system

Abstract

We provide the system. [Solution] Information acquisition means for obtaining user preferences, allergy information, and nutritional goals, A suggestion method that proposes the optimal meal menu based on previously acquired information, An activity data acquisition method that acquires user activity data via a wearable device, A menu adjustment means that adjusts the proposed menu based on the acquired activity data, An order processing means that processes orders based on the menu selected by the user, A delivery optimization method that optimizes delivery routes and timing, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional food delivery systems have the problem that when users select a menu, it is cumbersome to decide from a huge number of options, and also that menu proposals considering individual health conditions and preferences sufficiently cannot be made. In addition, since dynamic acquisition of consumers' health information and proposals based on it are not carried out, it is difficult to promote health and maintain nutritional balance. Furthermore, since the efficiency of delivery is insufficient, the realization of optimal delivery timing has not been achieved. Means for solving such problems have been desired.

Means for Solving the Problems

[0005] The present invention includes an information acquisition means for acquiring user preferences, allergy information, and nutritional goals, a suggestion means for proposing an optimal meal menu based on this information, and an activity data acquisition means for acquiring user activity data via a wearable device. Furthermore, it includes a menu adjustment means for adjusting the suggested menu based on the acquired activity data, an order processing means for processing orders based on the menu selected by the user, and a delivery optimization means for optimizing delivery routes and timing. This configuration makes it possible to propose meal menus suitable for the individual health condition and preferences of users and to achieve efficient delivery.

[0006] "Information acquisition means" refers to functions for collecting data such as user preferences, allergy information, and nutritional goals.

[0007] The "proposal method" refers to a function that constructs and presents a meal menu optimized for the user based on the acquired information.

[0008] "Activity data acquisition means" refers to a function for collecting data on the user's physical activity through a wearable device.

[0009] A "menu adjustment mechanism" is a function that optimizes already suggested menus based on the user's activity data.

[0010] An "order processing method" is a function that confirms and processes an order based on the menu selected by the user.

[0011] "Delivery optimization features" are functions that optimize delivery routes and timing so that orders are delivered in the most efficient way possible. [Brief explanation of the drawing]

[0012] [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. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a communication I / F (Interface) with a reference number is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention comprises a system that acquires user preferences, allergy information, and nutritional goals, and proposes an optimal meal menu based on this information. Furthermore, it has a function to acquire activity data via a wearable device and adjust the proposed menu based on that data.

[0034] Information acquisition and menu suggestion process:

[0035] Users input their preferences, allergy information, and nutritional goals into the system. This includes specific details such as "vegan," "nut allergy," or "prefer high-protein foods."

[0036] Based on the information received, the server selects the most suitable menu from a large number of options and suggests it to the user. The suggestion process also utilizes past selection history and a database of nutritional information for each food item.

[0037] Integration of activity data and optimization of menus:

[0038] The device connects with the user's wearable device to acquire activity data in real time. This data includes information such as calories burned and exercise levels.

[0039] The server analyzes the acquired activity data and provides a menu optimized for the user's daily activity level. For example, on days with high activity levels, it adjusts to increase calorie intake.

[0040] Streamlining ordering and delivery:

[0041] Users can easily select their preferred items from the suggested menu and confirm their order. Repeat orders can also be easily placed within the system.

[0042] After an order is confirmed, the server uses AI technology to optimize the delivery route and timing. This enables fast and efficient delivery.

[0043] For example, if a user enters "I want to eat the same vegan menu as yesterday," the server will suggest the same menu based on the previous day's order history and make minor adjustments based on activity data if necessary. After the user confirms the order, the server calculates the optimal delivery route and sends instructions to the delivery partner.

[0044] This allows for the provision of customized dining experiences tailored to user needs and enables efficient service. This system not only supports a healthy and balanced diet but also reduces time and effort.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users input their preferences, allergy information, and nutritional goals on their devices. This includes information such as their food preferences, food ingredients they want to avoid, and nutrients they want to consume.

[0048] Step 2:

[0049] The terminal sends the data entered by the user to the server. This data is stored in a secure database for use in future menu suggestions.

[0050] Step 3:

[0051] The server uses an AI algorithm to analyze available menus based on the received user information. It compares this information with past order history and nutritional information databases to generate the optimal menu.

[0052] Step 4:

[0053] The server sends the suggested menu to the terminal, making it available for the user to view. This menu includes information on the nutritional balance of the meal and estimated calories.

[0054] Step 5:

[0055] The device works in conjunction with wearable devices to collect user activity data. This data includes calories burned and exercise levels for the day.

[0056] Step 6:

[0057] The server re-evaluates the menu based on activity data and adjusts suggestions as needed. If activity has increased energy consumption, the menu can be modified to take that into account.

[0058] Step 7:

[0059] Users can select from the suggested menu and confirm their order. The system also allows for one-click ordering.

[0060] Step 8:

[0061] Once an order is confirmed, the server uses AI to calculate the optimal delivery route and time. This information is sent to the delivery partner to improve delivery efficiency.

[0062] Step 9:

[0063] The device notifies the user of the delivery status in real time, allowing them to check when their order will arrive.

[0064] This series of processing steps allows users to easily receive meals tailored to their individual needs.

[0065] (Example 1)

[0066] 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."

[0067] In modern society, it is difficult for individual users, who have different food preferences and allergies, to efficiently select, order, and receive nutritionally balanced meals. Furthermore, the lack of automated systems that flexibly adjust meals according to daily activity levels can make it difficult for users to obtain adequate nutrition. This invention aims to solve these problems and provide a system that enables users to receive appropriate meal suggestions and efficient delivery.

[0068] 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.

[0069] In this invention, the server includes information acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; suggestion means using a generation AI model that generates an optimal meal menu based on the previously acquired information; and activity data acquisition means for acquiring the user's exercise-related information via a portable device. This enables meal suggestions tailored to the user's individual needs and menu adjustments according to their activity level.

[0070] "Information acquisition means" refers to functions for acquiring information regarding users' preferences, allergies, and nutritional goals.

[0071] A "generative AI model" is an algorithm or program that generates the optimal meal menu based on information obtained from the user.

[0072] The "suggestion method" refers to a function that uses a generative AI model to suggest the most suitable meal menu to the user.

[0073] "Activity data acquisition means" refers to a function for acquiring information related to a user's exercise via a portable device.

[0074] The "menu adjustment mechanism" is a function that adjusts the proposed menu according to the user's activity level based on the acquired exercise-related information.

[0075] An "order processing mechanism" is a function that executes an order based on the menu selected by the user.

[0076] "Delivery optimization means" refers to a function that optimizes delivery routes and times to carry out deliveries efficiently.

[0077] This invention provides a system that offers personalized meal suggestions to users and enables efficient meal ordering and delivery. This system primarily utilizes three components: a server, a terminal, and a user, along with a generative AI model.

[0078] Users input personal data such as their preferences, allergy information, and nutritional goals using their devices. This information is transmitted to a server via the device. Based on the received information, the server uses a generative AI model to generate appropriate meal menus from its database. This generative AI model is designed to provide suggestions that meet the individual needs of the user through analysis of past data and patterns.

[0079] Furthermore, the terminal connects with the user's portable devices (such as wearable devices) to acquire exercise-related activity data (e.g., steps taken, calories burned) in real time. The server analyzes this activity data and adjusts the menu based on the user's activity level for the day. For example, even if the user prompts, "Considering today's exercise, please suggest a high-protein, low-calorie menu," the generating AI model can make the optimal adjustments.

[0080] Finally, the user selects from the menu suggested via the terminal and confirms their order. The server optimizes the delivery route and timing based on the order information. Real-time traffic information is used for delivery optimization, ensuring fast and efficient delivery. Through this process, the system can support the provision of appropriate and efficient meals to users.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users input their preferences, allergy information, and nutritional goals into the terminal. Specifically, they provide information in the form of prompts such as "vegan," "nut allergy," and "prefer high-protein foods." The entered data is formatted by the terminal and sent to the server.

[0084] Step 2:

[0085] The server searches the database based on the received user information and extracts meal menus that match the user's criteria. This process utilizes a generative AI model, performing data analysis based on the user's past preferences and similar patterns. The output is a list of recommended meal menus.

[0086] Step 3:

[0087] The device connects with the user's portable device to acquire real-time activity data (e.g., calories burned and steps taken). This acquired data is continuously transmitted to the server. This data is used as input, enabling dietary adjustments based on usage patterns.

[0088] Step 4:

[0089] The server receives and analyzes real-time activity data. This analysis readjusts the calories and nutrients in the currently suggested menu based on the user's activity level. AI-powered data processing is performed, and optimized meal suggestions are output.

[0090] Step 5:

[0091] The user selects their desired meal from an optimized menu list displayed on the terminal and confirms their order. The selected information is then sent back to the server via the terminal.

[0092] Step 6:

[0093] The server receives confirmed order information and optimizes delivery routes and timings. Here, a generative AI model algorithm is used to derive an efficient delivery plan that reflects real-time traffic information. As a result, optimized delivery instructions are output and provided to the delivery personnel.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern times, there is a demand for easy access to meals that cater to diverse dietary preferences, allergies, and nutritional goals, while simultaneously making adjustments to meal content based on individual users' physical activity levels remains challenging. Furthermore, efficient delivery is crucial for food delivery services, and optimizing delivery routes in response to real-time circumstances is a key issue. This invention was developed to address these challenges.

[0097] 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.

[0098] In this invention, the server includes information acquisition means for acquiring user preferences, allergy information, and nutritional goals; data analysis means for analyzing physical activity data acquired from a portable device and dynamically adjusting meal menus in real time; and delivery optimization means for optimizing delivery routes and timing. This enables customized meal suggestions tailored to the individual circumstances of each user and efficient delivery.

[0099] "Information acquisition means" refers to a system for collecting data on users' preferences, allergy information, and nutritional goals.

[0100] The "suggestion method" is a function that selects and provides the most suitable meal menu based on user information acquired in advance.

[0101] "Activity data acquisition method" refers to a method for collecting data on a user's physical activity via a portable device.

[0102] A "menu adjustment mechanism" is a system for dynamically adjusting the proposed meal menu based on acquired activity data.

[0103] An "order processing system" is a system for managing and processing orders based on the meal menu selected by the user.

[0104] "Delivery optimization methods" refer to methods for optimally adjusting delivery routes and timings in order to achieve efficient deliveries.

[0105] "Data analysis means" refers to technology that processes physical activity data acquired from portable devices and dynamically adjusts the user's meal menu in real time.

[0106] A "portable device" is a terminal device that a user can wear and that is capable of measuring and transmitting physical activity data.

[0107] The system that realizes this invention utilizes terminals such as smartphones and tablets, as well as wearable devices, to provide individually optimized menus in order to satisfy users' meal requests and ensure efficient delivery.

[0108] The server collects user preferences, allergy information, and nutritional goals entered through the terminal, and manages this information using data acquisition means. Based on this, the suggestion means uses a generation AI model to select the optimal foods and provides customized meal suggestions to the user. Activity data is collected from the wearable device via Bluetooth or Wi-Fi and transmitted to the terminal using activity data acquisition means. The server processes this data using data analysis means and makes appropriate adjustments to the suggested menu in real time.

[0109] The order processing system enables an efficient order flow based on the meal menu selected by the user. Furthermore, the delivery optimization system utilizes real-time travel information to optimize delivery routes and times, ensuring fast and effective service.

[0110] As a concrete example, consider a scenario where a user enters into the app the request, "I'm vegan and would like a menu suitable for days with increased activity." In response to this request, the server analyzes the user's past meal history and activity data to suggest an appropriate vegan menu. For example, suppose the prompt is, "35-year-old male, vegan, with a nut allergy. I'd like a higher protein intake. Please suggest a meal suitable for days with more activity than yesterday." The server inputs this prompt into a generating AI model to create a customized suggestion tailored to the user's needs. This makes it possible to provide users with a healthy and balanced eating experience.

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

[0112] Step 1:

[0113] Users enter personal information into a smartphone app. This information includes preferences, allergy information, and nutritional goals. This information is transmitted from the device to a database and stored by the data retrieval system.

[0114] Step 2:

[0115] The server retrieves user information from the database and generates an optimal meal menu using a suggestion tool. Using a generation AI model, it creates prompt messages and selects a customized menu based on preferences and past history. Output prompt messages might include phrases like "vegan, nut allergy, high protein preferred."

[0116] Step 3:

[0117] The device acquires activity data from portable devices via Bluetooth. This data includes heart rate, steps taken, and calories burned. The activity data acquisition system collects this data and transmits it to the server in real time.

[0118] Step 4:

[0119] The server processes the received activity data using data analysis tools to assess the user's current physical activity level. Based on this, it updates prompt messages, adjusts the menu, and calculates how to achieve the recommended nutritional balance.

[0120] Step 5:

[0121] The user reviews and selects from the adjusted menu on their terminal. The selected menu information is registered by the order processing system, and the order is confirmed.

[0122] Step 6:

[0123] The server uses confirmed order information to calculate the optimal delivery route and time using delivery optimization tools. Based on real-time movement information, it optimizes deliveries and sends instructions to delivery personnel.

[0124] Step 7:

[0125] Once delivery is complete, the delivery status and handover information are fed back to the server and notified to the user. This completes the series of services.

[0126] 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.

[0127] This invention is a system that provides meal menus that comprehensively consider the user's preferences, allergy information, nutritional goals, and emotional state. This system utilizes information acquisition means, suggestion means, activity data acquisition means, menu adjustment means, order processing means, delivery optimization means, and an emotion engine.

[0128] When users begin using the system, they input their preferences, allergy information, and nutritional goals through their terminal. This data is immediately sent to and stored on the server. Additionally, an emotion engine operates on the terminal, using the camera and microphone to analyze the user's facial expressions and voice tone. The resulting emotional data is then sent to the server.

[0129] Based on user information, including this emotional data, the server uses AI algorithms to suggest the most suitable meal menu. For example, if a user is feeling stressed, it can suggest a menu that includes ingredients with relaxing properties.

[0130] The suggested menu is displayed to the user on their device, and the user's activity data (e.g., calories burned and frequency of physical activity) is taken into consideration. When the user selects items from the provided menu, that information is used to confirm the order through the order processing system.

[0131] Next, the server uses AI to optimize delivery routes and timings based on the order details and provides this information to delivery partners. By utilizing real-time traffic information, delivery efficiency is improved.

[0132] As a concrete example, when a user returns home tired from work and picks up their device, the emotion engine senses fatigue and stress. Based on this information, the server suggests a menu including vitamin-rich herbal tea and light snacks. Once the user selects this menu, the meal is efficiently delivered.

[0133] Thus, the system provided by the present invention improves the quality of life by analyzing the user's emotions and physical activities and quickly providing an individually optimized dining experience.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] Users input their preferences, allergy information, and nutritional goals through their terminals. This information is sent to the server as basic data for system use.

[0137] Step 2:

[0138] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone in real time using an emotion engine. This analysis identifies the user's emotional state (e.g., stress, happiness) and sends it to the server.

[0139] Step 3:

[0140] The server uses an AI algorithm based on acquired user information and emotional data to generate optimal suggestions from a large menu. For example, for a user who needs to relax, it selects a menu that includes ingredients with calming properties.

[0141] Step 4:

[0142] The server sends the suggested menu list to the terminal and displays it to the user. This includes nutritional information for the food, estimated calories, and emotional benefits.

[0143] Step 5:

[0144] The device works in conjunction with the user's wearable device to acquire activity data (e.g., exercise level, calories burned). This data is sent to a server and influences menu selections.

[0145] Step 6:

[0146] The server takes activity data into account and fine-tunes the suggested menu. This results in a meal plan that is best suited to the user's physical condition.

[0147] Step 7:

[0148] Users can select their preferred items from the suggested menu and confirm their order. This process can be easily done from the terminal.

[0149] Step 8:

[0150] Based on the confirmed order details, the server uses a delivery optimization algorithm to calculate the optimal delivery route and delivery time, and then communicates this to the delivery partner.

[0151] Step 9:

[0152] The terminal notifies the user of the delivery status in real time. This allows the user to know the estimated arrival time of their meal and wait accordingly.

[0153] Through these steps, users can quickly receive meals tailored to their emotional state and health condition, thereby improving their quality of life.

[0154] (Example 2)

[0155] 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".

[0156] While conventional meal delivery systems can suggest menus that take into account user preferences, allergy information, and nutritional goals, they have the challenge of not being able to provide detailed suggestions that reflect the user's emotional state in real time. Furthermore, systems for efficiently delivering the suggested menus are not adequately developed, making it difficult to improve user satisfaction.

[0157] 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.

[0158] In this invention, the server includes data collection means for acquiring preferences, allergy information, and nutritional goals; suggestion means for analyzing the acquired data and emotional state to make meal suggestions; and activity data management means for acquiring physical activity status and adjusting suggestions. This enables the suggestion of an optimal meal menu adapted to the user's emotional state and the setting of an efficient delivery route.

[0159] "Data collection methods" refer to technologies for acquiring user preferences, allergy information, and nutritional goals, and for accumulating the information necessary for the system.

[0160] The "proposal method" is a technology that analyzes acquired data and emotional states, constructs an optimal meal menu based on that analysis, and provides it to the user.

[0161] "Activity data management means" refers to technology that acquires the user's physical activity status and adjusts the suggested menu accordingly.

[0162] An "order processing method" is a technology that processes and confirms an order based on the meal menu selected by the user.

[0163] "Delivery setting means" is a technology that calculates the optimal delivery route and delivery time based on order information and gives instructions to the delivery person.

[0164] This system provides users with optimal meal menus that take into account both emotional needs and nutritional balance. Specifically, the server and the user's terminal work together to acquire and analyze various data, proposing individually optimized meal menus and delivering them efficiently.

[0165] The device functions as the user interface, receiving input from the user. This includes input of preferences, allergy information, and nutritional goals. Furthermore, the device is equipped with a camera and microphone, which the emotion engine uses to analyze the user's facial expressions and tone of voice, acquiring emotional data in real time. This allows the meal suggestions to be adjusted according to the user's emotional state.

[0166] The server stores information transmitted from the terminal and performs a wide variety of data analyses. The server is equipped with a generative AI model that generates the optimal meal menu based on the collected data. The AI ​​model comprehensively considers the user's emotional state, activity level, and individual dietary requirements.

[0167] As a concrete example, when a user returns home feeling stressed and operates their device, the emotion engine detects their stress level, and the server responds by suggesting a menu using ingredients effective for relaxation. This suggested menu includes vitamin-rich herbal tea and easily digestible light meals.

[0168] Furthermore, after an order is confirmed, the server uses AI to calculate the optimal delivery route and time, and instructs the delivery person on a specific delivery schedule. This ensures that meals are delivered to the user quickly and efficiently.

[0169] As an example of a prompt, inputting information that includes the user's emotional state and activity level, such as "Suggest a suitable meal for when I'm feeling emotionally unsettled. My activity level is moderate," allows for more accurate meal suggestions.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user enters their preferences, allergy information, and nutritional goals into the terminal's interface. This input data is then sent from the terminal to the server. Specifically, the user enters the information according to the prompts on the screen and presses the submit button, at which point the data is transferred to the server.

[0173] Step 2:

[0174] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone, which are then analyzed by an emotion engine. Audio signals and video data are used as input, and these are processed by an emotion analysis algorithm to output data on the user's emotional state. Specifically, facial muscle movements and the pitch and speed of the voice are used as analysis points. The analysis results are sent to a server.

[0175] Step 3:

[0176] The server receives preference information, allergy information, nutritional goals, and emotional data transmitted from the terminal. Based on this, it uses a generative AI model to generate an optimal meal menu. It analyzes patterns in the input data and outputs ingredient combinations tailored to the user. Specifically, the process involves passing the input data to cloud-based computing resources and returning the results within a few seconds.

[0177] Step 4:

[0178] The server sends the generated meal menu to the terminal, which then displays it to the user. During this process, it compares the user's activity data and presents additional information about the menu. The system receives menu information as input and displays different options on the user screen as a result. Specific operations can be performed by touching or swiping the screen to view details.

[0179] Step 5:

[0180] The user selects their desired items from the displayed menu and confirms the order. The terminal sends this selection information to the server, and the order is processed. The process involves pressing the confirmation button after selecting items from the menu, which registers the data on the server.

[0181] Step 6:

[0182] The server calculates the optimal delivery route and timing using delivery optimization techniques based on confirmed order information. It outputs a delivery schedule using real-time travel route information. Specifically, it collects traffic information by referencing GPS data and selects the most efficient route.

[0183] Step 7:

[0184] The server provides the delivery person with the most suitable delivery information, and the meal is delivered to the user. In this final step, a delivery notification is displayed on the device, and the meal is delivered at the specified time. This is displayed as a notification that can be confirmed after delivery is complete.

[0185] (Application Example 2)

[0186] 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".

[0187] Conventional meal suggestion systems are limited to considering user preferences, allergy information, and nutritional goals, but they have the challenge of not being able to provide meal menus that take into account the user's emotional state. Furthermore, these systems still have challenges in comprehensively evaluating user activity information and emotional state to suggest the optimal meal menu in real time and to achieve efficient delivery.

[0188] 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.

[0189] In this invention, the server includes data acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; emotion analysis means for acquiring the user's emotional state and reflecting it in the meal menu; and activity information acquisition means for acquiring activity information via a portable terminal. This makes it possible to propose a personalized meal menu that reflects the user's emotional state and activity information.

[0190] "Data acquisition means" refers to procedures or devices for acquiring user preferences, allergy information, and nutritional goals.

[0191] "Selection method" refers to a procedure or device for suggesting the most suitable meal menu to the user based on previously acquired information.

[0192] "Activity information acquisition means" refers to a procedure or device for acquiring user activity information via a portable terminal.

[0193] "Menu modification means" refers to a procedure or device for adjusting a proposed meal menu based on acquired activity information.

[0194] "Order management means" refers to a procedure or device for processing an order based on the menu selected by the user.

[0195] "Delivery planning means" refers to procedures or devices for optimizing delivery routes and timing.

[0196] "Emotional analysis means" refers to a procedure or device for acquiring the emotional state of a user and reflecting that information in the meal menu.

[0197] The system of this invention consists of a user, a terminal, and a server. The user inputs their preferences, allergy information, and nutritional goals into the terminal, which then transmits this information to the server. The terminal also analyzes the user's facial expressions and voice tone through its built-in camera and microphone, and acquires emotional data via an emotion analysis device. This emotional data is also transmitted to the server.

[0198] Based on the received information, the server integrates the user's preferences, allergy information, nutritional goals, and emotional state through data acquisition means. Next, a generative AI model is used to generate a meal menu optimized for these factors, which is then displayed on the terminal via selection means. AI models often utilize technologies such as Python's TENSORFLOW® or PyTorch.

[0199] The user selects their preferred items from the suggested menu, and the order information is sent to the server. The server confirms the order via an order management system and optimizes the delivery route using a delivery planning system. Map information APIs are used in the delivery planning process to obtain real-time traffic information. A specific example is the use of the Google® Maps API.

[0200] As a concrete example, when a user returns home from work and picks up their device, the device detects the user's level of fatigue using emotion analysis. In response, the server suggests a meal menu with relaxing effects. For instance, a menu featuring herbal tea and ingredients effective for fatigue recovery might be displayed.

[0201] An example of a prompt message is, "Based on the user's emotional state, preferences, and nutritional goals, please suggest a meal menu that promotes relaxation." In this way, the system allows the user to quickly and efficiently receive the most suitable meal according to their current state.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user inputs preferences, allergy information, and nutritional goals into the device. The device collects this data and transmits it to the server via a data acquisition mechanism. Based on the input data, the device performs preprocessing, such as standardizing the data format.

[0205] Step 2:

[0206] The device uses its camera and microphone to capture the user's facial expressions and voice tone. An emotion analysis system analyzes this data to calculate the user's emotional state (e.g., stress, happiness). This emotional data is then sent to a server.

[0207] Step 3:

[0208] The server integrates received preference information, nutritional goals, allergy information, and emotional data. After data integration, a generative AI model is executed to generate a meal menu optimized for the user. The AI ​​model performs data calculations to optimize the combination of ingredients from these inputs.

[0209] Step 4:

[0210] The server displays the suggested meal menu on the terminal via a selection mechanism. The terminal then suggests the menu to the user in natural language and generates prompt messages.

[0211] Step 5:

[0212] The user selects the menu item they deem most suitable. The terminal receives the selected menu item and sends this information to the server via the order management system. During this process, the terminal performs an error check on the selected information.

[0213] Step 6:

[0214] The server uses delivery planning tools to optimize the delivery route for the selected menu items. Real-time traffic information is obtained via a map information API, and delivery times are calculated. Historical data is also used to optimize delivery routes.

[0215] Step 7:

[0216] Optimized delivery information is provided to delivery personnel, ensuring that meals are delivered to users within the specified time. The terminal monitors delivery progress in real time and has a function to notify users of the delivery status.

[0217] 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.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention comprises a system that acquires user preferences, allergy information, and nutritional goals, and proposes an optimal meal menu based on this information. Furthermore, it has a function to acquire activity data via a wearable device and adjust the proposed menu based on that data.

[0234] Information acquisition and menu suggestion process:

[0235] Users input their preferences, allergy information, and nutritional goals into the system. This includes specific details such as "vegan," "nut allergy," or "prefer high-protein foods."

[0236] Based on the information received, the server selects the most suitable menu from a large number of options and suggests it to the user. The suggestion process also utilizes past selection history and a database of nutritional information for each food item.

[0237] Integration of activity data and optimization of menus:

[0238] The device connects with the user's wearable device to acquire activity data in real time. This data includes information such as calories burned and exercise levels.

[0239] The server analyzes the acquired activity data and provides a menu optimized for the user's daily activity level. For example, on days with high activity levels, it adjusts to increase calorie intake.

[0240] Streamlining ordering and delivery:

[0241] Users can easily select their preferred items from the suggested menu and confirm their order. Repeat orders can also be easily placed within the system.

[0242] After an order is confirmed, the server uses AI technology to optimize the delivery route and timing. This enables fast and efficient delivery.

[0243] For example, if a user enters "I want to eat the same vegan menu as yesterday," the server will suggest the same menu based on the previous day's order history and make minor adjustments based on activity data if necessary. After the user confirms the order, the server calculates the optimal delivery route and sends instructions to the delivery partner.

[0244] This allows for the provision of customized dining experiences tailored to user needs and enables efficient service. This system not only supports a healthy and balanced diet but also reduces time and effort.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] Users input their preferences, allergy information, and nutritional goals on their devices. This includes information such as their food preferences, food ingredients they want to avoid, and nutrients they want to consume.

[0248] Step 2:

[0249] The terminal sends the data entered by the user to the server. This data is stored in a secure database for use in future menu suggestions.

[0250] Step 3:

[0251] The server uses an AI algorithm to analyze available menus based on the received user information. It compares this information with past order history and nutritional information databases to generate the optimal menu.

[0252] Step 4:

[0253] The server sends the suggested menu to the terminal, making it available for the user to view. This menu includes information on the nutritional balance of the meal and estimated calories.

[0254] Step 5:

[0255] The device works in conjunction with wearable devices to collect user activity data. This data includes calories burned and exercise levels for the day.

[0256] Step 6:

[0257] The server re-evaluates the menu based on activity data and adjusts suggestions as needed. If activity has increased energy consumption, the menu can be modified to take that into account.

[0258] Step 7:

[0259] Users can select from the suggested menu and confirm their order. The system also allows for one-click ordering.

[0260] Step 8:

[0261] Once an order is confirmed, the server uses AI to calculate the optimal delivery route and time. This information is sent to the delivery partner to improve delivery efficiency.

[0262] Step 9:

[0263] The device notifies the user of the delivery status in real time, allowing them to check when their order will arrive.

[0264] This series of processing steps allows users to easily receive meals tailored to their individual needs.

[0265] (Example 1)

[0266] 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."

[0267] In modern society, it is difficult for individual users, who have different food preferences and allergies, to efficiently select, order, and receive nutritionally balanced meals. Furthermore, the lack of automated systems that flexibly adjust meals according to daily activity levels can make it difficult for users to obtain adequate nutrition. This invention aims to solve these problems and provide a system that enables users to receive appropriate meal suggestions and efficient delivery.

[0268] 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.

[0269] In this invention, the server includes information acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; suggestion means using a generation AI model that generates an optimal meal menu based on the previously acquired information; and activity data acquisition means for acquiring the user's exercise-related information via a portable device. This enables meal suggestions tailored to the user's individual needs and menu adjustments according to their activity level.

[0270] "Information acquisition means" refers to functions for acquiring information regarding users' preferences, allergies, and nutritional goals.

[0271] A "generative AI model" is an algorithm or program that generates the optimal meal menu based on information obtained from the user.

[0272] The "suggestion method" refers to a function that uses a generative AI model to suggest the most suitable meal menu to the user.

[0273] "Activity data acquisition means" refers to a function for acquiring information related to a user's exercise via a portable device.

[0274] The "menu adjustment mechanism" is a function that adjusts the proposed menu according to the user's activity level based on the acquired exercise-related information.

[0275] An "order processing mechanism" is a function that executes an order based on the menu selected by the user.

[0276] "Delivery optimization means" refers to a function that optimizes delivery routes and times to carry out deliveries efficiently.

[0277] This invention provides a system that offers personalized meal suggestions to users and enables efficient meal ordering and delivery. This system primarily utilizes three components: a server, a terminal, and a user, along with a generative AI model.

[0278] The user inputs personal data such as their preferences, allergy information, and nutritional goals using a terminal. This information is sent to the server via the terminal. The server generates an appropriate meal menu from the database using a generative AI model based on the received information. This generative AI model is for making proposals that correspond to the user's individual needs through past data and pattern analysis.

[0279] In addition, the terminal cooperates with the user's portable devices (such as wearable devices) to obtain activity data related to exercise (e.g., number of steps, calories burned) in real time. The server analyzes this activity data and adjusts the menu based on the user's activity level for that day. For example, even when the user inputs a prompt such as "Please propose a high-protein and low-calorie menu considering today's exercise", the generative AI model can make optimal adjustments.

[0280] Finally, the user selects from the proposed menus through the terminal and confirms the order. The server optimizes the delivery route and timing based on the order information. Real-time traffic information is used for delivery optimization to achieve fast and efficient delivery. Through the above process, the system can support the provision of appropriate and efficient meals to users.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The user inputs their preferences, allergy information, and nutritional goals into the terminal. Specifically, the information is provided in the form of prompts such as "vegan", "nut allergy", "desire high-protein foods". The input data is formatted by the terminal and sent to the server.

[0284] Step 2:

[0285] The server searches the database based on the received user information and extracts a meal menu that meets the user's conditions. In this process, a generative AI model is utilized, and data analysis is performed based on the user's past preferences and similar patterns. As output, a list of recommended meal menus is generated.

[0286] Step 3:

[0287] The terminal collaborates with the user's portable device to obtain real-time activity data (e.g., calories consumed and number of steps). The acquired data is continuously transmitted to the server. This data is used as input, enabling meal adjustments according to usage status.

[0288] Step 4:

[0289] The server receives the real-time activity data and performs analysis. In this analysis, based on the user's exercise volume, calorie and nutrient readjustments in the currently proposed menu are carried out. Data processing using AI is performed, and an optimized meal proposal is output.

[0290] Step 5:

[0291] The user selects the desired meal from the optimized menu list displayed on the terminal and confirms the order. The selected information is transmitted to the server via the terminal again.

[0292] Step 6:

[0293] The server receives the confirmed order information and optimizes the delivery route and timing. Here, the algorithm of the generative AI model is used to derive an efficient delivery plan that reflects real-time traffic information. As a result, an optimized delivery instruction is output and provided to the delivery staff.

[0294] (Application Example 1)

[0295] 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."

[0296] In modern times, there is a demand for easy access to meals that cater to diverse dietary preferences, allergies, and nutritional goals, while simultaneously making adjustments to meal content based on individual users' physical activity levels remains challenging. Furthermore, efficient delivery is crucial for food delivery services, and optimizing delivery routes in response to real-time circumstances is a key issue. This invention was developed to address these challenges.

[0297] 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.

[0298] In this invention, the server includes information acquisition means for acquiring user preferences, allergy information, and nutritional goals; data analysis means for analyzing physical activity data acquired from a portable device and dynamically adjusting meal menus in real time; and delivery optimization means for optimizing delivery routes and timing. This enables customized meal suggestions tailored to the individual circumstances of each user and efficient delivery.

[0299] "Information acquisition means" refers to a system for collecting data on users' preferences, allergy information, and nutritional goals.

[0300] The "suggestion method" is a function that selects and provides the most suitable meal menu based on user information acquired in advance.

[0301] "Activity data acquisition method" refers to a method for collecting data on a user's physical activity via a portable device.

[0302] A "menu adjustment mechanism" is a system for dynamically adjusting the proposed meal menu based on acquired activity data.

[0303] The "order processing means" is a system for managing and processing orders based on the meal menu selected by the user.

[0304] The "delivery optimization means" is a method for optimally adjusting the delivery route and timing in order to achieve efficient delivery.

[0305] The "data analysis means" is a technology for processing physical activity data obtained from a portable device and dynamically adjusting the user's meal menu in real time.

[0306] The "portable device" is a terminal device that the user can wear and that is capable of measuring and transmitting physical activity data.

[0307] The system that realizes this invention utilizes terminals such as smartphones and tablets, as well as wearable devices, to provide an individually optimized menu in order to meet the user's meal requirements and ensure efficient delivery.

[0308] The server collects the user's preferences, allergy information, and nutritional goals input through the terminal, and manages these using the information acquisition means. Based on this, the proposal means uses a generated AI model to make an optimal food selection and provides a customized meal proposal to the user. Activity data is collected from the wearable device via Bluetooth or Wi-Fi and transmitted to the terminal using the activity data acquisition means. The server processes this data using the data analysis means and makes appropriate real-time adjustments to the proposed menu.

[0309] The order processing means realizes an efficient order flow based on the meal menu determined by the user. Furthermore, by utilizing the movement information provided in real time by the delivery optimization means and optimizing the delivery route and time, a prompt and effective service is ensured.

[0310] As a concrete example, consider a scenario where a user enters into the app the request, "I'm vegan and would like a menu suitable for days with increased activity." In response to this request, the server analyzes the user's past meal history and activity data to suggest an appropriate vegan menu. For example, suppose the prompt is, "35-year-old male, vegan, with a nut allergy. I'd like a higher protein intake. Please suggest a meal suitable for days with more activity than yesterday." The server inputs this prompt into a generating AI model to create a customized suggestion tailored to the user's needs. This makes it possible to provide users with a healthy and balanced eating experience.

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] Users enter personal information into a smartphone app. This information includes preferences, allergy information, and nutritional goals. This information is transmitted from the device to a database and stored by the data retrieval system.

[0314] Step 2:

[0315] The server retrieves user information from the database and generates an optimal meal menu using a suggestion tool. Using a generation AI model, it creates prompt messages and selects a customized menu based on preferences and past history. Output prompt messages might include phrases like "vegan, nut allergy, high protein preferred."

[0316] Step 3:

[0317] The device acquires activity data from portable devices via Bluetooth. This data includes heart rate, steps taken, and calories burned. The activity data acquisition system collects this data and transmits it to the server in real time.

[0318] Step 4:

[0319] The server processes the received activity data using data analysis tools to assess the user's current physical activity level. Based on this, it updates prompt messages, adjusts the menu, and calculates how to achieve the recommended nutritional balance.

[0320] Step 5:

[0321] The user reviews and selects from the adjusted menu on their terminal. The selected menu information is registered by the order processing system, and the order is confirmed.

[0322] Step 6:

[0323] The server uses confirmed order information to calculate the optimal delivery route and time using delivery optimization tools. Based on real-time movement information, it optimizes deliveries and sends instructions to delivery personnel.

[0324] Step 7:

[0325] Once delivery is complete, the delivery status and handover information are fed back to the server and notified to the user. This completes the series of services.

[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 is a system that provides meal menus that comprehensively consider the user's preferences, allergy information, nutritional goals, and emotional state. This system utilizes information acquisition means, suggestion means, activity data acquisition means, menu adjustment means, order processing means, delivery optimization means, and an emotion engine.

[0328] When users begin using the system, they input their preferences, allergy information, and nutritional goals through their terminal. This data is immediately sent to and stored on the server. Additionally, an emotion engine operates on the terminal, using the camera and microphone to analyze the user's facial expressions and voice tone. The resulting emotional data is then sent to the server.

[0329] Based on user information, including this emotional data, the server uses AI algorithms to suggest the most suitable meal menu. For example, if a user is feeling stressed, it can suggest a menu that includes ingredients with relaxing properties.

[0330] The suggested menu is displayed to the user on their device, and the user's activity data (e.g., calories burned and frequency of physical activity) is taken into consideration. When the user selects items from the provided menu, that information is used to confirm the order through the order processing system.

[0331] Next, the server uses AI to optimize delivery routes and timings based on the order details and provides this information to delivery partners. By utilizing real-time traffic information, delivery efficiency is improved.

[0332] As a concrete example, when a user returns home tired from work and picks up their device, the emotion engine senses fatigue and stress. Based on this information, the server suggests a menu including vitamin-rich herbal tea and light snacks. Once the user selects this menu, the meal is efficiently delivered.

[0333] Thus, the system provided by the present invention improves the quality of life by analyzing the user's emotions and physical activities and quickly providing an individually optimized dining experience.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] Users input their preferences, allergy information, and nutritional goals through their terminals. This information is sent to the server as basic data for system use.

[0337] Step 2:

[0338] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone in real time using an emotion engine. This analysis identifies the user's emotional state (e.g., stress, happiness) and sends it to the server.

[0339] Step 3:

[0340] The server uses an AI algorithm based on acquired user information and emotional data to generate optimal suggestions from a large menu. For example, for a user who needs to relax, it selects a menu that includes ingredients with calming properties.

[0341] Step 4:

[0342] The server sends the suggested menu list to the terminal and displays it to the user. This includes nutritional information for the food, estimated calories, and emotional benefits.

[0343] Step 5:

[0344] The device works in conjunction with the user's wearable device to acquire activity data (e.g., exercise level, calories burned). This data is sent to a server and influences menu selections.

[0345] Step 6:

[0346] The server takes activity data into account and fine-tunes the suggested menu. This results in a meal plan that is best suited to the user's physical condition.

[0347] Step 7:

[0348] Users can select their preferred items from the suggested menu and confirm their order. This process can be easily done from the terminal.

[0349] Step 8:

[0350] Based on the confirmed order details, the server uses a delivery optimization algorithm to calculate the optimal delivery route and delivery time, and then communicates this to the delivery partner.

[0351] Step 9:

[0352] The terminal notifies the user of the delivery status in real time. This allows the user to know the estimated arrival time of their meal and wait accordingly.

[0353] Through these steps, users can quickly receive meals tailored to their emotional state and health condition, thereby improving their quality of life.

[0354] (Example 2)

[0355] 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".

[0356] While conventional meal delivery systems can suggest menus that take into account user preferences, allergy information, and nutritional goals, they have the challenge of not being able to provide detailed suggestions that reflect the user's emotional state in real time. Furthermore, systems for efficiently delivering the suggested menus are not adequately developed, making it difficult to improve user satisfaction.

[0357] 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.

[0358] In this invention, the server includes data collection means for acquiring preferences, allergy information, and nutritional goals; suggestion means for analyzing the acquired data and emotional state to make meal suggestions; and activity data management means for acquiring physical activity status and adjusting suggestions. This enables the suggestion of an optimal meal menu adapted to the user's emotional state and the setting of an efficient delivery route.

[0359] "Data collection methods" refer to technologies for acquiring user preferences, allergy information, and nutritional goals, and for accumulating the information necessary for the system.

[0360] The "proposal method" is a technology that analyzes acquired data and emotional states, constructs an optimal meal menu based on that analysis, and provides it to the user.

[0361] "Activity data management means" refers to technology that acquires the user's physical activity status and adjusts the suggested menu accordingly.

[0362] An "order processing method" is a technology that processes and confirms an order based on the meal menu selected by the user.

[0363] "Delivery setting means" is a technology that calculates the optimal delivery route and delivery time based on order information and gives instructions to the delivery person.

[0364] This system provides users with optimal meal menus that take into account both emotional needs and nutritional balance. Specifically, the server and the user's terminal work together to acquire and analyze various data, proposing individually optimized meal menus and delivering them efficiently.

[0365] The device functions as the user interface, receiving input from the user. This includes input of preferences, allergy information, and nutritional goals. Furthermore, the device is equipped with a camera and microphone, which the emotion engine uses to analyze the user's facial expressions and tone of voice, acquiring emotional data in real time. This allows the meal suggestions to be adjusted according to the user's emotional state.

[0366] The server stores information transmitted from the terminal and performs a wide variety of data analyses. The server is equipped with a generative AI model that generates the optimal meal menu based on the collected data. The AI ​​model comprehensively considers the user's emotional state, activity level, and individual dietary requirements.

[0367] As a concrete example, when a user returns home feeling stressed and operates their device, the emotion engine detects their stress level, and the server responds by suggesting a menu using ingredients effective for relaxation. This suggested menu includes vitamin-rich herbal tea and easily digestible light meals.

[0368] Furthermore, after an order is confirmed, the server uses AI to calculate the optimal delivery route and time, and instructs the delivery person on a specific delivery schedule. This ensures that meals are delivered to the user quickly and efficiently.

[0369] As an example of a prompt, inputting information that includes the user's emotional state and activity level, such as "Suggest a suitable meal for when I'm feeling emotionally unsettled. My activity level is moderate," allows for more accurate meal suggestions.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The user enters their preferences, allergy information, and nutritional goals into the terminal's interface. This input data is then sent from the terminal to the server. Specifically, the user enters the information according to the prompts on the screen and presses the submit button, at which point the data is transferred to the server.

[0373] Step 2:

[0374] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone, which are then analyzed by an emotion engine. Audio signals and video data are used as input, and these are processed by an emotion analysis algorithm to output data on the user's emotional state. Specifically, facial muscle movements and the pitch and speed of the voice are used as analysis points. The analysis results are sent to a server.

[0375] Step 3:

[0376] The server receives preference information, allergy information, nutritional goals, and emotional data transmitted from the terminal. Based on this, it uses a generative AI model to generate an optimal meal menu. It analyzes patterns in the input data and outputs ingredient combinations tailored to the user. Specifically, the process involves passing the input data to cloud-based computing resources and returning the results within a few seconds.

[0377] Step 4:

[0378] The server sends the generated meal menu to the terminal, which then displays it to the user. During this process, it compares the user's activity data and presents additional information about the menu. The system receives menu information as input and displays different options on the user screen as a result. Specific operations can be performed by touching or swiping the screen to view details.

[0379] Step 5:

[0380] The user selects their desired items from the displayed menu and confirms the order. The terminal sends this selection information to the server, and the order is processed. The process involves pressing the confirmation button after selecting items from the menu, which registers the data on the server.

[0381] Step 6:

[0382] The server calculates the optimal delivery route and timing using delivery optimization techniques based on confirmed order information. It outputs a delivery schedule using real-time travel route information. Specifically, it collects traffic information by referencing GPS data and selects the most efficient route.

[0383] Step 7:

[0384] The server provides the delivery person with the most suitable delivery information, and the meal is delivered to the user. In this final step, a delivery notification is displayed on the device, and the meal is delivered at the specified time. This is displayed as a notification that can be confirmed after delivery is complete.

[0385] (Application Example 2)

[0386] 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."

[0387] Conventional meal suggestion systems are limited to considering user preferences, allergy information, and nutritional goals, but they have the challenge of not being able to provide meal menus that take into account the user's emotional state. Furthermore, these systems still have challenges in comprehensively evaluating user activity information and emotional state to suggest the optimal meal menu in real time and to achieve efficient delivery.

[0388] 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.

[0389] In this invention, the server includes data acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; emotion analysis means for acquiring the user's emotional state and reflecting it in the meal menu; and activity information acquisition means for acquiring activity information via a portable terminal. This makes it possible to propose a personalized meal menu that reflects the user's emotional state and activity information.

[0390] "Data acquisition means" refers to procedures or devices for acquiring user preferences, allergy information, and nutritional goals.

[0391] "Selection method" refers to a procedure or device for suggesting the most suitable meal menu to the user based on previously acquired information.

[0392] "Activity information acquisition means" refers to a procedure or device for acquiring user activity information via a portable terminal.

[0393] "Menu modification means" refers to a procedure or device for adjusting a proposed meal menu based on acquired activity information.

[0394] "Order management means" refers to a procedure or device for processing an order based on the menu selected by the user.

[0395] "Delivery planning means" refers to procedures or devices for optimizing delivery routes and timing.

[0396] "Emotional analysis means" refers to a procedure or device for acquiring the emotional state of a user and reflecting that information in the meal menu.

[0397] The system of this invention consists of a user, a terminal, and a server. The user inputs their preferences, allergy information, and nutritional goals into the terminal, which then transmits this information to the server. The terminal also analyzes the user's facial expressions and voice tone through its built-in camera and microphone, and acquires emotional data via an emotion analysis device. This emotional data is also transmitted to the server.

[0398] Based on the received information, the server integrates the user's preferences, allergy information, nutritional goals, and emotional state through data acquisition methods. Next, a generative AI model is used to generate a meal menu optimized for these factors, which is then displayed on the terminal via selection methods. AI models often utilize technologies such as Python's TensorFlow or PyTorch.

[0399] The user selects their preferred items from the suggested menu, and the order information is sent to the server. The server confirms the order via an order management system and optimizes the delivery route using a delivery planning system. The delivery planning utilizes map information APIs to obtain real-time traffic information. A specific example of this might be the Google Maps API.

[0400] As a concrete example, when a user returns home from work and picks up their device, the device detects the user's level of fatigue using emotion analysis. In response, the server suggests a meal menu with relaxing effects. For instance, a menu featuring herbal tea and ingredients effective for fatigue recovery might be displayed.

[0401] An example of a prompt message is, "Based on the user's emotional state, preferences, and nutritional goals, please suggest a meal menu that promotes relaxation." In this way, the system allows the user to quickly and efficiently receive the most suitable meal according to their current state.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The user inputs preferences, allergy information, and nutritional goals into the device. The device collects this data and transmits it to the server via a data acquisition mechanism. Based on the input data, the device performs preprocessing, such as standardizing the data format.

[0405] Step 2:

[0406] The device uses its camera and microphone to capture the user's facial expressions and voice tone. An emotion analysis system analyzes this data to calculate the user's emotional state (e.g., stress, happiness). This emotional data is then sent to a server.

[0407] Step 3:

[0408] The server integrates received preference information, nutritional goals, allergy information, and emotional data. After data integration, a generative AI model is executed to generate a meal menu optimized for the user. The AI ​​model performs data calculations to optimize the combination of ingredients from these inputs.

[0409] Step 4:

[0410] The server displays the suggested meal menu on the terminal via a selection mechanism. The terminal then suggests the menu to the user in natural language and generates prompt messages.

[0411] Step 5:

[0412] The user selects the menu item they deem most suitable. The terminal receives the selected menu item and sends this information to the server via the order management system. During this process, the terminal performs an error check on the selected information.

[0413] Step 6:

[0414] The server uses delivery planning tools to optimize the delivery route for the selected menu items. Real-time traffic information is obtained via a map information API, and delivery times are calculated. Historical data is also used to optimize delivery routes.

[0415] Step 7:

[0416] Optimized delivery information is provided to delivery personnel, ensuring that meals are delivered to users within the specified time. The terminal monitors delivery progress in real time and has a function to notify users of the delivery status.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention comprises a system that acquires user preferences, allergy information, and nutritional goals, and proposes an optimal meal menu based on this information. Furthermore, it has a function to acquire activity data via a wearable device and adjust the proposed menu based on that data.

[0434] Information acquisition and menu suggestion process:

[0435] Users input their preferences, allergy information, and nutritional goals into the system. This includes specific details such as "vegan," "nut allergy," or "prefer high-protein foods."

[0436] Based on the information received, the server selects the most suitable menu from a large number of options and suggests it to the user. The suggestion process also utilizes past selection history and a database of nutritional information for each food item.

[0437] Integration of activity data and optimization of menus:

[0438] The device connects with the user's wearable device to acquire activity data in real time. This data includes information such as calories burned and exercise levels.

[0439] The server analyzes the acquired activity data and provides a menu optimized for the user's daily activity level. For example, on days with high activity levels, it adjusts to increase calorie intake.

[0440] Streamlining ordering and delivery:

[0441] Users can easily select their preferred items from the suggested menu and confirm their order. Repeat orders can also be easily placed within the system.

[0442] After an order is confirmed, the server uses AI technology to optimize the delivery route and timing. This enables fast and efficient delivery.

[0443] For example, if a user enters "I want to eat the same vegan menu as yesterday," the server will suggest the same menu based on the previous day's order history and make minor adjustments based on activity data if necessary. After the user confirms the order, the server calculates the optimal delivery route and sends instructions to the delivery partner.

[0444] This allows for the provision of customized dining experiences tailored to user needs and enables efficient service. This system not only supports a healthy and balanced diet but also reduces time and effort.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] Users input their preferences, allergy information, and nutritional goals on their devices. This includes information such as their food preferences, food ingredients they want to avoid, and nutrients they want to consume.

[0448] Step 2:

[0449] The terminal sends the data entered by the user to the server. This data is stored in a secure database for use in future menu suggestions.

[0450] Step 3:

[0451] The server uses an AI algorithm to analyze available menus based on the received user information. It compares this information with past order history and nutritional information databases to generate the optimal menu.

[0452] Step 4:

[0453] The server sends the suggested menu to the terminal, making it available for the user to view. This menu includes information on the nutritional balance of the meal and estimated calories.

[0454] Step 5:

[0455] The device works in conjunction with wearable devices to collect user activity data. This data includes calories burned and exercise levels for the day.

[0456] Step 6:

[0457] The server re-evaluates the menu based on activity data and adjusts suggestions as needed. If activity has increased energy consumption, the menu can be modified to take that into account.

[0458] Step 7:

[0459] Users can select from the suggested menu and confirm their order. The system also allows for one-click ordering.

[0460] Step 8:

[0461] Once an order is confirmed, the server uses AI to calculate the optimal delivery route and time. This information is sent to the delivery partner to improve delivery efficiency.

[0462] Step 9:

[0463] The device notifies the user of the delivery status in real time, allowing them to check when their order will arrive.

[0464] This series of processing steps allows users to easily receive meals tailored to their individual needs.

[0465] (Example 1)

[0466] 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."

[0467] In modern society, it is difficult for individual users, who have different food preferences and allergies, to efficiently select, order, and receive nutritionally balanced meals. Furthermore, the lack of automated systems that flexibly adjust meals according to daily activity levels can make it difficult for users to obtain adequate nutrition. This invention aims to solve these problems and provide a system that enables users to receive appropriate meal suggestions and efficient delivery.

[0468] 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.

[0469] In this invention, the server includes information acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; suggestion means using a generation AI model that generates an optimal meal menu based on the previously acquired information; and activity data acquisition means for acquiring the user's exercise-related information via a portable device. This enables meal suggestions tailored to the user's individual needs and menu adjustments according to their activity level.

[0470] "Information acquisition means" refers to functions for acquiring information regarding users' preferences, allergies, and nutritional goals.

[0471] A "generative AI model" is an algorithm or program that generates the optimal meal menu based on information obtained from the user.

[0472] The "suggestion method" refers to a function that uses a generative AI model to suggest the most suitable meal menu to the user.

[0473] "Activity data acquisition means" refers to a function for acquiring information related to a user's exercise via a portable device.

[0474] The "menu adjustment mechanism" is a function that adjusts the proposed menu according to the user's activity level based on the acquired exercise-related information.

[0475] An "order processing mechanism" is a function that executes an order based on the menu selected by the user.

[0476] "Delivery optimization means" refers to a function that optimizes delivery routes and times to carry out deliveries efficiently.

[0477] This invention provides a system that offers personalized meal suggestions to users and enables efficient meal ordering and delivery. This system primarily utilizes three components: a server, a terminal, and a user, along with a generative AI model.

[0478] Users input personal data such as their preferences, allergy information, and nutritional goals using their devices. This information is transmitted to a server via the device. Based on the received information, the server uses a generative AI model to generate appropriate meal menus from its database. This generative AI model is designed to provide suggestions that meet the individual needs of the user through analysis of past data and patterns.

[0479] Furthermore, the terminal connects with the user's portable devices (such as wearable devices) to acquire exercise-related activity data (e.g., steps taken, calories burned) in real time. The server analyzes this activity data and adjusts the menu based on the user's activity level for the day. For example, even if the user prompts, "Considering today's exercise, please suggest a high-protein, low-calorie menu," the generating AI model can make the optimal adjustments.

[0480] Finally, the user selects from the menu suggested via the terminal and confirms their order. The server optimizes the delivery route and timing based on the order information. Real-time traffic information is used for delivery optimization, ensuring fast and efficient delivery. Through this process, the system can support the provision of appropriate and efficient meals to users.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] Users input their preferences, allergy information, and nutritional goals into the terminal. Specifically, they provide information in the form of prompts such as "vegan," "nut allergy," and "prefer high-protein foods." The entered data is formatted by the terminal and sent to the server.

[0484] Step 2:

[0485] The server searches the database based on the received user information and extracts meal menus that match the user's criteria. This process utilizes a generative AI model, performing data analysis based on the user's past preferences and similar patterns. The output is a list of recommended meal menus.

[0486] Step 3:

[0487] The device connects with the user's portable device to acquire real-time activity data (e.g., calories burned and steps taken). This acquired data is continuously transmitted to the server. This data is used as input, enabling dietary adjustments based on usage patterns.

[0488] Step 4:

[0489] The server receives and analyzes real-time activity data. This analysis readjusts the calories and nutrients in the currently suggested menu based on the user's activity level. AI-powered data processing is performed, and optimized meal suggestions are output.

[0490] Step 5:

[0491] The user selects their desired meal from an optimized menu list displayed on the terminal and confirms their order. The selected information is then sent back to the server via the terminal.

[0492] Step 6:

[0493] The server receives confirmed order information and optimizes delivery routes and timings. Here, a generative AI model algorithm is used to derive an efficient delivery plan that reflects real-time traffic information. As a result, optimized delivery instructions are output and provided to the delivery personnel.

[0494] (Application Example 1)

[0495] 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."

[0496] In modern times, there is a demand for easy access to meals that cater to diverse dietary preferences, allergies, and nutritional goals, while simultaneously making adjustments to meal content based on individual users' physical activity levels remains challenging. Furthermore, efficient delivery is crucial for food delivery services, and optimizing delivery routes in response to real-time circumstances is a key issue. This invention was developed to address these challenges.

[0497] 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.

[0498] In this invention, the server includes information acquisition means for acquiring user preferences, allergy information, and nutritional goals; data analysis means for analyzing physical activity data acquired from a portable device and dynamically adjusting meal menus in real time; and delivery optimization means for optimizing delivery routes and timing. This enables customized meal suggestions tailored to the individual circumstances of each user and efficient delivery.

[0499] "Information acquisition means" refers to a system for collecting data on users' preferences, allergy information, and nutritional goals.

[0500] The "suggestion method" is a function that selects and provides the most suitable meal menu based on user information acquired in advance.

[0501] "Activity data acquisition method" refers to a method for collecting data on a user's physical activity via a portable device.

[0502] A "menu adjustment mechanism" is a system for dynamically adjusting the proposed meal menu based on acquired activity data.

[0503] An "order processing system" is a system for managing and processing orders based on the meal menu selected by the user.

[0504] "Delivery optimization methods" refer to methods for optimally adjusting delivery routes and timings in order to achieve efficient deliveries.

[0505] "Data analysis means" refers to technology that processes physical activity data acquired from portable devices and dynamically adjusts the user's meal menu in real time.

[0506] A "portable device" is a terminal device that a user can wear and that is capable of measuring and transmitting physical activity data.

[0507] The system that realizes this invention utilizes terminals such as smartphones and tablets, as well as wearable devices, to provide individually optimized menus in order to satisfy users' meal requests and ensure efficient delivery.

[0508] The server collects user preferences, allergy information, and nutritional goals entered through the terminal, and manages this information using data acquisition means. Based on this, the suggestion means uses a generation AI model to select the optimal foods and provides customized meal suggestions to the user. Activity data is collected from the wearable device via Bluetooth or Wi-Fi and transmitted to the terminal using activity data acquisition means. The server processes this data using data analysis means and makes appropriate adjustments to the suggested menu in real time.

[0509] The order processing system enables an efficient order flow based on the meal menu selected by the user. Furthermore, the delivery optimization system utilizes real-time travel information to optimize delivery routes and times, ensuring fast and effective service.

[0510] As a concrete example, consider a scenario where a user enters into the app the request, "I'm vegan and would like a menu suitable for days with increased activity." In response to this request, the server analyzes the user's past meal history and activity data to suggest an appropriate vegan menu. For example, suppose the prompt is, "35-year-old male, vegan, with a nut allergy. I'd like a higher protein intake. Please suggest a meal suitable for days with more activity than yesterday." The server inputs this prompt into a generating AI model to create a customized suggestion tailored to the user's needs. This makes it possible to provide users with a healthy and balanced eating experience.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] Users enter personal information into a smartphone app. This information includes preferences, allergy information, and nutritional goals. This information is transmitted from the device to a database and stored by the data retrieval system.

[0514] Step 2:

[0515] The server retrieves user information from the database and generates an optimal meal menu using a suggestion tool. Using a generation AI model, it creates prompt messages and selects a customized menu based on preferences and past history. Output prompt messages might include phrases like "vegan, nut allergy, high protein preferred."

[0516] Step 3:

[0517] The device acquires activity data from portable devices via Bluetooth. This data includes heart rate, steps taken, and calories burned. The activity data acquisition system collects this data and transmits it to the server in real time.

[0518] Step 4:

[0519] The server processes the received activity data using data analysis tools to assess the user's current physical activity level. Based on this, it updates prompt messages, adjusts the menu, and calculates how to achieve the recommended nutritional balance.

[0520] Step 5:

[0521] The user reviews and selects from the adjusted menu on their terminal. The selected menu information is registered by the order processing system, and the order is confirmed.

[0522] Step 6:

[0523] The server uses confirmed order information to calculate the optimal delivery route and time using delivery optimization tools. Based on real-time movement information, it optimizes deliveries and sends instructions to delivery personnel.

[0524] Step 7:

[0525] Once delivery is complete, the delivery status and handover information are fed back to the server and notified to the user. This completes the series of services.

[0526] 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.

[0527] This invention is a system that provides meal menus that comprehensively consider the user's preferences, allergy information, nutritional goals, and emotional state. This system utilizes information acquisition means, suggestion means, activity data acquisition means, menu adjustment means, order processing means, delivery optimization means, and an emotion engine.

[0528] When users begin using the system, they input their preferences, allergy information, and nutritional goals through their terminal. This data is immediately sent to and stored on the server. Additionally, an emotion engine operates on the terminal, using the camera and microphone to analyze the user's facial expressions and voice tone. The resulting emotional data is then sent to the server.

[0529] Based on user information, including this emotional data, the server uses AI algorithms to suggest the most suitable meal menu. For example, if a user is feeling stressed, it can suggest a menu that includes ingredients with relaxing properties.

[0530] The suggested menu is displayed to the user on their device, and the user's activity data (e.g., calories burned and frequency of physical activity) is taken into consideration. When the user selects items from the provided menu, that information is used to confirm the order through the order processing system.

[0531] Next, the server uses AI to optimize delivery routes and timings based on the order details and provides this information to delivery partners. By utilizing real-time traffic information, delivery efficiency is improved.

[0532] As a concrete example, when a user returns home tired from work and picks up their device, the emotion engine senses fatigue and stress. Based on this information, the server suggests a menu including vitamin-rich herbal tea and light snacks. Once the user selects this menu, the meal is efficiently delivered.

[0533] Thus, the system provided by the present invention improves the quality of life by analyzing the user's emotions and physical activities and quickly providing an individually optimized dining experience.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] Users input their preferences, allergy information, and nutritional goals through their terminals. This information is sent to the server as basic data for system use.

[0537] Step 2:

[0538] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone in real time using an emotion engine. This analysis identifies the user's emotional state (e.g., stress, happiness) and sends it to the server.

[0539] Step 3:

[0540] The server uses an AI algorithm based on acquired user information and emotional data to generate optimal suggestions from a large menu. For example, for a user who needs to relax, it selects a menu that includes ingredients with calming properties.

[0541] Step 4:

[0542] The server sends the suggested menu list to the terminal and displays it to the user. This includes nutritional information for the food, estimated calories, and emotional benefits.

[0543] Step 5:

[0544] The device works in conjunction with the user's wearable device to acquire activity data (e.g., exercise level, calories burned). This data is sent to a server and influences menu selections.

[0545] Step 6:

[0546] The server takes activity data into account and fine-tunes the suggested menu. This results in a meal plan that is best suited to the user's physical condition.

[0547] Step 7:

[0548] Users can select their preferred items from the suggested menu and confirm their order. This process can be easily done from the terminal.

[0549] Step 8:

[0550] Based on the confirmed order details, the server uses a delivery optimization algorithm to calculate the optimal delivery route and delivery time, and then communicates this to the delivery partner.

[0551] Step 9:

[0552] The terminal notifies the user of the delivery status in real time. This allows the user to know the estimated arrival time of their meal and wait accordingly.

[0553] Through these steps, users can quickly receive meals tailored to their emotional state and health condition, thereby improving their quality of life.

[0554] (Example 2)

[0555] 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."

[0556] While conventional meal delivery systems can suggest menus that take into account user preferences, allergy information, and nutritional goals, they have the challenge of not being able to provide detailed suggestions that reflect the user's emotional state in real time. Furthermore, systems for efficiently delivering the suggested menus are not adequately developed, making it difficult to improve user satisfaction.

[0557] 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.

[0558] In this invention, the server includes data collection means for acquiring preferences, allergy information, and nutritional goals; suggestion means for analyzing the acquired data and emotional state to make meal suggestions; and activity data management means for acquiring physical activity status and adjusting suggestions. This enables the suggestion of an optimal meal menu adapted to the user's emotional state and the setting of an efficient delivery route.

[0559] "Data collection methods" refer to technologies for acquiring user preferences, allergy information, and nutritional goals, and for accumulating the information necessary for the system.

[0560] The "proposal method" is a technology that analyzes acquired data and emotional states, constructs an optimal meal menu based on that analysis, and provides it to the user.

[0561] "Activity data management means" refers to technology that acquires the user's physical activity status and adjusts the suggested menu accordingly.

[0562] An "order processing method" is a technology that processes and confirms an order based on the meal menu selected by the user.

[0563] "Delivery setting means" is a technology that calculates the optimal delivery route and delivery time based on order information and gives instructions to the delivery person.

[0564] This system provides users with optimal meal menus that take into account both emotional needs and nutritional balance. Specifically, the server and the user's terminal work together to acquire and analyze various data, proposing individually optimized meal menus and delivering them efficiently.

[0565] The device functions as the user interface, receiving input from the user. This includes input of preferences, allergy information, and nutritional goals. Furthermore, the device is equipped with a camera and microphone, which the emotion engine uses to analyze the user's facial expressions and tone of voice, acquiring emotional data in real time. This allows the meal suggestions to be adjusted according to the user's emotional state.

[0566] The server stores information transmitted from the terminal and performs a wide variety of data analyses. The server is equipped with a generative AI model that generates the optimal meal menu based on the collected data. The AI ​​model comprehensively considers the user's emotional state, activity level, and individual dietary requirements.

[0567] As a concrete example, when a user returns home feeling stressed and operates their device, the emotion engine detects their stress level, and the server responds by suggesting a menu using ingredients effective for relaxation. This suggested menu includes vitamin-rich herbal tea and easily digestible light meals.

[0568] Furthermore, after an order is confirmed, the server uses AI to calculate the optimal delivery route and time, and instructs the delivery person on a specific delivery schedule. This ensures that meals are delivered to the user quickly and efficiently.

[0569] As an example of a prompt, inputting information that includes the user's emotional state and activity level, such as "Suggest a suitable meal for when I'm feeling emotionally unsettled. My activity level is moderate," allows for more accurate meal suggestions.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The user enters their preferences, allergy information, and nutritional goals into the terminal's interface. This input data is then sent from the terminal to the server. Specifically, the user enters the information according to the prompts on the screen and presses the submit button, at which point the data is transferred to the server.

[0573] Step 2:

[0574] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone, which are then analyzed by an emotion engine. Audio signals and video data are used as input, and these are processed by an emotion analysis algorithm to output data on the user's emotional state. Specifically, facial muscle movements and the pitch and speed of the voice are used as analysis points. The analysis results are sent to a server.

[0575] Step 3:

[0576] The server receives preference information, allergy information, nutritional goals, and emotional data transmitted from the terminal. Based on this, it uses a generative AI model to generate an optimal meal menu. It analyzes patterns in the input data and outputs ingredient combinations tailored to the user. Specifically, the process involves passing the input data to cloud-based computing resources and returning the results within a few seconds.

[0577] Step 4:

[0578] The server sends the generated meal menu to the terminal, which then displays it to the user. During this process, it compares the user's activity data and presents additional information about the menu. The system receives menu information as input and displays different options on the user screen as a result. Specific operations can be performed by touching or swiping the screen to view details.

[0579] Step 5:

[0580] The user selects their desired items from the displayed menu and confirms the order. The terminal sends this selection information to the server, and the order is processed. The process involves pressing the confirmation button after selecting items from the menu, which registers the data on the server.

[0581] Step 6:

[0582] The server calculates the optimal delivery route and timing using delivery optimization techniques based on confirmed order information. It outputs a delivery schedule using real-time travel route information. Specifically, it collects traffic information by referencing GPS data and selects the most efficient route.

[0583] Step 7:

[0584] The server provides the delivery person with the most suitable delivery information, and the meal is delivered to the user. In this final step, a delivery notification is displayed on the device, and the meal is delivered at the specified time. This is displayed as a notification that can be confirmed after delivery is complete.

[0585] (Application Example 2)

[0586] 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."

[0587] Conventional meal suggestion systems are limited to considering user preferences, allergy information, and nutritional goals, but they have the challenge of not being able to provide meal menus that take into account the user's emotional state. Furthermore, these systems still have challenges in comprehensively evaluating user activity information and emotional state to suggest the optimal meal menu in real time and to achieve efficient delivery.

[0588] 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.

[0589] In this invention, the server includes data acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; emotion analysis means for acquiring the user's emotional state and reflecting it in the meal menu; and activity information acquisition means for acquiring activity information via a portable terminal. This makes it possible to propose a personalized meal menu that reflects the user's emotional state and activity information.

[0590] "Data acquisition means" refers to procedures or devices for acquiring user preferences, allergy information, and nutritional goals.

[0591] "Selection method" refers to a procedure or device for suggesting the most suitable meal menu to the user based on previously acquired information.

[0592] "Activity information acquisition means" refers to a procedure or device for acquiring user activity information via a portable terminal.

[0593] "Menu modification means" refers to a procedure or device for adjusting a proposed meal menu based on acquired activity information.

[0594] "Order management means" refers to a procedure or device for processing an order based on the menu selected by the user.

[0595] "Delivery planning means" refers to procedures or devices for optimizing delivery routes and timing.

[0596] "Emotional analysis means" refers to a procedure or device for acquiring the emotional state of a user and reflecting that information in the meal menu.

[0597] The system of this invention consists of a user, a terminal, and a server. The user inputs their preferences, allergy information, and nutritional goals into the terminal, which then transmits this information to the server. The terminal also analyzes the user's facial expressions and voice tone through its built-in camera and microphone, and acquires emotional data via an emotion analysis device. This emotional data is also transmitted to the server.

[0598] Based on the received information, the server integrates the user's preferences, allergy information, nutritional goals, and emotional state through data acquisition methods. Next, a generative AI model is used to generate a meal menu optimized for these factors, which is then displayed on the terminal via selection methods. AI models often utilize technologies such as Python's TensorFlow or PyTorch.

[0599] The user selects their preferred items from the suggested menu, and the order information is sent to the server. The server confirms the order via an order management system and optimizes the delivery route using a delivery planning system. The delivery planning utilizes map information APIs to obtain real-time traffic information. A specific example of this might be the Google Maps API.

[0600] As a concrete example, when a user returns home from work and picks up their device, the device detects the user's level of fatigue using emotion analysis. In response, the server suggests a meal menu with relaxing effects. For instance, a menu featuring herbal tea and ingredients effective for fatigue recovery might be displayed.

[0601] An example of a prompt message is, "Based on the user's emotional state, preferences, and nutritional goals, please suggest a meal menu that promotes relaxation." In this way, the system allows the user to quickly and efficiently receive the most suitable meal according to their current state.

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The user inputs preferences, allergy information, and nutritional goals into the device. The device collects this data and transmits it to the server via a data acquisition mechanism. Based on the input data, the device performs preprocessing, such as standardizing the data format.

[0605] Step 2:

[0606] The device uses its camera and microphone to capture the user's facial expressions and voice tone. An emotion analysis system analyzes this data to calculate the user's emotional state (e.g., stress, happiness). This emotional data is then sent to a server.

[0607] Step 3:

[0608] The server integrates received preference information, nutritional goals, allergy information, and emotional data. After data integration, a generative AI model is executed to generate a meal menu optimized for the user. The AI ​​model performs data calculations to optimize the combination of ingredients from these inputs.

[0609] Step 4:

[0610] The server displays the suggested meal menu on the terminal via a selection mechanism. The terminal then suggests the menu to the user in natural language and generates prompt messages.

[0611] Step 5:

[0612] The user selects the menu item they deem most suitable. The terminal receives the selected menu item and sends this information to the server via the order management system. During this process, the terminal performs an error check on the selected information.

[0613] Step 6:

[0614] The server uses delivery planning tools to optimize the delivery route for the selected menu items. Real-time traffic information is obtained via a map information API, and delivery times are calculated. Historical data is also used to optimize delivery routes.

[0615] Step 7:

[0616] Optimized delivery information is provided to delivery personnel, ensuring that meals are delivered to users within the specified time. The terminal monitors delivery progress in real time and has a function to notify users of the delivery status.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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".

[0634] This invention comprises a system that acquires user preferences, allergy information, and nutritional goals, and proposes an optimal meal menu based on this information. Furthermore, it has a function to acquire activity data via a wearable device and adjust the proposed menu based on that data.

[0635] Information acquisition and menu suggestion process:

[0636] Users input their preferences, allergy information, and nutritional goals into the system. This includes specific details such as "vegan," "nut allergy," or "prefer high-protein foods."

[0637] Based on the information received, the server selects the most suitable menu from a large number of options and suggests it to the user. The suggestion process also utilizes past selection history and a database of nutritional information for each food item.

[0638] Integration of activity data and optimization of menus:

[0639] The device connects with the user's wearable device to acquire activity data in real time. This data includes information such as calories burned and exercise levels.

[0640] The server analyzes the acquired activity data and provides a menu optimized for the user's daily activity level. For example, on days with high activity levels, it adjusts to increase calorie intake.

[0641] Streamlining ordering and delivery:

[0642] Users can easily select their preferred items from the suggested menu and confirm their order. Repeat orders can also be easily placed within the system.

[0643] After an order is confirmed, the server uses AI technology to optimize the delivery route and timing. This enables fast and efficient delivery.

[0644] For example, if a user enters "I want to eat the same vegan menu as yesterday," the server will suggest the same menu based on the previous day's order history and make minor adjustments based on activity data if necessary. After the user confirms the order, the server calculates the optimal delivery route and sends instructions to the delivery partner.

[0645] This allows for the provision of customized dining experiences tailored to user needs and enables efficient service. This system not only supports a healthy and balanced diet but also reduces time and effort.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] Users input their preferences, allergy information, and nutritional goals on their devices. This includes information such as their food preferences, food ingredients they want to avoid, and nutrients they want to consume.

[0649] Step 2:

[0650] The terminal sends the data entered by the user to the server. This data is stored in a secure database for use in future menu suggestions.

[0651] Step 3:

[0652] The server uses an AI algorithm to analyze available menus based on the received user information. It compares this information with past order history and nutritional information databases to generate the optimal menu.

[0653] Step 4:

[0654] The server sends the suggested menu to the terminal, making it available for the user to view. This menu includes information on the nutritional balance of the meal and estimated calories.

[0655] Step 5:

[0656] The device works in conjunction with wearable devices to collect user activity data. This data includes calories burned and exercise levels for the day.

[0657] Step 6:

[0658] The server re-evaluates the menu based on activity data and adjusts suggestions as needed. If activity has increased energy consumption, the menu can be modified to take that into account.

[0659] Step 7:

[0660] Users can select from the suggested menu and confirm their order. The system also allows for one-click ordering.

[0661] Step 8:

[0662] Once an order is confirmed, the server uses AI to calculate the optimal delivery route and time. This information is sent to the delivery partner to improve delivery efficiency.

[0663] Step 9:

[0664] The device notifies the user of the delivery status in real time, allowing them to check when their order will arrive.

[0665] This series of processing steps allows users to easily receive meals tailored to their individual needs.

[0666] (Example 1)

[0667] 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".

[0668] In modern society, it is difficult for individual users, who have different food preferences and allergies, to efficiently select, order, and receive nutritionally balanced meals. Furthermore, the lack of automated systems that flexibly adjust meals according to daily activity levels can make it difficult for users to obtain adequate nutrition. This invention aims to solve these problems and provide a system that enables users to receive appropriate meal suggestions and efficient delivery.

[0669] 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.

[0670] In this invention, the server includes information acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; suggestion means using a generation AI model that generates an optimal meal menu based on the previously acquired information; and activity data acquisition means for acquiring the user's exercise-related information via a portable device. This enables meal suggestions tailored to the user's individual needs and menu adjustments according to their activity level.

[0671] "Information acquisition means" refers to functions for acquiring information regarding users' preferences, allergies, and nutritional goals.

[0672] A "generative AI model" is an algorithm or program that generates the optimal meal menu based on information obtained from the user.

[0673] The "suggestion method" refers to a function that uses a generative AI model to suggest the most suitable meal menu to the user.

[0674] "Activity data acquisition means" refers to a function for acquiring information related to a user's exercise via a portable device.

[0675] The "menu adjustment mechanism" is a function that adjusts the proposed menu according to the user's activity level based on the acquired exercise-related information.

[0676] An "order processing mechanism" is a function that executes an order based on the menu selected by the user.

[0677] "Delivery optimization means" refers to a function that optimizes delivery routes and times to carry out deliveries efficiently.

[0678] This invention provides a system that offers personalized meal suggestions to users and enables efficient meal ordering and delivery. This system primarily utilizes three components: a server, a terminal, and a user, along with a generative AI model.

[0679] Users input personal data such as their preferences, allergy information, and nutritional goals using their devices. This information is transmitted to a server via the device. Based on the received information, the server uses a generative AI model to generate appropriate meal menus from its database. This generative AI model is designed to provide suggestions that meet the individual needs of the user through analysis of past data and patterns.

[0680] Furthermore, the terminal connects with the user's portable devices (such as wearable devices) to acquire exercise-related activity data (e.g., steps taken, calories burned) in real time. The server analyzes this activity data and adjusts the menu based on the user's activity level for the day. For example, even if the user prompts, "Considering today's exercise, please suggest a high-protein, low-calorie menu," the generating AI model can make the optimal adjustments.

[0681] Finally, the user selects from the menu suggested via the terminal and confirms their order. The server optimizes the delivery route and timing based on the order information. Real-time traffic information is used for delivery optimization, ensuring fast and efficient delivery. Through this process, the system can support the provision of appropriate and efficient meals to users.

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] Users input their preferences, allergy information, and nutritional goals into the terminal. Specifically, they provide information in the form of prompts such as "vegan," "nut allergy," and "prefer high-protein foods." The entered data is formatted by the terminal and sent to the server.

[0685] Step 2:

[0686] The server searches the database based on the received user information and extracts meal menus that match the user's criteria. This process utilizes a generative AI model, performing data analysis based on the user's past preferences and similar patterns. The output is a list of recommended meal menus.

[0687] Step 3:

[0688] The device connects with the user's portable device to acquire real-time activity data (e.g., calories burned and steps taken). This acquired data is continuously transmitted to the server. This data is used as input, enabling dietary adjustments based on usage patterns.

[0689] Step 4:

[0690] The server receives and analyzes real-time activity data. This analysis readjusts the calories and nutrients in the currently suggested menu based on the user's activity level. AI-powered data processing is performed, and optimized meal suggestions are output.

[0691] Step 5:

[0692] The user selects their desired meal from an optimized menu list displayed on the terminal and confirms their order. The selected information is then sent back to the server via the terminal.

[0693] Step 6:

[0694] The server receives confirmed order information and optimizes delivery routes and timings. Here, a generative AI model algorithm is used to derive an efficient delivery plan that reflects real-time traffic information. As a result, optimized delivery instructions are output and provided to the delivery personnel.

[0695] (Application Example 1)

[0696] 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".

[0697] In modern times, there is a demand for easy access to meals that cater to diverse dietary preferences, allergies, and nutritional goals, while simultaneously making adjustments to meal content based on individual users' physical activity levels remains challenging. Furthermore, efficient delivery is crucial for food delivery services, and optimizing delivery routes in response to real-time circumstances is a key issue. This invention was developed to address these challenges.

[0698] 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.

[0699] In this invention, the server includes information acquisition means for acquiring user preferences, allergy information, and nutritional goals; data analysis means for analyzing physical activity data acquired from a portable device and dynamically adjusting meal menus in real time; and delivery optimization means for optimizing delivery routes and timing. This enables customized meal suggestions tailored to the individual circumstances of each user and efficient delivery.

[0700] "Information acquisition means" refers to a system for collecting data on users' preferences, allergy information, and nutritional goals.

[0701] The "suggestion method" is a function that selects and provides the most suitable meal menu based on user information acquired in advance.

[0702] "Activity data acquisition method" refers to a method for collecting data on a user's physical activity via a portable device.

[0703] A "menu adjustment mechanism" is a system for dynamically adjusting the proposed meal menu based on acquired activity data.

[0704] An "order processing system" is a system for managing and processing orders based on the meal menu selected by the user.

[0705] "Delivery optimization methods" refer to methods for optimally adjusting delivery routes and timings in order to achieve efficient deliveries.

[0706] "Data analysis means" refers to technology that processes physical activity data acquired from portable devices and dynamically adjusts the user's meal menu in real time.

[0707] A "portable device" is a terminal device that a user can wear and that is capable of measuring and transmitting physical activity data.

[0708] The system that realizes this invention utilizes terminals such as smartphones and tablets, as well as wearable devices, to provide individually optimized menus in order to satisfy users' meal requests and ensure efficient delivery.

[0709] The server collects user preferences, allergy information, and nutritional goals entered through the terminal, and manages this information using data acquisition means. Based on this, the suggestion means uses a generation AI model to select the optimal foods and provides customized meal suggestions to the user. Activity data is collected from the wearable device via Bluetooth or Wi-Fi and transmitted to the terminal using activity data acquisition means. The server processes this data using data analysis means and makes appropriate adjustments to the suggested menu in real time.

[0710] The order processing system enables an efficient order flow based on the meal menu selected by the user. Furthermore, the delivery optimization system utilizes real-time travel information to optimize delivery routes and times, ensuring fast and effective service.

[0711] As a concrete example, consider a scenario where a user enters into the app the request, "I'm vegan and would like a menu suitable for days with increased activity." In response to this request, the server analyzes the user's past meal history and activity data to suggest an appropriate vegan menu. For example, suppose the prompt is, "35-year-old male, vegan, with a nut allergy. I'd like a higher protein intake. Please suggest a meal suitable for days with more activity than yesterday." The server inputs this prompt into a generating AI model to create a customized suggestion tailored to the user's needs. This makes it possible to provide users with a healthy and balanced eating experience.

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] Users enter personal information into a smartphone app. This information includes preferences, allergy information, and nutritional goals. This information is transmitted from the device to a database and stored by the data retrieval system.

[0715] Step 2:

[0716] The server retrieves user information from the database and generates an optimal meal menu using a suggestion tool. Using a generation AI model, it creates prompt messages and selects a customized menu based on preferences and past history. Output prompt messages might include phrases like "vegan, nut allergy, high protein preferred."

[0717] Step 3:

[0718] The device acquires activity data from portable devices via Bluetooth. This data includes heart rate, steps taken, and calories burned. The activity data acquisition system collects this data and transmits it to the server in real time.

[0719] Step 4:

[0720] The server processes the received activity data using data analysis tools to assess the user's current physical activity level. Based on this, it updates prompt messages, adjusts the menu, and calculates how to achieve the recommended nutritional balance.

[0721] Step 5:

[0722] The user reviews and selects from the adjusted menu on their terminal. The selected menu information is registered by the order processing system, and the order is confirmed.

[0723] Step 6:

[0724] The server uses confirmed order information to calculate the optimal delivery route and time using delivery optimization tools. Based on real-time movement information, it optimizes deliveries and sends instructions to delivery personnel.

[0725] Step 7:

[0726] Once delivery is complete, the delivery status and handover information are fed back to the server and notified to the user. This completes the series of services.

[0727] 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.

[0728] This invention is a system that provides meal menus that comprehensively consider the user's preferences, allergy information, nutritional goals, and emotional state. This system utilizes information acquisition means, suggestion means, activity data acquisition means, menu adjustment means, order processing means, delivery optimization means, and an emotion engine.

[0729] When users begin using the system, they input their preferences, allergy information, and nutritional goals through their terminal. This data is immediately sent to and stored on the server. Additionally, an emotion engine operates on the terminal, using the camera and microphone to analyze the user's facial expressions and voice tone. The resulting emotional data is then sent to the server.

[0730] Based on user information, including this emotional data, the server uses AI algorithms to suggest the most suitable meal menu. For example, if a user is feeling stressed, it can suggest a menu that includes ingredients with relaxing properties.

[0731] The suggested menu is displayed to the user on their device, and the user's activity data (e.g., calories burned and frequency of physical activity) is taken into consideration. When the user selects items from the provided menu, that information is used to confirm the order through the order processing system.

[0732] Next, the server uses AI to optimize delivery routes and timings based on the order details and provides this information to delivery partners. By utilizing real-time traffic information, delivery efficiency is improved.

[0733] As a concrete example, when a user returns home tired from work and picks up their device, the emotion engine senses fatigue and stress. Based on this information, the server suggests a menu including vitamin-rich herbal tea and light snacks. Once the user selects this menu, the meal is efficiently delivered.

[0734] Thus, the system provided by the present invention improves the quality of life by analyzing the user's emotions and physical activities and quickly providing an individually optimized dining experience.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] Users input their preferences, allergy information, and nutritional goals through their terminals. This information is sent to the server as basic data for system use.

[0738] Step 2:

[0739] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone in real time using an emotion engine. This analysis identifies the user's emotional state (e.g., stress, happiness) and sends it to the server.

[0740] Step 3:

[0741] The server uses an AI algorithm based on acquired user information and emotional data to generate optimal suggestions from a large menu. For example, for a user who needs to relax, it selects a menu that includes ingredients with calming properties.

[0742] Step 4:

[0743] The server sends the suggested menu list to the terminal and displays it to the user. This includes nutritional information for the food, estimated calories, and emotional benefits.

[0744] Step 5:

[0745] The device works in conjunction with the user's wearable device to acquire activity data (e.g., exercise level, calories burned). This data is sent to a server and influences menu selections.

[0746] Step 6:

[0747] The server takes activity data into account and fine-tunes the suggested menu. This results in a meal plan that is best suited to the user's physical condition.

[0748] Step 7:

[0749] Users can select their preferred items from the suggested menu and confirm their order. This process can be easily done from the terminal.

[0750] Step 8:

[0751] Based on the confirmed order details, the server uses a delivery optimization algorithm to calculate the optimal delivery route and delivery time, and then communicates this to the delivery partner.

[0752] Step 9:

[0753] The terminal notifies the user of the delivery status in real time. This allows the user to know the estimated arrival time of their meal and wait accordingly.

[0754] Through these steps, users can quickly receive meals tailored to their emotional state and health condition, thereby improving their quality of life.

[0755] (Example 2)

[0756] 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".

[0757] While conventional meal delivery systems can suggest menus that take into account user preferences, allergy information, and nutritional goals, they have the challenge of not being able to provide detailed suggestions that reflect the user's emotional state in real time. Furthermore, systems for efficiently delivering the suggested menus are not adequately developed, making it difficult to improve user satisfaction.

[0758] 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.

[0759] In this invention, the server includes data collection means for acquiring preferences, allergy information, and nutritional goals; suggestion means for analyzing the acquired data and emotional state to make meal suggestions; and activity data management means for acquiring physical activity status and adjusting suggestions. This enables the suggestion of an optimal meal menu adapted to the user's emotional state and the setting of an efficient delivery route.

[0760] "Data collection methods" refer to technologies for acquiring user preferences, allergy information, and nutritional goals, and for accumulating the information necessary for the system.

[0761] The "proposal method" is a technology that analyzes acquired data and emotional states, constructs an optimal meal menu based on that analysis, and provides it to the user.

[0762] "Activity data management means" refers to technology that acquires the user's physical activity status and adjusts the suggested menu accordingly.

[0763] An "order processing method" is a technology that processes and confirms an order based on the meal menu selected by the user.

[0764] "Delivery setting means" is a technology that calculates the optimal delivery route and delivery time based on order information and gives instructions to the delivery person.

[0765] This system provides users with optimal meal menus that take into account both emotional needs and nutritional balance. Specifically, the server and the user's terminal work together to acquire and analyze various data, proposing individually optimized meal menus and delivering them efficiently.

[0766] The device functions as the user interface, receiving input from the user. This includes input of preferences, allergy information, and nutritional goals. Furthermore, the device is equipped with a camera and microphone, which the emotion engine uses to analyze the user's facial expressions and tone of voice, acquiring emotional data in real time. This allows the meal suggestions to be adjusted according to the user's emotional state.

[0767] The server stores information transmitted from the terminal and performs a wide variety of data analyses. The server is equipped with a generative AI model that generates the optimal meal menu based on the collected data. The AI ​​model comprehensively considers the user's emotional state, activity level, and individual dietary requirements.

[0768] As a concrete example, when a user returns home feeling stressed and operates their device, the emotion engine detects their stress level, and the server responds by suggesting a menu using ingredients effective for relaxation. This suggested menu includes vitamin-rich herbal tea and easily digestible light meals.

[0769] Furthermore, after an order is confirmed, the server uses AI to calculate the optimal delivery route and time, and instructs the delivery person on a specific delivery schedule. This ensures that meals are delivered to the user quickly and efficiently.

[0770] As an example of a prompt, inputting information that includes the user's emotional state and activity level, such as "Suggest a suitable meal for when I'm feeling emotionally unsettled. My activity level is moderate," allows for more accurate meal suggestions.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The user enters their preferences, allergy information, and nutritional goals into the terminal's interface. This input data is then sent from the terminal to the server. Specifically, the user enters the information according to the prompts on the screen and presses the submit button, at which point the data is transferred to the server.

[0774] Step 2:

[0775] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone, which are then analyzed by an emotion engine. Audio signals and video data are used as input, and these are processed by an emotion analysis algorithm to output data on the user's emotional state. Specifically, facial muscle movements and the pitch and speed of the voice are used as analysis points. The analysis results are sent to a server.

[0776] Step 3:

[0777] The server receives preference information, allergy information, nutritional goals, and emotional data transmitted from the terminal. Based on this, it uses a generative AI model to generate an optimal meal menu. It analyzes patterns in the input data and outputs ingredient combinations tailored to the user. Specifically, the process involves passing the input data to cloud-based computing resources and returning the results within a few seconds.

[0778] Step 4:

[0779] The server sends the generated meal menu to the terminal, which then displays it to the user. During this process, it compares the user's activity data and presents additional information about the menu. The system receives menu information as input and displays different options on the user screen as a result. Specific operations can be performed by touching or swiping the screen to view details.

[0780] Step 5:

[0781] The user selects their desired items from the displayed menu and confirms the order. The terminal sends this selection information to the server, and the order is processed. The process involves pressing the confirmation button after selecting items from the menu, which registers the data on the server.

[0782] Step 6:

[0783] The server calculates the optimal delivery route and timing using delivery optimization techniques based on confirmed order information. It outputs a delivery schedule using real-time travel route information. Specifically, it collects traffic information by referencing GPS data and selects the most efficient route.

[0784] Step 7:

[0785] The server provides the delivery person with the most suitable delivery information, and the meal is delivered to the user. In this final step, a delivery notification is displayed on the device, and the meal is delivered at the specified time. This is displayed as a notification that can be confirmed after delivery is complete.

[0786] (Application Example 2)

[0787] 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".

[0788] Conventional meal suggestion systems are limited to considering user preferences, allergy information, and nutritional goals, but they have the challenge of not being able to provide meal menus that take into account the user's emotional state. Furthermore, these systems still have challenges in comprehensively evaluating user activity information and emotional state to suggest the optimal meal menu in real time and to achieve efficient delivery.

[0789] 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.

[0790] In this invention, the server includes data acquisition means for acquiring the user's preferences, allergy information, and nutritional goals; emotion analysis means for acquiring the user's emotional state and reflecting it in the meal menu; and activity information acquisition means for acquiring activity information via a portable terminal. This makes it possible to propose a personalized meal menu that reflects the user's emotional state and activity information.

[0791] "Data acquisition means" refers to procedures or devices for acquiring user preferences, allergy information, and nutritional goals.

[0792] "Selection method" refers to a procedure or device for suggesting the most suitable meal menu to the user based on previously acquired information.

[0793] "Activity information acquisition means" refers to a procedure or device for acquiring user activity information via a portable terminal.

[0794] "Menu modification means" refers to a procedure or device for adjusting a proposed meal menu based on acquired activity information.

[0795] "Order management means" refers to a procedure or device for processing an order based on the menu selected by the user.

[0796] "Delivery planning means" refers to procedures or devices for optimizing delivery routes and timing.

[0797] "Emotional analysis means" refers to a procedure or device for acquiring the emotional state of a user and reflecting that information in the meal menu.

[0798] The system of this invention consists of a user, a terminal, and a server. The user inputs their preferences, allergy information, and nutritional goals into the terminal, which then transmits this information to the server. The terminal also analyzes the user's facial expressions and voice tone through its built-in camera and microphone, and acquires emotional data via an emotion analysis device. This emotional data is also transmitted to the server.

[0799] Based on the received information, the server integrates the user's preferences, allergy information, nutritional goals, and emotional state through data acquisition methods. Next, a generative AI model is used to generate a meal menu optimized for these factors, which is then displayed on the terminal via selection methods. AI models often utilize technologies such as Python's TensorFlow or PyTorch.

[0800] The user selects their preferred items from the suggested menu, and the order information is sent to the server. The server confirms the order via an order management system and optimizes the delivery route using a delivery planning system. The delivery planning utilizes map information APIs to obtain real-time traffic information. A specific example of this might be the Google Maps API.

[0801] As a concrete example, when a user returns home from work and picks up their device, the device detects the user's level of fatigue using emotion analysis. In response, the server suggests a meal menu with relaxing effects. For instance, a menu featuring herbal tea and ingredients effective for fatigue recovery might be displayed.

[0802] An example of a prompt message is, "Based on the user's emotional state, preferences, and nutritional goals, please suggest a meal menu that promotes relaxation." In this way, the system allows the user to quickly and efficiently receive the most suitable meal according to their current state.

[0803] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0804] Step 1:

[0805] The user inputs preferences, allergy information, and nutritional goals into the device. The device collects this data and transmits it to the server via a data acquisition mechanism. Based on the input data, the device performs preprocessing, such as standardizing the data format.

[0806] Step 2:

[0807] The device uses its camera and microphone to capture the user's facial expressions and voice tone. An emotion analysis system analyzes this data to calculate the user's emotional state (e.g., stress, happiness). This emotional data is then sent to a server.

[0808] Step 3:

[0809] The server integrates received preference information, nutritional goals, allergy information, and emotional data. After data integration, a generative AI model is executed to generate a meal menu optimized for the user. The AI ​​model performs data calculations to optimize the combination of ingredients from these inputs.

[0810] Step 4:

[0811] The server displays the suggested meal menu on the terminal via a selection mechanism. The terminal then suggests the menu to the user in natural language and generates prompt messages.

[0812] Step 5:

[0813] The user selects the menu item they deem most suitable. The terminal receives the selected menu item and sends this information to the server via the order management system. During this process, the terminal performs an error check on the selected information.

[0814] Step 6:

[0815] The server uses delivery planning tools to optimize the delivery route for the selected menu items. Real-time traffic information is obtained via a map information API, and delivery times are calculated. Historical data is also used to optimize delivery routes.

[0816] Step 7:

[0817] Optimized delivery information is provided to delivery personnel, ensuring that meals are delivered to users within the specified time. The terminal monitors delivery progress in real time and has a function to notify users of the delivery status.

[0818] 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.

[0819] 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.

[0820] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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."

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] Information acquisition means for obtaining user preferences, allergy information, and nutritional goals,

[0842] A suggestion method that proposes the optimal meal menu based on previously acquired information,

[0843] An activity data acquisition method that acquires user activity data via a wearable device,

[0844] A menu adjustment means that adjusts the proposed menu based on the acquired activity data,

[0845] An order processing means that processes orders based on the menu selected by the user,

[0846] A delivery optimization method that optimizes delivery routes and timing,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, wherein the suggestion means is configured to interactively suggest a menu to the user using natural language.

[0850] (Claim 3)

[0851] The system according to claim 1, wherein the delivery optimization means is configured to optimize delivery routes using real-time traffic information and provide this information to delivery partners.

[0852] "Example 1"

[0853] (Claim 1)

[0854] Information acquisition means for obtaining user preferences, allergy information, and nutritional goals,

[0855] A proposed method using a generative AI model that generates an optimal meal menu based on previously acquired information,

[0856] An activity data acquisition means that acquires user exercise-related information via a portable device,

[0857] A menu adjustment means for adapting a proposed menu based on acquired exercise-related information,

[0858] An order processing means that executes instructions based on the menu selected by the user,

[0859] A delivery optimization method that optimizes delivery routes and times,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein the suggestion means is configured to interactively suggest improved menus to the user using natural language.

[0863] (Claim 3)

[0864] The system according to claim 1, wherein the delivery optimization means is configured to optimize delivery routes using real-time traffic conditions and provide information to delivery personnel.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] Information acquisition means for obtaining user preferences, allergy information, and nutritional goals,

[0868] A suggestion method that proposes the optimal meal menu based on previously acquired information,

[0869] An activity data acquisition means for acquiring user activity data via a portable device,

[0870] A menu adjustment means that adjusts the proposed menu based on the acquired activity data,

[0871] An order processing means that processes orders based on the menu selected by the user,

[0872] A delivery optimization method that optimizes delivery routes and timing,

[0873] A data analysis method that analyzes physical activity data acquired from a portable device and dynamically adjusts meal menus in real time,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the suggestion means is configured to interactively suggest a menu to the user using natural language.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the delivery optimization means is configured to optimize delivery routes using real-time movement information and provide information to delivery personnel.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] A data collection method for obtaining preferences, allergy information, and nutritional goals,

[0882] A method for making dietary suggestions by analyzing acquired data and emotional states,

[0883] An activity data management system that acquires information on physical activity and adjusts suggestions accordingly,

[0884] An order processing means for confirming an order based on the selected meal,

[0885] A delivery setting method that calculates the delivery route and time based on order information,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, wherein the proposed means is configured to interactively present a meal menu using natural language processing.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein the delivery setting means is configured to optimize the delivery route using travel route information and give instructions to the delivery person.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] A data acquisition method for obtaining user preferences, allergy information, and nutritional goals,

[0894] A selection method that suggests the optimal meal menu based on previously acquired information,

[0895] An activity information acquisition means that acquires user activity information via a portable terminal,

[0896] A menu modification means that adjusts the proposed menu based on the acquired activity information,

[0897] An order management system that processes orders based on the menu selected by the user,

[0898] A delivery planning method that optimizes delivery routes and timing,

[0899] An emotional analysis method that acquires the emotional state of users and reflects it in the meal menu,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, wherein the selection means is configured to propose menus to the user in a bidirectional manner using natural language.

[0903] (Claim 3)

[0904] The system according to claim 1, wherein the delivery planning means is configured to optimize delivery routes using real-time traffic information and provide information to delivery personnel. [Explanation of symbols]

[0905] 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 acquisition means for obtaining user preferences, allergy information, and nutritional goals, A suggestion method that proposes the optimal meal menu based on previously acquired information, An activity data acquisition method that acquires user activity data via a wearable device, A menu adjustment means that adjusts the proposed menu based on the acquired activity data, An order processing means that processes orders based on the menu selected by the user, A delivery optimization method that optimizes delivery routes and timing, A system that includes this.

2. The system according to claim 1, wherein the suggestion means is configured to interactively suggest a menu to the user using natural language.

3. The system according to claim 1, wherein the delivery optimization means is configured to optimize delivery routes using real-time traffic information and provide this information to delivery partners.

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