output.

[0789] Step 4:

[0790] The server sends the generated menu information to the terminal and presents it to the user through an interactive interface.

[0791] In terms of specific actions, the device displays suggestions to the user in a natural and friendly tone. For example, a message like, "If you're feeling tired today, why not recharge your energy with this recommended salad?" might be displayed.

[0792] Step 5:

[0793] The user selects a menu item from the suggestions and confirms their order.

[0794] In terms of specific operations, the terminal receives the user's selection and sends that information to the server. The input is a specific menu item being selected, and the output is the order information being processed on the server.

[0795] Step 6:

[0796] After an order is confirmed, the server optimizes the delivery route and arranges for delivery.

[0797] Specifically, the server uses route optimization techniques to calculate the most appropriate delivery route. The output is a delivery plan showing the shortest distance and fastest time, which is then shared with the delivery team.

[0798] (Application Example 2)

[0799] 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".

[0800] In today's world, a challenge exists in that the food selection and ordering processes do not adequately consider the emotional and health states of individual users, resulting in an inability to provide the optimal user experience. Furthermore, the optimization of the delivery process is insufficient, leading to problems with fast and effective delivery. To address these challenges, a system is needed that can recognize user emotions in real time and provide suggestions and support based on those emotions.

[0801] 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.

[0802] In this invention, the server includes data processing means for collecting preference information and health information and generating personalized food choices based on this information; emotion recognition means for interacting with the user, assisting with orders using natural language, and analyzing the user's emotional state; route optimization means for optimizing delivery routes and shortening delivery times; and suggestion adjustment means for adjusting food choices according to the emotional state. This makes it possible to adapt to the user's emotional state and achieve personalized food selection and rapid delivery.

[0803] "Preference information" refers to data that shows users' preferences and selection tendencies regarding food.

[0804] "Health information" refers to data about the user's health status and nutrition.

[0805] "Data processing means" refers to a function that performs processing to generate food options based on the collected information.

[0806] "Natural language" refers to the linguistic forms that humans use on a daily basis, and the forms that computers use for analysis and generation.

[0807] An "interactive interface means" is a function that allows users and systems to exchange information with each other via natural language.

[0808] "Emotion recognition means" refers to a function that analyzes the user's emotional state from voice and text data.

[0809] A "route optimization method" is a function that calculates the optimal route to maximize time efficiency in the delivery process.

[0810] A "suggestion adjustment mechanism" is a function that adjusts the food options suggested based on the user's emotional state.

[0811] To realize this invention, the system implements a program that includes the following elements: The server collects preference information and health information and processes it to generate personalized food choices based on this information. As a data processing means, for example, a machine learning framework such as TensorFlow is used to perform optimal food suggestions according to the user's preferences and health condition.

[0812] Interaction with the user takes place through an interactive interface built into the device. This utilizes mobile application development frameworks such as Flutter and React Native, and employs Dialogflow or Azure Cognitive Services for natural language processing. This allows for emotion recognition through natural conversations with the user, and further enables the adjustment of responses and suggestions based on the user's emotional state using emotion recognition tools.

[0813] Route optimization in the delivery process is performed by the server using the Google Maps API. This reflects real-time traffic conditions and selects the best route based on emotional state and whether the customer is in a hurry.

[0814] For example, if a user says to their device, "Today was a really busy day," the device analyzes their stress level using emotion recognition technology. Based on this, it suggests a food menu that can quickly restore energy and delivers it via the shortest route.

[0815] An example of a prompt for a generative AI model is: "If the user is tired, generate a menu suggestion for relaxation. Also, generate emotionally responsive dialogue and suggest the fastest delivery route."

[0816] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0817] Step 1:

[0818] The user inputs information into the terminal using natural language. This input data, including the user's voice and text information, is collected by an interactive interface. Specifically, the terminal receives data from the microphone or keyboard.

[0819] Step 2:

[0820] The device sends collected voice or text data to the server, where it is analyzed using emotion recognition technology. The input information is processed using Dialogflow or Azure Cognitive Services to analyze the user's emotional state. In this process, the server processes the data and extracts emotions (e.g., stress, joy, depression) from the text and voice data.

[0821] Step 3:

[0822] The server uses the analyzed emotional state and data processing tools to generate personalized food choices. TensorFlow is used to suggest menus based on the user's preferences, health information, and emotional state. In this step, emotional and health information is input, and personalized food choices are output.

[0823] Step 4:

[0824] The terminal presents the user with a list of generated food options. The user selects their desired items from these options and confirms their order. Throughout this process, the interactive interface visualizes and presents the options to the user, and performs interactions to accept their selection.

[0825] Step 5:

[0826] The server uses route optimization techniques to create a delivery plan based on confirmed order information. It uses the Google Maps API to calculate the optimal delivery route, taking real-time traffic information into account. The inputs in this step are order information and the user's emotional state, and the output is the optimized delivery route.

[0827] Step 6:

[0828] The terminal notifies the user of the estimated delivery time upon order confirmation. Specifically, the terminal displays the generated delivery information on the screen and provides real-time feedback to the user on the status.

[0829] 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.

[0830] 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.

[0831] 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 robot 414.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0837] 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."

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] The following is further disclosed regarding the embodiments described above.

[0851] (Claim 1)

[0852] Information processing means for collecting preference information and health information, and generating personalized food menus based on this information,

[0853] An interactive interface means that interacts with users and assists them with ordering using natural language,

[0854] Route optimization means for optimizing delivery routes and shortening delivery times,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, which works in conjunction with a wearable electronic device to manage the user's calorie consumption and nutrient intake.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising an order simplification means that enables reordering in a single operation.

[0860] "Example 1"

[0861] (Claim 1)

[0862] Information processing means for collecting preference information and health information, and generating personalized food menus based on this information,

[0863] An interactive interface means that communicates with users in natural language and assists them with ordering,

[0864] A route optimization method that calculates the optimal delivery route after order confirmation and shortens delivery time,

[0865] A method for suggesting menus that take past order history into consideration,

[0866] A method for proposing food products using generative models,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which receives data from a wearable electronic device and analyzes the user's health status.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising an order simplification means that enables reordering in a single operation.

[0872] "Application Example 1"

[0873] (Claim 1)

[0874] Information processing means that collects information on preferences and health, and generates an individualized meal plan based on this information,

[0875] An interactive interface means that engages with the user in a conversational format and supports their choices using natural language,

[0876] Route optimization methods to optimize paths and reduce travel time by means of transportation,

[0877] A means of linking with smart devices to understand the user's exercise and nutritional intake and manage health information,

[0878] A means of simplifying operations that allows food selection to be repeated with a single instruction,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, which optimizes communication with users by utilizing voice dialogue technology.

[0882] (Claim 3)

[0883] The system according to claim 1, which uses an instruction processing mechanism to adjust the delivery pattern in real time based on traffic conditions.

[0884] "Example 2 of combining an emotion engine"

[0885] (Claim 1)

[0886] Information processing means for collecting preference information and health information, and generating personalized food menus based on this information,

[0887] An interactive interface means that interacts with users and assists them with ordering using natural language,

[0888] An emotion recognition method that analyzes voice and text data to recognize the user's emotional state in real time and reflect it in menu suggestions,

[0889] Route optimization means for optimizing delivery routes and shortening delivery times,

[0890] An information feedback system that analyzes user feedback and incorporates emotional feedback into future proposals,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, which works in conjunction with a wearable electronic device to manage the user's energy consumption and nutrient intake.

[0894] (Claim 3)

[0895] The system according to claim 1, further comprising an order simplification means that enables reordering in a single operation.

[0896] "Application example 2 when combining with an emotional engine"

[0897] (Claim 1)

[0898] A data processing means that collects preference information and health information and generates personalized food options based on this information,

[0899] A means of recognizing emotions that interacts with users, assists with ordering using natural language, and analyzes the user's emotional state,

[0900] Route optimization means for optimizing delivery routes and shortening delivery times,

[0901] A suggestion adjustment mechanism that adjusts food choices according to emotional state,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, which works in conjunction with a wearable electronic device to manage the user's energy consumption and nutrients and provides menu suggestions based on emotions.

[0905] (Claim 3)

[0906] The system according to claim 1, further comprising an order simplification means that enables reordering with a single operation and adjusts the dialogue according to the emotional state. [Explanation of Symbols]

[0907] 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. Information processing means that collects information on preferences and health, and generates an individualized meal plan based on this information, An interactive interface means that engages with the user in a conversational format and supports their choices using natural language, Route optimization methods to optimize paths and reduce travel time by means of transportation, A means of linking with smart devices to understand the user's exercise and nutritional intake and manage health information, A means of simplifying operations that allows food selection to be repeated with a single instruction, A system that includes this.

2. The system according to claim 1, which optimizes communication with users by utilizing voice dialogue technology.

3. The system according to claim 1, which uses an instruction processing mechanism to adjust the delivery pattern in real time based on traffic conditions.

Images