system

Abstract

We provide the system. [Solution] Means for collecting users' health-related information, A means for analyzing the user's nutritional needs based on the said information, A means for generating meal suggestions that address analyzed needs, A means of selecting the necessary ingredients based on the proposal and carrying out the purchase procedure via the network, A method for identifying appropriate restaurants and shops using the user's current location information, A system that includes means of presenting this information to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, it is not easy for an individual to manage their own health and obtain appropriate nutrition in a busy lifestyle. Therefore, there is a need for a system that can easily receive nutrition proposals based on an individual's health status. However, in the current system, it is difficult to receive customized meal proposals that reflect the detailed health status of individual users. In addition, there is also a lack of effective ways to utilize health information and nutrition proposals in real time. The present invention aims to solve such problems.

Means for Solving the Problems

[0005] This invention provides a system that continuously collects and analyzes users' health-related information to identify their individualized nutritional needs. Based on the analyzed needs, it automatically generates appropriate meal suggestions and includes a means for selecting and purchasing necessary ingredients via a network. It also incorporates a process that uses the user's current location information to identify suitable restaurants and shops in the surrounding area. In this way, it is possible to support individual users in managing their health in a way that is easily usable in their daily lives.

[0006] "Means of collecting user health-related information" refers to functions that acquire health-related data such as weight, diet, exercise level, and sleep duration through the user's device or application.

[0007] "Methods for analyzing nutritional needs" refer to the process of analyzing the nutrients and dietary trends that users require, based on collected health-related information.

[0008] A "means for generating meal suggestions" refers to an automated system that presents users with appropriate meal content and eating methods based on their analyzed nutritional needs.

[0009] "The means of selecting ingredients and carrying out the purchase procedure via the network" refers to a function that identifies the ingredients necessary for the generated meal suggestions and makes them available for purchase online.

[0010] "A means of identifying suitable restaurants and shops using current location information" refers to a system that uses a user's real-time location information to identify restaurants and shops that offer suitable meals in their vicinity.

[0011] "Means of presenting to the user" refers to the interface displayed on a device or application so that the user can easily understand and utilize the analysis results, meal suggestions, and store information mentioned above. [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, when an emotion engine is combined. [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] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 is a system that supports users' health management, aiming to collect and analyze a large amount of health-related data and provide personalized dietary suggestions. This system has a software program running on a cloud server at its core and operates in conjunction with the user's device.

[0034] User devices input daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. This data is transmitted to a cloud server using secure communication methods. The system automatically aggregates daily health information simply by the user entering basic data.

[0035] The server stores the user's health-related information and uses a generative AI model to perform analysis. The analysis diagnoses nutritional needs based on the user's health status and lifestyle, and creates a personalized nutrition plan. Based on the results, it generates and presents appropriate meal suggestions to the user.

[0036] Furthermore, the server accesses the online supermarket database to select the necessary ingredients for meal suggestions. Users can easily purchase the required ingredients online. The server also utilizes the user's real-time location information to search for nearby restaurants and shops, providing information on suitable restaurants in the vicinity.

[0037] For example, if a user requests a low-carb meal at lunchtime, the server will use past data to assess the user's health and nutritional status and suggest suitable menu options. It can also use GPS information to guide users to nearby restaurants offering low-carb menus.

[0038] In this way, the present invention functions as a personalized health management and dietary suggestion system that users can use on a daily basis.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user terminal collects health data such as weight, diet, exercise level, and sleep duration entered by the user through health-related applications. Users can manually enter data or the device can automatically retrieve information in conjunction with the user.

[0042] Step 2:

[0043] The device periodically sends collected health data to a cloud server. Data transmission uses encrypted communication protocols to protect user privacy.

[0044] Step 3:

[0045] The server stores the received health data in a database. All data is structured and securely managed.

[0046] Step 4:

[0047] The server uses a generative AI model to analyze stored user health data. This model assesses the user's health status and nutritional needs by comparing them with past data.

[0048] Step 5:

[0049] The server creates a personalized nutrition plan for the user based on the analysis results. This plan includes recommended foods and meal menus for the user.

[0050] Step 6:

[0051] The server accesses the online supermarket's database and identifies the ingredients needed for the created nutrition plan. Users can then select and purchase these ingredients online.

[0052] Step 7:

[0053] The server obtains the user's current location using GPS and searches for suitable nearby restaurants and shops. Based on the identified shop information, it guides the user to easily visit them.

[0054] Step 8:

[0055] The server sends the generated nutrition plan and store information to the user's terminal and presents it to the user as appropriate meal suggestions. Based on the information received, the user then works to improve their daily eating habits.

[0056] (Example 1)

[0057] 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."

[0058] In today's busy lifestyle, selecting and implementing an optimal diet based on individual health conditions and nutritional needs is difficult for many people. Furthermore, the process of selecting and efficiently purchasing the right ingredients is complex. Additionally, if meal suggestions do not address individual circumstances, user satisfaction may suffer.

[0059] 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.

[0060] In this invention, the server includes a device for collecting the user's health-related information, a device for analyzing the user's nutritional needs using a generative AI model, a device for generating meal suggestions using prompt sentences, a device for selecting necessary ingredients and performing purchase procedures via a communication network, and a device for identifying relevant facilities using the user's current location information. This enables personalized nutritional suggestions, rapid ingredient purchases, and identification of optimal relevant facilities.

[0061] "User health-related information" refers to various health-related data such as the user's weight, diet, exercise level, and sleep duration.

[0062] A "cloud environment" is an environment that utilizes computing resources and storage capacity provided via the internet to store and process data.

[0063] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to perform analysis and provide insights into a user's health status and nutritional needs.

[0064] A "prompt statement" is an input statement used to give instructions to a generative AI model and obtain a specific output.

[0065] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's nutritional needs.

[0066] A "communication network" refers to the network infrastructure used to send and receive data.

[0067] "Related facilities" refer to stores and facilities that provide food and services suitable for the user, identified based on the user's current location information.

[0068] This invention is a system that efficiently supports users' health management. This system is centered around a software program that runs on the cloud and operates in conjunction with the user's device. Users can input health-related information daily using a device with a health management application installed. Specifically, this includes information such as weight, diet, exercise levels, and sleep duration.

[0069] The device transmits this health-related information to a cloud server via a secure communication method (e.g., an encrypted internet connection). The information is encrypted during this process to protect the user's data privacy. The server stores the received information in a dedicated database in preparation for subsequent analysis.

[0070] The server performs analysis using a generated AI model based on the stored data. This AI model has the ability to diagnose personalized nutritional needs by taking into account the user's past health data and lifestyle. For example, a prompt such as "Please suggest an optimal low-calorie, nutritionally balanced dinner plan based on the user's current lifestyle and health status" is used as an instruction to the AI ​​model. Based on this prompt, the AI ​​generates specific meal suggestions.

[0071] Furthermore, the server collaborates with food supply services on the network to select the necessary ingredients for the suggested meal. Users can then easily purchase the necessary items online using the generated ingredient list. The server also has the ability to use the user's real-time location information to search for nearby restaurants and food stores and present appropriate options to the user. For example, if a user requests a low-carb menu, the server can guide them to nearby restaurants.

[0072] This allows users to take an interest in their own health and smoothly manage their health in their daily lives.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users input daily health-related information, such as weight, diet, exercise levels, and sleep duration, into their devices using a health management application. The input data is organized within the application, and its integrity is checked. As output, health-related information that has been verified for integrity is generated.

[0076] Step 2:

[0077] The device sends verified health information to the cloud server using a secure protocol (e.g., HTTPS). During this process, the device encrypts the data to protect user privacy. The output is an encrypted health data packet.

[0078] Step 3:

[0079] The server decrypts the received encrypted data and stores it in the database. During storage, the data is indexed to improve searchability. The output is a grouped health information record stored in the database.

[0080] Step 4:

[0081] The server starts an analysis using a generative AI model based on health information stored in the database. Specifically, it uses prompts to assess the user's health status and diagnoses their individualized nutritional needs. The output is the nutritional needs and diagnosis results based on the analysis.

[0082] Step 5:

[0083] The server automatically generates specific meal suggestions based on the nutritional needs assessment. The generated meal suggestions are created using an algorithm that selects the optimal menu from a large number of options. The output is a user-optimized list of meal suggestions.

[0084] Step 6:

[0085] Based on the generated meal suggestions, the server accesses a database of food supply services on the network and selects the necessary ingredients. The selected ingredient information considers the most economical and nutritious choices for the user. The output is a list of the required ingredients.

[0086] Step 7:

[0087] The server uses the user's location services to identify the nearest suitable restaurants and establishments. Prioritizing establishments that offer the suggested meal options, the server provides a list of nearby relevant establishments.

[0088] Step 8:

[0089] Users receive meal suggestions and facility information on their devices, and can purchase ingredients online or check information for visiting stores. The output consists of options for actions that help users manage their health.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern society, personalized health management tailored to individual lifestyles and dietary recommendations based on those lifestyles are crucial. However, many systems lack sufficient data collection and analysis capabilities, making it difficult to provide personalized dietary recommendations. Furthermore, the cumbersome process of actually obtaining food based on these recommendations is another challenge. Additionally, the system fails to accurately identify appropriate dining facilities based on the user's current location, resulting in a less-than-ideal user experience.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for collecting user health-related information, means for analyzing the user's nutritional needs based on said information, means for generating meal suggestions corresponding to the analyzed needs, means for executing food delivery procedures based on the generated meal suggestions, and means for identifying appropriate dining establishments using the user's current location information. This enables personalized health management and meal suggestions, as well as easy food retrieval based on them. It also enables guidance to appropriate dining establishments according to the user's current location, improving the user experience.

[0095] "User health-related information" refers to data that indicates the user's health status in their daily life, and specifically includes weight, diet, exercise level, and sleep duration.

[0096] "Means of analyzing nutritional needs" refer to technologies and methods for analyzing the nutrients and dietary content that users need based on collected health-related information, and for providing appropriate dietary suggestions.

[0097] "Methods for generating meal suggestions" refer to technologies and methods for creating and providing specific content tailored to the analyzed nutritional needs of users.

[0098] "Means for carrying out food delivery procedures" refers to technologies and methods for carrying out procedures to deliver suggested food items to users based on generated meal suggestions.

[0099] "Methods for identifying appropriate dining establishments using current location information" refers to technologies and methods that search for nearby restaurants and other establishments based on the user's current location, and identify facilities that meet the user's needs.

[0100] To realize this invention, a dedicated application is installed on the user's device to collect health-related information. The collected data is then securely transmitted to a cloud server. The cloud server receives various health data and uses a generative AI model to analyze the user's nutritional needs in detail. This generative AI model is built using a solution such as PyTorch and operates based on the Python language.

[0101] The server generates personalized meal suggestions based on the analysis results and sends the information to the user's device. It also collaborates with a food delivery network to arrange for the delivery of necessary food items based on the generated meal suggestions. This allows users to easily obtain the ingredients for their suggested meals online.

[0102] Furthermore, the server uses the user's current location information to identify and provide the most suitable dining establishments based on the user's health needs. This process utilizes advanced location services to pinpoint nearby dining establishments.

[0103] As a concrete example of its use, when a user enters their weight and exercise level from the previous day into the app in the morning, the server creates an optimal nutrition plan through a generative AI model. For example, it might suggest, "You're lacking protein, so we recommend a high-protein recipe for lunch today." Furthermore, based on that suggestion, it will guide the user to high-protein menus offered at nearby restaurants, and if the user wishes, they can order those menus through a food delivery service.

[0104] An example of a prompt to input into the generating AI model is, "Based on recent exercise and dietary data, please create a suggestion for the best late-night snack."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] Users input health-related information into their devices. Specifically, users enter daily health data such as weight, diet, exercise levels, and sleep duration into a dedicated application. This information is then transmitted from the device to a cloud server via secure communication.

[0108] Step 2:

[0109] The server stores received health-related information and analyzes it using a generative AI model. The input is the user's health data, and the model uses this to diagnose the individual's nutritional needs. Data processing includes comparison with the user's past records and analysis using statistical methods. The output is a nutrition plan based on the analysis.

[0110] Step 3:

[0111] The server generates personalized meal suggestions based on the analysis results. A prompt message is sent to the generating AI model: "Create meal suggestions to compensate for the user's exercise level and nutritional deficiencies," and the suggested menu is retrieved. The input for this step is the output of step 2, and the output is a specific meal menu.

[0112] Step 4:

[0113] The server prepares to execute food delivery procedures based on the generated meal suggestions. The input is the meal suggestions generated in step 3. The server identifies the necessary ingredients for the suggested menu and integrates them with online sales services to make them available for order. The output is the relevant information for the delivery procedures.

[0114] Step 5:

[0115] Based on the user's current location, the system identifies suitable restaurants and bars. The server receives the location information, queries the relevant database, and recommends a suitable establishment for the user. The input for this step is the user's current location, and the output is a list of recommended restaurants and bars.

[0116] Step 6:

[0117] The terminal displays meal suggestions, food delivery information, and restaurant information sent from the server to the user. The user can select a suggested menu item and proceed directly to the ordering process. The input is the output of steps 4 and 5, and the output is the information presented to the user.

[0118] 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.

[0119] This invention is a system that supports users' health management, and is particularly specialized in providing meal suggestions that take into account the user's emotional state. This system operates through the collaboration of a complex software program installed on a cloud server and an application on the user's terminal.

[0120] The user's device collects daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. In addition, the device is equipped with emotion recognition capabilities, detecting emotional states using user-input text, voice, and the device's sensors. Users can manually input this data, or the information can be automatically acquired using the sensors.

[0121] The server receives collected health and emotional state data and stores it in a database. The server's built-in emotion engine analyzes the received emotional state data and generates a user emotional profile. This profile is integrated with health data to contribute to creating personalized meal suggestions based on the user's nutritional needs and emotions.

[0122] For example, if a user is feeling stressed, the server generates a meal plan that incorporates ingredients to help with relaxation. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online supermarket database. The user can then purchase the suggested ingredients online with just a few clicks.

[0123] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their emotional state. For example, if a user wants a change of pace, it can recommend cafes and restaurants that are perfect for refreshing themselves.

[0124] This makes it possible to realize more precise and adaptive health management and dietary suggestions that take into account the user's emotional state in their daily life.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user's device collects health data such as weight, diet, exercise levels, and sleep duration entered by the user through health-related applications. Furthermore, it automatically recognizes the user's emotions from text and voice data using the device's camera and microphone.

[0128] Step 2:

[0129] The device transmits collected health and emotional data to a cloud server using an encrypted communication protocol. Real-time data transmission is desirable.

[0130] Step 3:

[0131] The server stores the received health and emotional data in a database. Based on the received emotional data, the emotion engine generates an emotional profile.

[0132] Step 4:

[0133] The server analyzes health data and emotional profiles to generate a customized meal plan based on the user's nutritional needs and current emotional state. For example, it might recommend foods rich in B vitamins to a user who is feeling down.

[0134] Step 5:

[0135] The server identifies the necessary ingredients for the generated meal plan from the online supermarket database and provides the user with online purchasing options. The user selects the ingredients suggested by the server and proceeds with the purchase.

[0136] Step 6:

[0137] The server obtains the user's current location via GPS and searches its database for stores and restaurants that match the user's emotional state. For example, if the user is looking to relax, it will recommend a restaurant that uses natural ingredients.

[0138] Step 7:

[0139] The server sends customized meal plans, information on available ingredients, and store locations to the user's terminal and displays these suggestions to the user. Based on the information provided, the user makes emotionally responsive and healthy choices in their daily life.

[0140] (Example 2)

[0141] 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 will be referred to as the "terminal."

[0142] In modern society, managing users' health is a crucial issue. However, conventional health management systems rarely provide nutritional suggestions that adequately consider the user's emotional state. As a result, it is difficult to mitigate the impact of emotions such as stress on health. Therefore, there is a need to provide more precise and individualized health management and dietary suggestions that take emotional states into account.

[0143] 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.

[0144] In this invention, the server includes means for collecting the user's biometric data and psychological state, means for generating the user's psychological profile based on said information, and means for analyzing the user's nutritional needs based on said profile and health-related information. This enables personalized nutritional suggestions that take into account the user's emotional state.

[0145] "User biometric data" refers to information about the user's physical health, such as weight, diet, exercise level, and sleep duration.

[0146] "Psychological state" refers to information that indicates the user's mental state, such as their emotions and stress levels.

[0147] A "psychological profile" is a collection of data that analyzes a user's emotional state and captures its characteristics.

[0148] "Health-related information" refers to a set of data related to the user's physical and mental health status.

[0149] "Nutritional needs" refer to the user's requirements for nutrients necessary to maintain or improve their health.

[0150] "Meal suggestions" refer to meal plans and recommendations provided based on the user's nutritional needs and psychological state.

[0151] A "communication network" refers to network infrastructure used for exchanging information, such as the internet.

[0152] "Purchase procedure" refers to the series of actions taken to select and purchase a product in online shopping, etc.

[0153] "Location information" refers to data about a user's current location, which is obtained using technologies such as GPS.

[0154] "Means of conducting purchase procedures via a communication network" refers to a system that uses networks such as the internet to purchase necessary food items online.

[0155] This invention is a system that supports users' health management, and in particular aims to provide meal suggestions that take into account the user's emotional state. This system operates in conjunction with a complex software program installed on a cloud server and an application on the user's terminal.

[0156] The user terminal uses health-related applications to collect daily biometric data such as weight, diet, exercise levels, and sleep duration. The terminal also features emotion recognition capabilities, allowing it to detect the user's psychological state using text and voice input, as well as sensors built into the device. Users can manually input this data or have it automatically acquired using the sensors.

[0157] The server receives collected biometric and psychological data and stores it in a database. The server's built-in emotion engine analyzes the emotional data and generates a user psychological profile. This profile is integrated with health-related information and used to create personalized dietary recommendations based on the user's nutritional needs and psychological state.

[0158] For example, if a user is experiencing stress, the server generates a meal plan incorporating stress-relieving ingredients. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online marketplace database. Users can then purchase the suggested ingredients online with just a few clicks.

[0159] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their psychological state. For example, if a user needs a change of pace, it can guide them to a cafe or restaurant suitable for regaining their energy.

[0160] Examples of prompts to input into the generating AI model include, "Create the optimal meal plan for when the user is feeling stressed," and "Suggest restaurants that match the user's current emotional state." In this way, more sophisticated and adaptive health management and meal suggestions that take into account the user's psychological state in daily life can be realized.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user's device uses health-related applications to collect daily biometric data such as weight, diet, exercise level, and sleep duration. Input includes data manually entered by the user and data automatically collected from the device's sensors. This data is converted into an internal data format and prepared for subsequent processing. Specifically, when a user takes a photo of their meal and registers the information in the app, the nutritional data of the food is automatically recorded.

[0164] Step 2:

[0165] The user terminal uses emotion recognition to detect the user's psychological state based on text, voice data, and data acquired from built-in sensors. Inputs include the user's emotional diary and voice memos, which are analyzed to quantify the emotional state and store as part of the psychological profile. Specifically, speech recognition technology is used to analyze keywords in conversations and measure stress levels.

[0166] Step 3:

[0167] The server receives biometric and psychological state data transmitted from user terminals and stores them in a database. The input is a data stream from the end-user terminal, and the output generates a record of daily health and emotional states in the integrated database. Specifically, data is uploaded in real time and stored in server storage for analysis.

[0168] Step 4:

[0169] The server uses an emotion engine to analyze psychological data and generate a user's psychological profile. It takes past and current emotional data as input and uses this data to computationally detect the user's individual emotional tendencies. The output includes user characteristics such as "tendency to show high stress levels on weekends." The emotion engine combines natural language processing and machine learning to create the profile.

[0170] Step 5:

[0171] The server analyzes the user's nutritional needs based on their psychological profile and biometric data, and generates customized meal suggestions. It uses integrated user data as input to calculate the optimal balance of nutrients. The output is a nutrition plan tailored to the user's emotions and health status. Specific operations include selecting food groups using a nutrition algorithm.

[0172] Step 6:

[0173] The server configures the system to allow users to purchase necessary ingredients via the network based on the generated meal suggestions. The output connects to the online supermarket's database and provides users with links to purchase items. For example, users can easily proceed with the purchase process by simply pressing the "Add to Cart" button on the app.

[0174] Step 7:

[0175] The server obtains the user's current location information and identifies stores and restaurants that match their psychological state. The input is the user's real-time location information, and the system searches a store database based on the generated psychological profile, providing recommended store information as output. Specifically, this includes displaying nearby relaxing cafes on a map.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] Conventional health management systems lack the ability to support users' mental health by providing meal suggestions that take into account the user's emotional state or identifying appropriate dining facilities. Therefore, there is a need for more sophisticated and personalized services based on the user's emotions and health data.

[0179] 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.

[0180] In this invention, the server includes means for collecting user health-related information, means for detecting the user's emotional state, and means for suggesting foods that promote relaxation and mood improvement based on the detected emotional state. This enables adaptive, personalized meal suggestions and identification of dining establishments that take the user's mental health into consideration.

[0181] "Means of collecting user health-related information" refers to functions that acquire health-related data such as weight, diet, exercise levels, and sleep duration from the user's device.

[0182] A "means for analyzing nutritional needs" is a mechanism that analyzes the nutrients and dietary content required by users based on collected health-related information, and identifies specific needs.

[0183] A "means for generating meal suggestions" is a system that creates meal menus suitable for the user based on analyzed nutritional needs and emotional state.

[0184] "Means of conducting purchase procedures via a communication network" refers to the process of using a network to place an order for the proposed ingredients to be purchased online.

[0185] "Means for detecting a user's emotional state" refers to technologies that recognize and classify a user's emotions using text, voice, or sensors.

[0186] "A means of suggesting ingredients that promote relaxation and mood improvement" refers to a system that takes into account the user's current emotional state and selects and suggests ingredients that contribute to stress reduction and mood improvement.

[0187] "Means for identifying appropriate dining establishments or offices" refers to a function that searches for and selects suitable facilities from a relevant database based on the user's location information and emotional state.

[0188] This invention relates to a system that supports user health management and provides meal suggestions that take into account the user's emotional state. The server receives health-related information and emotional state data transmitted from the user terminal and analyzes it. The specific program functions as follows:

[0189] First, the user's device collects health-related information. This is done by inputting data such as weight, diet, exercise levels, and sleep duration via a dedicated application or by automatically acquiring it through sensors. The device also uses emotion recognition technology to analyze the user's emotions from text input, voice, and sensor data. Emotion recognition software such as Google® Cloud AI and IBM Watson® can be used for emotion analysis.

[0190] The server stores this data in a database on Amazon Web Services (AWS®) and then analyzes the user's nutritional needs. This analysis uses Python to integrate health and emotional data and generates personalized meal suggestions using a generative AI model.

[0191] The suggested meals include information on the necessary ingredients, which is transmitted via a communication network to online grocery sales services. This allows users to easily purchase the recommended ingredients.

[0192] Furthermore, based on the user's emotional state, food and beverage options related to disaster prevention and stress reduction are suggested. The server utilizes the user's location information to search for the most suitable restaurants or offices from a relevant database and guide the user there.

[0193] For example, if a user tells the robot, "I'm feeling a bit down today," the server will analyze that emotion, play mood-enhancing music, suggest a chocolate dessert recipe, and, if necessary, automatically order the ingredients.

[0194] An example of a prompt message for the generating AI model would be: "Please suggest an energy-boosting meal plan recommended for the afternoon when the user is feeling tired."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user's device collects health-related information. Users manually input data such as weight, diet, exercise levels, and sleep duration using an application, or the device's sensors automatically acquire this information. As a result, health-related data is input, and a health information file containing that data is output.

[0198] Step 2:

[0199] The user's device performs emotion recognition. It passes the user's text input or voice to emotion recognition software for analysis of their emotional state. The input is text or voice data, and the output is an emotion profile indicating the user's emotional state. This process utilizes emotion analysis APIs such as Google Cloud AI.

[0200] Step 3:

[0201] The server receives health information files and emotional profiles sent from the user's terminal. The input data includes health information and emotional profiles, which the server stores in a database on AWS. The output is the identification information for the stored data.

[0202] Step 4:

[0203] The server analyzes health information and emotional profiles to identify the user's nutritional needs. Using Python scripts, it integrates and analyzes the data, and a generated AI model suggests appropriate nutrients and meal plans based on the input data. The output is a meal plan optimized for the user.

[0204] Step 5:

[0205] The server processes the purchase of ingredients online via the communication network based on the generated meal plan. The input is the ingredient information specified in the meal plan, and the output is the purchase confirmation information from the online store.

[0206] Step 6:

[0207] The server uses the user's emotional state and location information to search for the most suitable restaurants and offices. It searches relevant databases, receiving location information and emotional status data as input, and outputs a list of recommended establishments.

[0208] Step 7:

[0209] Users can choose actions based on information provided by the server. They execute their actual meal plans by purchasing suggested ingredients or visiting recommended restaurants. The input is suggestions from the server, and the output is a log of the user's actions.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention is a system that supports users' health management, aiming to collect and analyze a large amount of health-related data and provide personalized dietary suggestions. This system has a software program running on a cloud server at its core and operates in conjunction with the user's device.

[0227] User devices input daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. This data is transmitted to a cloud server using secure communication methods. The system automatically aggregates daily health information simply by the user entering basic data.

[0228] The server stores the user's health-related information and uses a generative AI model to perform analysis. The analysis diagnoses nutritional needs based on the user's health status and lifestyle, and creates a personalized nutrition plan. Based on the results, it generates and presents appropriate meal suggestions to the user.

[0229] Furthermore, the server accesses the online supermarket database to select the necessary ingredients for meal suggestions. Users can easily purchase the required ingredients online. The server also utilizes the user's real-time location information to search for nearby restaurants and shops, providing information on suitable restaurants in the vicinity.

[0230] For example, if a user requests a low-carb meal at lunchtime, the server will use past data to assess the user's health and nutritional status and suggest suitable menu options. It can also use GPS information to guide users to nearby restaurants offering low-carb menus.

[0231] In this way, the present invention functions as a personalized health management and dietary suggestion system that users can use on a daily basis.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user terminal collects health data such as weight, diet, exercise level, and sleep duration entered by the user through health-related applications. Users can manually enter data or the device can automatically retrieve information in conjunction with the user.

[0235] Step 2:

[0236] The device periodically sends collected health data to a cloud server. Data transmission uses encrypted communication protocols to protect user privacy.

[0237] Step 3:

[0238] The server stores the received health data in a database. All data is structured and securely managed.

[0239] Step 4:

[0240] The server uses a generative AI model to analyze stored user health data. This model assesses the user's health status and nutritional needs by comparing them with past data.

[0241] Step 5:

[0242] The server creates a personalized nutrition plan for the user based on the analysis results. This plan includes recommended foods and meal menus for the user.

[0243] Step 6:

[0244] The server accesses the online supermarket's database and identifies the ingredients needed for the created nutrition plan. Users can then select and purchase these ingredients online.

[0245] Step 7:

[0246] The server obtains the user's current location using GPS and searches for suitable nearby restaurants and shops. Based on the identified shop information, it guides the user to easily visit them.

[0247] Step 8:

[0248] The server sends the generated nutrition plan and store information to the user's terminal and presents it to the user as appropriate meal suggestions. Based on the information received, the user then works to improve their daily eating habits.

[0249] (Example 1)

[0250] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0251] In today's busy lifestyle, selecting and implementing an optimal diet based on individual health conditions and nutritional needs is difficult for many people. Furthermore, the process of selecting and efficiently purchasing the right ingredients is complex. Additionally, if meal suggestions do not address individual circumstances, user satisfaction may suffer.

[0252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0253] In this invention, the server includes a device for collecting the user's health-related information, a device for analyzing the user's nutritional needs using a generative AI model, a device for generating meal suggestions using prompt sentences, a device for selecting necessary ingredients and performing purchase procedures via a communication network, and a device for identifying relevant facilities using the user's current location information. This enables personalized nutritional suggestions, rapid ingredient purchases, and identification of optimal relevant facilities.

[0254] "User health-related information" refers to various health-related data such as the user's weight, diet, exercise level, and sleep duration.

[0255] A "cloud environment" is an environment that utilizes computing resources and storage capacity provided via the internet to store and process data.

[0256] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to perform analysis and provide insights into a user's health status and nutritional needs.

[0257] A "prompt statement" is an input statement used to give instructions to a generative AI model and obtain a specific output.

[0258] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's nutritional needs.

[0259] A "communication network" refers to the network infrastructure used to send and receive data.

[0260] "Related facilities" refer to stores and facilities that provide food and services suitable for the user, identified based on the user's current location information.

[0261] This invention is a system that efficiently supports users' health management. This system is centered around a software program that runs on the cloud and operates in conjunction with the user's device. Users can input health-related information daily using a device with a health management application installed. Specifically, this includes information such as weight, diet, exercise levels, and sleep duration.

[0262] The device transmits this health-related information to a cloud server via a secure communication method (e.g., an encrypted internet connection). The information is encrypted during this process to protect the user's data privacy. The server stores the received information in a dedicated database in preparation for subsequent analysis.

[0263] The server performs analysis using a generated AI model based on the stored data. This AI model has the ability to diagnose personalized nutritional needs by taking into account the user's past health data and lifestyle. For example, a prompt such as "Please suggest an optimal low-calorie, nutritionally balanced dinner plan based on the user's current lifestyle and health status" is used as an instruction to the AI ​​model. Based on this prompt, the AI ​​generates specific meal suggestions.

[0264] Furthermore, the server collaborates with food supply services on the network to select the necessary ingredients for the suggested meal. Users can then easily purchase the necessary items online using the generated ingredient list. The server also has the ability to use the user's real-time location information to search for nearby restaurants and food stores and present appropriate options to the user. For example, if a user requests a low-carb menu, the server can guide them to nearby restaurants.

[0265] This allows users to take an interest in their own health and smoothly manage their health in their daily lives.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] Users input daily health-related information, such as weight, diet, exercise levels, and sleep duration, into their devices using a health management application. The input data is organized within the application, and its integrity is checked. As output, health-related information that has been verified for integrity is generated.

[0269] Step 2:

[0270] The device sends verified health information to the cloud server using a secure protocol (e.g., HTTPS). During this process, the device encrypts the data to protect user privacy. The output is an encrypted health data packet.

[0271] Step 3:

[0272] The server decrypts the received encrypted data and stores it in the database. During storage, the data is indexed to improve searchability. The output is a grouped health information record stored in the database.

[0273] Step 4:

[0274] The server starts an analysis using a generative AI model based on health information stored in the database. Specifically, it uses prompts to assess the user's health status and diagnoses their individualized nutritional needs. The output is the nutritional needs and diagnosis results based on the analysis.

[0275] Step 5:

[0276] The server automatically generates specific meal suggestions based on the nutritional needs assessment. The generated meal suggestions are created using an algorithm that selects the optimal menu from a large number of options. The output is a user-optimized list of meal suggestions.

[0277] Step 6:

[0278] Based on the generated meal suggestions, the server accesses a database of food supply services on the network and selects the necessary ingredients. The selected ingredient information considers the most economical and nutritious choices for the user. The output is a list of the required ingredients.

[0279] Step 7:

[0280] The server uses the user's location information service to identify the nearest suitable restaurants and facilities. Priority is given to stores that offer the proposed meal content. The output is a list of nearby related facilities.

[0281] Step 8:

[0282] The user receives meal suggestions and facility information on the terminal, purchases ingredients online, or checks information for visiting the store. The output is options for actions that are useful for the user's health management.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In modern society, health management tailored to individual lifestyles and meal suggestions based on it are important. However, in many systems, data collection and analysis are insufficient, making it difficult to provide individualized meal suggestions. Also, the complexity of the procedures for actually obtaining food based on meal suggestions is an issue. Furthermore, there is a problem that appropriate dining facilities near the user's current location are not accurately identified, and the user experience is not improved.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for collecting user health-related information, means for analyzing the user's nutritional needs based on said information, means for generating meal suggestions corresponding to the analyzed needs, means for executing food delivery procedures based on the generated meal suggestions, and means for identifying appropriate dining establishments using the user's current location information. This enables personalized health management and meal suggestions, as well as easy food retrieval based on them. It also enables guidance to appropriate dining establishments according to the user's current location, improving the user experience.

[0288] "User health-related information" refers to data that indicates the user's health status in their daily life, and specifically includes weight, diet, exercise level, and sleep duration.

[0289] "Means of analyzing nutritional needs" refer to technologies and methods for analyzing the nutrients and dietary content that users need based on collected health-related information, and for providing appropriate dietary suggestions.

[0290] "Methods for generating meal suggestions" refer to technologies and methods for creating and providing specific content tailored to the analyzed nutritional needs of users.

[0291] "Means for carrying out food delivery procedures" refers to technologies and methods for carrying out procedures to deliver suggested food items to users based on generated meal suggestions.

[0292] "Methods for identifying appropriate dining establishments using current location information" refers to technologies and methods that search for nearby restaurants and other establishments based on the user's current location, and identify facilities that meet the user's needs.

[0293] To realize this invention, a dedicated application is installed on the user's device to collect health-related information. The collected data is then securely transmitted to a cloud server. The cloud server receives various health data and uses a generative AI model to analyze the user's nutritional needs in detail. This generative AI model is built using a solution such as PyTorch and operates based on the Python language.

[0294] The server generates personalized meal suggestions based on the analysis results and sends the information to the user's device. It also collaborates with a food delivery network to arrange for the delivery of necessary food items based on the generated meal suggestions. This allows users to easily obtain the ingredients for their suggested meals online.

[0295] Furthermore, the server uses the user's current location information to identify and provide the most suitable dining establishments based on the user's health needs. This process utilizes advanced location services to pinpoint nearby dining establishments.

[0296] As a concrete example of its use, when a user enters their weight and exercise level from the previous day into the app in the morning, the server creates an optimal nutrition plan through a generative AI model. For example, it might suggest, "You're lacking protein, so we recommend a high-protein recipe for lunch today." Furthermore, based on that suggestion, it will guide the user to high-protein menus offered at nearby restaurants, and if the user wishes, they can order those menus through a food delivery service.

[0297] An example of a prompt to input into the generating AI model is, "Based on recent exercise and dietary data, please create a suggestion for the best late-night snack."

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The user inputs health-related information into the terminal. Specifically, the user inputs daily health data such as weight, diet content, exercise volume, and sleep time into a dedicated application. This information is input and transmitted from the terminal to the cloud server via secure communication.

[0301] Step 2:

[0302] The server accumulates the received health-related information and performs analysis using a generated AI model. The input is the user's health data, and the model diagnoses the individual's nutritional needs based on this. As data processing, comparison with the user's past records and analysis using statistical methods are carried out. The output is a nutritional plan based on the analysis.

[0303] Step 3:

[0304] Based on the analysis results, the server generates personalized diet recommendations. A prompt sentence "Create a diet recommendation to supplement the user's exercise volume and nutritional deficiencies" is sent to the generated AI model to obtain a recommended menu. The input for this step is the output of Step 2, and the output is a specific diet menu.

[0305] Step 4:

[0306] Based on the generated diet recommendation, the server prepares for executing the food delivery procedure. The input is the diet recommendation generated in Step 3. The server identifies the necessary ingredients for the recommended menu and coordinates with an online sales service to arrange it in an orderable form. The output is information related to the delivery procedure.

[0307] Step 5:

[0308] Based on the user's current location information, appropriate dining facilities are identified. The server receives the location information, queries the relevant database, and recommends facilities suitable for the user. The input for this step is the user's current location information, and the output is a list of recommended dining facilities.

[0309] Step 6:

[0310] The terminal displays meal suggestions, food delivery information, and restaurant information sent from the server to the user. The user can select a suggested menu item and proceed directly to the ordering process. The input is the output of steps 4 and 5, and the output is the information presented to the user.

[0311] 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.

[0312] This invention is a system that supports users' health management, and is particularly specialized in providing meal suggestions that take into account the user's emotional state. This system operates through the collaboration of a complex software program installed on a cloud server and an application on the user's terminal.

[0313] The user's device collects daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. In addition, the device is equipped with emotion recognition capabilities, detecting emotional states using user-input text, voice, and the device's sensors. Users can manually input this data, or the information can be automatically acquired using the sensors.

[0314] The server receives collected health and emotional state data and stores it in a database. The server's built-in emotion engine analyzes the received emotional state data and generates a user emotional profile. This profile is integrated with health data to contribute to creating personalized meal suggestions based on the user's nutritional needs and emotions.

[0315] For example, if a user is feeling stressed, the server generates a meal plan that incorporates ingredients to help with relaxation. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online supermarket database. The user can then purchase the suggested ingredients online with just a few clicks.

[0316] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their emotional state. For example, if a user wants a change of pace, it can recommend cafes and restaurants that are perfect for refreshing themselves.

[0317] This makes it possible to realize more precise and adaptive health management and dietary suggestions that take into account the user's emotional state in their daily life.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user's device collects health data such as weight, diet, exercise levels, and sleep duration entered by the user through health-related applications. Furthermore, it automatically recognizes the user's emotions from text and voice data using the device's camera and microphone.

[0321] Step 2:

[0322] The device transmits collected health and emotional data to a cloud server using an encrypted communication protocol. Real-time data transmission is desirable.

[0323] Step 3:

[0324] The server stores the received health and emotional data in a database. Based on the received emotional data, the emotion engine generates an emotional profile.

[0325] Step 4:

[0326] The server analyzes health data and emotional profiles to generate a customized meal plan based on the user's nutritional needs and current emotional state. For example, it might recommend foods rich in B vitamins to a user who is feeling down.

[0327] Step 5:

[0328] The server identifies the necessary ingredients for the generated meal plan from the online supermarket database and provides the user with online purchasing options. The user selects the ingredients suggested by the server and proceeds with the purchase.

[0329] Step 6:

[0330] The server obtains the user's current location via GPS and searches its database for stores and restaurants that match the user's emotional state. For example, if the user is looking to relax, it will recommend a restaurant that uses natural ingredients.

[0331] Step 7:

[0332] The server sends customized meal plans, information on available ingredients, and store locations to the user's terminal and displays these suggestions to the user. Based on the information provided, the user makes emotionally responsive and healthy choices in their daily life.

[0333] (Example 2)

[0334] 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".

[0335] In modern society, managing users' health is a crucial issue. However, conventional health management systems rarely provide nutritional suggestions that adequately consider the user's emotional state. As a result, it is difficult to mitigate the impact of emotions such as stress on health. Therefore, there is a need to provide more precise and individualized health management and dietary suggestions that take emotional states into account.

[0336] 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.

[0337] In this invention, the server includes means for collecting the user's biometric data and psychological state, means for generating the user's psychological profile based on said information, and means for analyzing the user's nutritional needs based on said profile and health-related information. This enables personalized nutritional suggestions that take into account the user's emotional state.

[0338] "User biometric data" refers to information about the user's physical health, such as weight, diet, exercise level, and sleep duration.

[0339] "Psychological state" refers to information that indicates the user's mental state, such as their emotions and stress levels.

[0340] A "psychological profile" is a collection of data that analyzes a user's emotional state and captures its characteristics.

[0341] "Health-related information" refers to a set of data related to the user's physical and mental health status.

[0342] "Nutritional needs" refer to the user's requirements for nutrients necessary to maintain or improve their health.

[0343] "Meal suggestions" refer to meal plans and recommendations provided based on the user's nutritional needs and psychological state.

[0344] A "communication network" refers to network infrastructure used for exchanging information, such as the internet.

[0345] "Purchase procedure" refers to the series of actions taken to select and purchase a product in online shopping, etc.

[0346] "Location information" refers to data about a user's current location, which is obtained using technologies such as GPS.

[0347] "Means of conducting purchase procedures via a communication network" refers to a system that uses networks such as the internet to purchase necessary food items online.

[0348] This invention is a system that supports users' health management, and in particular aims to provide meal suggestions that take into account the user's emotional state. This system operates in conjunction with a complex software program installed on a cloud server and an application on the user's terminal.

[0349] The user terminal uses health-related applications to collect daily biometric data such as weight, diet, exercise levels, and sleep duration. The terminal also features emotion recognition capabilities, allowing it to detect the user's psychological state using text and voice input, as well as sensors built into the device. Users can manually input this data or have it automatically acquired using the sensors.

[0350] The server receives collected biometric and psychological data and stores it in a database. The server's built-in emotion engine analyzes the emotional data and generates a user psychological profile. This profile is integrated with health-related information and used to create personalized dietary recommendations based on the user's nutritional needs and psychological state.

[0351] For example, if a user is experiencing stress, the server generates a meal plan incorporating stress-relieving ingredients. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online marketplace database. Users can then purchase the suggested ingredients online with just a few clicks.

[0352] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their psychological state. For example, if a user needs a change of pace, it can guide them to a cafe or restaurant suitable for regaining their energy.

[0353] Examples of prompts to input into the generating AI model include, "Create the optimal meal plan for when the user is feeling stressed," and "Suggest restaurants that match the user's current emotional state." In this way, more sophisticated and adaptive health management and meal suggestions that take into account the user's psychological state in daily life can be realized.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user's device uses health-related applications to collect daily biometric data such as weight, diet, exercise level, and sleep duration. Input includes data manually entered by the user and data automatically collected from the device's sensors. This data is converted into an internal data format and prepared for subsequent processing. Specifically, when a user takes a photo of their meal and registers the information in the app, the nutritional data of the food is automatically recorded.

[0357] Step 2:

[0358] The user terminal uses emotion recognition to detect the user's psychological state based on text, voice data, and data acquired from built-in sensors. Inputs include the user's emotional diary and voice memos, which are analyzed to quantify the emotional state and store as part of the psychological profile. Specifically, speech recognition technology is used to analyze keywords in conversations and measure stress levels.

[0359] Step 3:

[0360] The server receives biometric and psychological state data transmitted from user terminals and stores them in a database. The input is a data stream from the end-user terminal, and the output generates a record of daily health and emotional states in the integrated database. Specifically, data is uploaded in real time and stored in server storage for analysis.

[0361] Step 4:

[0362] The server uses an emotion engine to analyze psychological data and generate a user's psychological profile. It takes past and current emotional data as input and uses this data to computationally detect the user's individual emotional tendencies. The output includes user characteristics such as "tendency to show high stress levels on weekends." The emotion engine combines natural language processing and machine learning to create the profile.

[0363] Step 5:

[0364] The server analyzes the user's nutritional needs based on their psychological profile and biometric data, and generates customized meal suggestions. It uses integrated user data as input to calculate the optimal balance of nutrients. The output is a nutrition plan tailored to the user's emotions and health status. Specific operations include selecting food groups using a nutrition algorithm.

[0365] Step 6:

[0366] The server configures the system to allow users to purchase necessary ingredients via the network based on the generated meal suggestions. The output connects to the online supermarket's database and provides users with links to purchase items. For example, users can easily proceed with the purchase process by simply pressing the "Add to Cart" button on the app.

[0367] Step 7:

[0368] The server obtains the user's current location information and identifies stores and restaurants that match their psychological state. The input is the user's real-time location information, and the system searches a store database based on the generated psychological profile, providing recommended store information as output. Specifically, this includes displaying nearby relaxing cafes on a map.

[0369] (Application Example 2)

[0370] 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."

[0371] Conventional health management systems lack the ability to support users' mental health by providing meal suggestions that take into account the user's emotional state or identifying appropriate dining facilities. Therefore, there is a need for more sophisticated and personalized services based on the user's emotions and health data.

[0372] 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.

[0373] In this invention, the server includes means for collecting user health-related information, means for detecting the user's emotional state, and means for suggesting foods that promote relaxation and mood improvement based on the detected emotional state. This enables adaptive, personalized meal suggestions and identification of dining establishments that take the user's mental health into consideration.

[0374] "Means of collecting user health-related information" refers to functions that acquire health-related data such as weight, diet, exercise levels, and sleep duration from the user's device.

[0375] A "means for analyzing nutritional needs" is a mechanism that analyzes the nutrients and dietary content required by users based on collected health-related information, and identifies specific needs.

[0376] A "means for generating meal suggestions" is a system that creates meal menus suitable for the user based on analyzed nutritional needs and emotional state.

[0377] "Means of conducting purchase procedures via a communication network" refers to the process of using a network to place an order for the proposed ingredients to be purchased online.

[0378] "Means for detecting a user's emotional state" refers to technologies that recognize and classify a user's emotions using text, voice, or sensors.

[0379] "A means of suggesting ingredients that promote relaxation and mood improvement" refers to a system that takes into account the user's current emotional state and selects and suggests ingredients that contribute to stress reduction and mood improvement.

[0380] "Means for identifying appropriate dining establishments or offices" refers to a function that searches for and selects suitable facilities from a relevant database based on the user's location information and emotional state.

[0381] This invention relates to a system that supports user health management and provides meal suggestions that take into account the user's emotional state. The server receives health-related information and emotional state data transmitted from the user terminal and analyzes it. The specific program functions as follows:

[0382] First, the user's device collects health-related information. This is done by inputting data such as weight, diet, exercise levels, and sleep duration via a dedicated application or by automatically acquiring it through sensors. The device also uses emotion recognition technology to analyze the user's emotions from text input, voice, and sensor data. Emotion recognition software such as Google Cloud AI and IBM Watson can be used for emotion analysis.

[0383] The server stores this data in a database on Amazon Web Services (AWS) and then analyzes the user's nutritional needs. This analysis uses Python to integrate health and emotional data and generates personalized meal suggestions using a generative AI model.

[0384] The suggested meals include information on the necessary ingredients, which is transmitted via a communication network to online grocery sales services. This allows users to easily purchase the recommended ingredients.

[0385] Furthermore, based on the user's emotional state, food and beverage options related to disaster prevention and stress reduction are suggested. The server utilizes the user's location information to search for the most suitable restaurants or offices from a relevant database and guide the user there.

[0386] For example, if a user tells the robot, "I'm feeling a bit down today," the server will analyze that emotion, play mood-enhancing music, suggest a chocolate dessert recipe, and, if necessary, automatically order the ingredients.

[0387] An example of a prompt message for the generating AI model would be: "Please suggest an energy-boosting meal plan recommended for the afternoon when the user is feeling tired."

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user's device collects health-related information. Users manually input data such as weight, diet, exercise levels, and sleep duration using an application, or the device's sensors automatically acquire this information. As a result, health-related data is input, and a health information file containing that data is output.

[0391] Step 2:

[0392] The user's device performs emotion recognition. It passes the user's text input or voice to emotion recognition software for analysis of their emotional state. The input is text or voice data, and the output is an emotion profile indicating the user's emotional state. This process utilizes emotion analysis APIs such as Google Cloud AI.

[0393] Step 3:

[0394] The server receives health information files and emotional profiles sent from the user's terminal. The input data includes health information and emotional profiles, which the server stores in a database on AWS. The output is the identification information for the stored data.

[0395] Step 4:

[0396] The server analyzes health information and emotional profiles to identify the user's nutritional needs. Using Python scripts, it integrates and analyzes the data, and a generated AI model suggests appropriate nutrients and meal plans based on the input data. The output is a meal plan optimized for the user.

[0397] Step 5:

[0398] The server processes the purchase of ingredients online via the communication network based on the generated meal plan. The input is the ingredient information specified in the meal plan, and the output is the purchase confirmation information from the online store.

[0399] Step 6:

[0400] The server uses the user's emotional state and location information to search for the most suitable restaurants and offices. It searches relevant databases, receiving location information and emotional status data as input, and outputs a list of recommended establishments.

[0401] Step 7:

[0402] Users can choose actions based on information provided by the server. They execute their actual meal plans by purchasing suggested ingredients or visiting recommended restaurants. The input is suggestions from the server, and the output is a log of the user's actions.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

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

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] This invention is a system that supports users' health management, aiming to collect and analyze a large amount of health-related data and provide personalized dietary suggestions. This system has a software program running on a cloud server at its core and operates in conjunction with the user's device.

[0420] User devices input daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. This data is transmitted to a cloud server using secure communication methods. The system automatically aggregates daily health information simply by the user entering basic data.

[0421] The server stores the user's health-related information and uses a generative AI model to perform analysis. The analysis diagnoses nutritional needs based on the user's health status and lifestyle, and creates a personalized nutrition plan. Based on the results, it generates and presents appropriate meal suggestions to the user.

[0422] Furthermore, the server accesses the online supermarket database to select the necessary ingredients for meal suggestions. Users can easily purchase the required ingredients online. The server also utilizes the user's real-time location information to search for nearby restaurants and shops, providing information on suitable restaurants in the vicinity.

[0423] For example, if a user requests a low-carb meal at lunchtime, the server will use past data to assess the user's health and nutritional status and suggest suitable menu options. It can also use GPS information to guide users to nearby restaurants offering low-carb menus.

[0424] In this way, the present invention functions as a personalized health management and dietary suggestion system that users can use on a daily basis.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The user terminal collects health data such as weight, diet, exercise level, and sleep duration entered by the user through health-related applications. Users can manually enter data or the device can automatically retrieve information in conjunction with the user.

[0428] Step 2:

[0429] The device periodically sends collected health data to a cloud server. Data transmission uses encrypted communication protocols to protect user privacy.

[0430] Step 3:

[0431] The server stores the received health data in a database. All data is structured and securely managed.

[0432] Step 4:

[0433] The server uses a generative AI model to analyze stored user health data. This model assesses the user's health status and nutritional needs by comparing them with past data.

[0434] Step 5:

[0435] The server creates a personalized nutrition plan for the user based on the analysis results. This plan includes recommended foods and meal menus for the user.

[0436] Step 6:

[0437] The server accesses the online supermarket's database and identifies the ingredients needed for the created nutrition plan. Users can then select and purchase these ingredients online.

[0438] Step 7:

[0439] The server obtains the user's current location using GPS and searches for suitable nearby restaurants and shops. Based on the identified shop information, it guides the user to easily visit them.

[0440] Step 8:

[0441] The server sends the generated nutrition plan and store information to the user's terminal and presents it to the user as appropriate meal suggestions. Based on the information received, the user then works to improve their daily eating habits.

[0442] (Example 1)

[0443] 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."

[0444] In today's busy lifestyle, selecting and implementing an optimal diet based on individual health conditions and nutritional needs is difficult for many people. Furthermore, the process of selecting and efficiently purchasing the right ingredients is complex. Additionally, if meal suggestions do not address individual circumstances, user satisfaction may suffer.

[0445] 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.

[0446] In this invention, the server includes a device for collecting the user's health-related information, a device for analyzing the user's nutritional needs using a generative AI model, a device for generating meal suggestions using prompt sentences, a device for selecting necessary ingredients and performing purchase procedures via a communication network, and a device for identifying relevant facilities using the user's current location information. This enables personalized nutritional suggestions, rapid ingredient purchases, and identification of optimal relevant facilities.

[0447] "User health-related information" refers to various health-related data such as the user's weight, diet, exercise level, and sleep duration.

[0448] A "cloud environment" is an environment that utilizes computing resources and storage capacity provided via the internet to store and process data.

[0449] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to perform analysis and provide insights into a user's health status and nutritional needs.

[0450] A "prompt statement" is an input statement used to give instructions to a generative AI model and obtain a specific output.

[0451] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's nutritional needs.

[0452] A "communication network" refers to the network infrastructure used to send and receive data.

[0453] "Related facilities" refer to stores and facilities that provide food and services suitable for the user, identified based on the user's current location information.

[0454] This invention is a system that efficiently supports users' health management. This system is centered around a software program that runs on the cloud and operates in conjunction with the user's device. Users can input health-related information daily using a device with a health management application installed. Specifically, this includes information such as weight, diet, exercise levels, and sleep duration.

[0455] The device transmits this health-related information to a cloud server via a secure communication method (e.g., an encrypted internet connection). The information is encrypted during this process to protect the user's data privacy. The server stores the received information in a dedicated database in preparation for subsequent analysis.

[0456] The server performs analysis using a generated AI model based on the stored data. This AI model has the ability to diagnose personalized nutritional needs by taking into account the user's past health data and lifestyle. For example, a prompt such as "Please suggest an optimal low-calorie, nutritionally balanced dinner plan based on the user's current lifestyle and health status" is used as an instruction to the AI ​​model. Based on this prompt, the AI ​​generates specific meal suggestions.

[0457] Furthermore, the server collaborates with food supply services on the network to select the necessary ingredients for the suggested meal. Users can then easily purchase the necessary items online using the generated ingredient list. The server also has the ability to use the user's real-time location information to search for nearby restaurants and food stores and present appropriate options to the user. For example, if a user requests a low-carb menu, the server can guide them to nearby restaurants.

[0458] This allows users to take an interest in their own health and smoothly manage their health in their daily lives.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] Users input daily health-related information, such as weight, diet, exercise levels, and sleep duration, into their devices using a health management application. The input data is organized within the application, and its integrity is checked. As output, health-related information that has been verified for integrity is generated.

[0462] Step 2:

[0463] The device sends verified health information to the cloud server using a secure protocol (e.g., HTTPS). During this process, the device encrypts the data to protect user privacy. The output is an encrypted health data packet.

[0464] Step 3:

[0465] The server decrypts the received encrypted data and stores it in the database. During storage, the data is indexed to improve searchability. The output is a grouped health information record stored in the database.

[0466] Step 4:

[0467] The server starts an analysis using a generative AI model based on health information stored in the database. Specifically, it uses prompts to assess the user's health status and diagnoses their individualized nutritional needs. The output is the nutritional needs and diagnosis results based on the analysis.

[0468] Step 5:

[0469] The server automatically generates specific meal suggestions based on the nutritional needs assessment. The generated meal suggestions are created using an algorithm that selects the optimal menu from a large number of options. The output is a user-optimized list of meal suggestions.

[0470] Step 6:

[0471] Based on the generated meal suggestions, the server accesses a database of food supply services on the network and selects the necessary ingredients. The selected ingredient information considers the most economical and nutritious choices for the user. The output is a list of the required ingredients.

[0472] Step 7:

[0473] The server uses the user's location services to identify the nearest suitable restaurants and establishments. Prioritizing establishments that offer the suggested meal options, the server provides a list of nearby relevant establishments.

[0474] Step 8:

[0475] Users receive meal suggestions and facility information on their devices, and can purchase ingredients online or check information for visiting stores. The output consists of options for actions that help users manage their health.

[0476] (Application Example 1)

[0477] 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."

[0478] In modern society, personalized health management tailored to individual lifestyles and dietary recommendations based on those lifestyles are crucial. However, many systems lack sufficient data collection and analysis capabilities, making it difficult to provide personalized dietary recommendations. Furthermore, the cumbersome process of actually obtaining food based on these recommendations is another challenge. Additionally, the system fails to accurately identify appropriate dining facilities based on the user's current location, resulting in a less-than-ideal user experience.

[0479] 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.

[0480] In this invention, the server includes means for collecting user health-related information, means for analyzing the user's nutritional needs based on said information, means for generating meal suggestions corresponding to the analyzed needs, means for executing food delivery procedures based on the generated meal suggestions, and means for identifying appropriate dining establishments using the user's current location information. This enables personalized health management and meal suggestions, as well as easy food retrieval based on them. It also enables guidance to appropriate dining establishments according to the user's current location, improving the user experience.

[0481] "User health-related information" refers to data that indicates the user's health status in their daily life, and specifically includes weight, diet, exercise level, and sleep duration.

[0482] "Means of analyzing nutritional needs" refer to technologies and methods for analyzing the nutrients and dietary content that users need based on collected health-related information, and for providing appropriate dietary suggestions.

[0483] "Methods for generating meal suggestions" refer to technologies and methods for creating and providing specific content tailored to the analyzed nutritional needs of users.

[0484] "Means for carrying out food delivery procedures" refers to technologies and methods for carrying out procedures to deliver suggested food items to users based on generated meal suggestions.

[0485] "Methods for identifying appropriate dining establishments using current location information" refers to technologies and methods that search for nearby restaurants and other establishments based on the user's current location, and identify facilities that meet the user's needs.

[0486] To realize this invention, a dedicated application is installed on the user's device to collect health-related information. The collected data is then securely transmitted to a cloud server. The cloud server receives various health data and uses a generative AI model to analyze the user's nutritional needs in detail. This generative AI model is built using a solution such as PyTorch and operates based on the Python language.

[0487] The server generates personalized meal suggestions based on the analysis results and sends the information to the user's device. It also collaborates with a food delivery network to arrange for the delivery of necessary food items based on the generated meal suggestions. This allows users to easily obtain the ingredients for their suggested meals online.

[0488] Furthermore, the server uses the user's current location information to identify and provide the most suitable dining establishments based on the user's health needs. This process utilizes advanced location services to pinpoint nearby dining establishments.

[0489] As a concrete example of its use, when a user enters their weight and exercise level from the previous day into the app in the morning, the server creates an optimal nutrition plan through a generative AI model. For example, it might suggest, "You're lacking protein, so we recommend a high-protein recipe for lunch today." Furthermore, based on that suggestion, it will guide the user to high-protein menus offered at nearby restaurants, and if the user wishes, they can order those menus through a food delivery service.

[0490] An example of a prompt to input into the generating AI model is, "Based on recent exercise and dietary data, please create a suggestion for the best late-night snack."

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] Users input health-related information into their devices. Specifically, users enter daily health data such as weight, diet, exercise levels, and sleep duration into a dedicated application. This information is then transmitted from the device to a cloud server via secure communication.

[0494] Step 2:

[0495] The server stores received health-related information and analyzes it using a generative AI model. The input is the user's health data, and the model uses this to diagnose the individual's nutritional needs. Data processing includes comparison with the user's past records and analysis using statistical methods. The output is a nutrition plan based on the analysis.

[0496] Step 3:

[0497] The server generates personalized meal suggestions based on the analysis results. A prompt message is sent to the generating AI model: "Create meal suggestions to compensate for the user's exercise level and nutritional deficiencies," and the suggested menu is retrieved. The input for this step is the output of step 2, and the output is a specific meal menu.

[0498] Step 4:

[0499] The server prepares to execute food delivery procedures based on the generated meal suggestions. The input is the meal suggestions generated in step 3. The server identifies the necessary ingredients for the suggested menu and integrates them with online sales services to make them available for order. The output is the relevant information for the delivery procedures.

[0500] Step 5:

[0501] Based on the user's current location, the system identifies suitable restaurants and bars. The server receives the location information, queries the relevant database, and recommends a suitable establishment for the user. The input for this step is the user's current location, and the output is a list of recommended restaurants and bars.

[0502] Step 6:

[0503] The terminal displays meal suggestions, food delivery information, and restaurant information sent from the server to the user. The user can select a suggested menu item and proceed directly to the ordering process. The input is the output of steps 4 and 5, and the output is the information presented to the user.

[0504] 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.

[0505] This invention is a system that supports users' health management, and is particularly specialized in providing meal suggestions that take into account the user's emotional state. This system operates through the collaboration of a complex software program installed on a cloud server and an application on the user's terminal.

[0506] The user's device collects daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. In addition, the device is equipped with emotion recognition capabilities, detecting emotional states using user-input text, voice, and the device's sensors. Users can manually input this data, or the information can be automatically acquired using the sensors.

[0507] The server receives collected health and emotional state data and stores it in a database. The server's built-in emotion engine analyzes the received emotional state data and generates a user emotional profile. This profile is integrated with health data to contribute to creating personalized meal suggestions based on the user's nutritional needs and emotions.

[0508] For example, if a user is feeling stressed, the server generates a meal plan that incorporates ingredients to help with relaxation. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online supermarket database. The user can then purchase the suggested ingredients online with just a few clicks.

[0509] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their emotional state. For example, if a user wants a change of pace, it can recommend cafes and restaurants that are perfect for refreshing themselves.

[0510] This makes it possible to realize more precise and adaptive health management and dietary suggestions that take into account the user's emotional state in their daily life.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] The user's device collects health data such as weight, diet, exercise levels, and sleep duration entered by the user through health-related applications. Furthermore, it automatically recognizes the user's emotions from text and voice data using the device's camera and microphone.

[0514] Step 2:

[0515] The device transmits collected health and emotional data to a cloud server using an encrypted communication protocol. Real-time data transmission is desirable.

[0516] Step 3:

[0517] The server stores the received health and emotional data in a database. Based on the received emotional data, the emotion engine generates an emotional profile.

[0518] Step 4:

[0519] The server analyzes health data and emotional profiles to generate a customized meal plan based on the user's nutritional needs and current emotional state. For example, it might recommend foods rich in B vitamins to a user who is feeling down.

[0520] Step 5:

[0521] The server identifies the necessary ingredients for the generated meal plan from the online supermarket database and provides the user with online purchasing options. The user selects the ingredients suggested by the server and proceeds with the purchase.

[0522] Step 6:

[0523] The server obtains the user's current location via GPS and searches its database for stores and restaurants that match the user's emotional state. For example, if the user is looking to relax, it will recommend a restaurant that uses natural ingredients.

[0524] Step 7:

[0525] The server sends customized meal plans, information on available ingredients, and store locations to the user's terminal and displays these suggestions to the user. Based on the information provided, the user makes emotionally responsive and healthy choices in their daily life.

[0526] (Example 2)

[0527] 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."

[0528] In modern society, managing users' health is a crucial issue. However, conventional health management systems rarely provide nutritional suggestions that adequately consider the user's emotional state. As a result, it is difficult to mitigate the impact of emotions such as stress on health. Therefore, there is a need to provide more precise and individualized health management and dietary suggestions that take emotional states into account.

[0529] 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.

[0530] In this invention, the server includes means for collecting the user's biometric data and psychological state, means for generating the user's psychological profile based on said information, and means for analyzing the user's nutritional needs based on said profile and health-related information. This enables personalized nutritional suggestions that take into account the user's emotional state.

[0531] "User biometric data" refers to information about the user's physical health, such as weight, diet, exercise level, and sleep duration.

[0532] "Psychological state" refers to information that indicates the user's mental state, such as their emotions and stress levels.

[0533] A "psychological profile" is a collection of data that analyzes a user's emotional state and captures its characteristics.

[0534] "Health-related information" refers to a set of data related to the user's physical and mental health status.

[0535] "Nutritional needs" refer to the user's requirements for nutrients necessary to maintain or improve their health.

[0536] "Meal suggestions" refer to meal plans and recommendations provided based on the user's nutritional needs and psychological state.

[0537] A "communication network" refers to network infrastructure used for exchanging information, such as the internet.

[0538] "Purchase procedure" refers to the series of actions taken to select and purchase a product in online shopping, etc.

[0539] "Location information" refers to data about a user's current location, which is obtained using technologies such as GPS.

[0540] "Means of conducting purchase procedures via a communication network" refers to a system that uses networks such as the internet to purchase necessary food items online.

[0541] This invention is a system that supports users' health management, and in particular aims to provide meal suggestions that take into account the user's emotional state. This system operates in conjunction with a complex software program installed on a cloud server and an application on the user's terminal.

[0542] The user terminal uses health-related applications to collect daily biometric data such as weight, diet, exercise levels, and sleep duration. The terminal also features emotion recognition capabilities, allowing it to detect the user's psychological state using text and voice input, as well as sensors built into the device. Users can manually input this data or have it automatically acquired using the sensors.

[0543] The server receives collected biometric and psychological data and stores it in a database. The server's built-in emotion engine analyzes the emotional data and generates a user psychological profile. This profile is integrated with health-related information and used to create personalized dietary recommendations based on the user's nutritional needs and psychological state.

[0544] For example, if a user is experiencing stress, the server generates a meal plan incorporating stress-relieving ingredients. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online marketplace database. Users can then purchase the suggested ingredients online with just a few clicks.

[0545] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their psychological state. For example, if a user needs a change of pace, it can guide them to a cafe or restaurant suitable for regaining their energy.

[0546] Examples of prompts to input into the generating AI model include, "Create the optimal meal plan for when the user is feeling stressed," and "Suggest restaurants that match the user's current emotional state." In this way, more sophisticated and adaptive health management and meal suggestions that take into account the user's psychological state in daily life can be realized.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The user's device uses health-related applications to collect daily biometric data such as weight, diet, exercise level, and sleep duration. Input includes data manually entered by the user and data automatically collected from the device's sensors. This data is converted into an internal data format and prepared for subsequent processing. Specifically, when a user takes a photo of their meal and registers the information in the app, the nutritional data of the food is automatically recorded.

[0550] Step 2:

[0551] The user terminal uses emotion recognition to detect the user's psychological state based on text, voice data, and data acquired from built-in sensors. Inputs include the user's emotional diary and voice memos, which are analyzed to quantify the emotional state and store as part of the psychological profile. Specifically, speech recognition technology is used to analyze keywords in conversations and measure stress levels.

[0552] Step 3:

[0553] The server receives biometric and psychological state data transmitted from user terminals and stores them in a database. The input is a data stream from the end-user terminal, and the output generates a record of daily health and emotional states in the integrated database. Specifically, data is uploaded in real time and stored in server storage for analysis.

[0554] Step 4:

[0555] The server uses an emotion engine to analyze psychological data and generate a user's psychological profile. It takes past and current emotional data as input and uses this data to computationally detect the user's individual emotional tendencies. The output includes user characteristics such as "tendency to show high stress levels on weekends." The emotion engine combines natural language processing and machine learning to create the profile.

[0556] Step 5:

[0557] The server analyzes the user's nutritional needs based on their psychological profile and biometric data, and generates customized meal suggestions. It uses integrated user data as input to calculate the optimal balance of nutrients. The output is a nutrition plan tailored to the user's emotions and health status. Specific operations include selecting food groups using a nutrition algorithm.

[0558] Step 6:

[0559] The server configures the system to allow users to purchase necessary ingredients via the network based on the generated meal suggestions. The output connects to the online supermarket's database and provides users with links to purchase items. For example, users can easily proceed with the purchase process by simply pressing the "Add to Cart" button on the app.

[0560] Step 7:

[0561] The server obtains the user's current location information and identifies stores and restaurants that match their psychological state. The input is the user's real-time location information, and the system searches a store database based on the generated psychological profile, providing recommended store information as output. Specifically, this includes displaying nearby relaxing cafes on a map.

[0562] (Application Example 2)

[0563] 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."

[0564] Conventional health management systems lack the ability to support users' mental health by providing meal suggestions that take into account the user's emotional state or identifying appropriate dining facilities. Therefore, there is a need for more sophisticated and personalized services based on the user's emotions and health data.

[0565] 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.

[0566] In this invention, the server includes means for collecting user health-related information, means for detecting the user's emotional state, and means for suggesting foods that promote relaxation and mood improvement based on the detected emotional state. This enables adaptive, personalized meal suggestions and identification of dining establishments that take the user's mental health into consideration.

[0567] "Means of collecting user health-related information" refers to functions that acquire health-related data such as weight, diet, exercise levels, and sleep duration from the user's device.

[0568] A "means for analyzing nutritional needs" is a mechanism that analyzes the nutrients and dietary content required by users based on collected health-related information, and identifies specific needs.

[0569] A "means for generating meal suggestions" is a system that creates meal menus suitable for the user based on analyzed nutritional needs and emotional state.

[0570] "Means of conducting purchase procedures via a communication network" refers to the process of using a network to place an order for the proposed ingredients to be purchased online.

[0571] "Means for detecting a user's emotional state" refers to technologies that recognize and classify a user's emotions using text, voice, or sensors.

[0572] "A means of suggesting ingredients that promote relaxation and mood improvement" refers to a system that takes into account the user's current emotional state and selects and suggests ingredients that contribute to stress reduction and mood improvement.

[0573] "Means for identifying appropriate dining establishments or offices" refers to a function that searches for and selects suitable facilities from a relevant database based on the user's location information and emotional state.

[0574] This invention relates to a system that supports user health management and provides meal suggestions that take into account the user's emotional state. The server receives health-related information and emotional state data transmitted from the user terminal and analyzes it. The specific program functions as follows:

[0575] First, the user's device collects health-related information. This is done by inputting data such as weight, diet, exercise levels, and sleep duration via a dedicated application or by automatically acquiring it through sensors. The device also uses emotion recognition technology to analyze the user's emotions from text input, voice, and sensor data. Emotion recognition software such as Google Cloud AI and IBM Watson can be used for emotion analysis.

[0576] The server stores this data in a database on Amazon Web Services (AWS) and then analyzes the user's nutritional needs. This analysis uses Python to integrate health and emotional data and generates personalized meal suggestions using a generative AI model.

[0577] The suggested meals include information on the necessary ingredients, which is transmitted via a communication network to online grocery sales services. This allows users to easily purchase the recommended ingredients.

[0578] Furthermore, based on the user's emotional state, food and beverage options related to disaster prevention and stress reduction are suggested. The server utilizes the user's location information to search for the most suitable restaurants or offices from a relevant database and guide the user there.

[0579] For example, if a user tells the robot, "I'm feeling a bit down today," the server will analyze that emotion, play mood-enhancing music, suggest a chocolate dessert recipe, and, if necessary, automatically order the ingredients.

[0580] An example of a prompt message for the generating AI model would be: "Please suggest an energy-boosting meal plan recommended for the afternoon when the user is feeling tired."

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The user's device collects health-related information. Users manually input data such as weight, diet, exercise levels, and sleep duration using an application, or the device's sensors automatically acquire this information. As a result, health-related data is input, and a health information file containing that data is output.

[0584] Step 2:

[0585] The user's device performs emotion recognition. It passes the user's text input or voice to emotion recognition software for analysis of their emotional state. The input is text or voice data, and the output is an emotion profile indicating the user's emotional state. This process utilizes emotion analysis APIs such as Google Cloud AI.

[0586] Step 3:

[0587] The server receives health information files and emotional profiles sent from the user's terminal. The input data includes health information and emotional profiles, which the server stores in a database on AWS. The output is the identification information for the stored data.

[0588] Step 4:

[0589] The server analyzes health information and emotional profiles to identify the user's nutritional needs. Using Python scripts, it integrates and analyzes the data, and a generated AI model suggests appropriate nutrients and meal plans based on the input data. The output is a meal plan optimized for the user.

[0590] Step 5:

[0591] The server processes the purchase of ingredients online via the communication network based on the generated meal plan. The input is the ingredient information specified in the meal plan, and the output is the purchase confirmation information from the online store.

[0592] Step 6:

[0593] The server uses the user's emotional state and location information to search for the most suitable restaurants and offices. It searches relevant databases, receiving location information and emotional status data as input, and outputs a list of recommended establishments.

[0594] Step 7:

[0595] Users can choose actions based on information provided by the server. They execute their actual meal plans by purchasing suggested ingredients or visiting recommended restaurants. The input is suggestions from the server, and the output is a log of the user's actions.

[0596] 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.

[0597] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0598] 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.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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".

[0613] This invention is a system that supports users' health management, aiming to collect and analyze a large amount of health-related data and provide personalized dietary suggestions. This system has a software program running on a cloud server at its core and operates in conjunction with the user's device.

[0614] User devices input daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. This data is transmitted to a cloud server using secure communication methods. The system automatically aggregates daily health information simply by the user entering basic data.

[0615] The server stores the user's health-related information and uses a generative AI model to perform analysis. The analysis diagnoses nutritional needs based on the user's health status and lifestyle, and creates a personalized nutrition plan. Based on the results, it generates and presents appropriate meal suggestions to the user.

[0616] Furthermore, the server accesses the online supermarket database to select the necessary ingredients for meal suggestions. Users can easily purchase the required ingredients online. The server also utilizes the user's real-time location information to search for nearby restaurants and shops, providing information on suitable restaurants in the vicinity.

[0617] For example, if a user requests a low-carb meal at lunchtime, the server will use past data to assess the user's health and nutritional status and suggest suitable menu options. It can also use GPS information to guide users to nearby restaurants offering low-carb menus.

[0618] In this way, the present invention functions as a personalized health management and dietary suggestion system that users can use on a daily basis.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The user terminal collects health data such as weight, diet, exercise level, and sleep duration entered by the user through health-related applications. Users can manually enter data or the device can automatically retrieve information in conjunction with the user.

[0622] Step 2:

[0623] The device periodically sends collected health data to a cloud server. Data transmission uses encrypted communication protocols to protect user privacy.

[0624] Step 3:

[0625] The server stores the received health data in a database. All data is structured and securely managed.

[0626] Step 4:

[0627] The server uses a generative AI model to analyze stored user health data. This model assesses the user's health status and nutritional needs by comparing them with past data.

[0628] Step 5:

[0629] The server creates a personalized nutrition plan for the user based on the analysis results. This plan includes recommended foods and meal menus for the user.

[0630] Step 6:

[0631] The server accesses the online supermarket's database and identifies the ingredients needed for the created nutrition plan. Users can then select and purchase these ingredients online.

[0632] Step 7:

[0633] The server obtains the user's current location using GPS and searches for suitable nearby restaurants and shops. Based on the identified shop information, it guides the user to easily visit them.

[0634] Step 8:

[0635] The server sends the generated nutrition plan and store information to the user's terminal and presents it to the user as appropriate meal suggestions. Based on the information received, the user then works to improve their daily eating habits.

[0636] (Example 1)

[0637] 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".

[0638] In today's busy lifestyle, selecting and implementing an optimal diet based on individual health conditions and nutritional needs is difficult for many people. Furthermore, the process of selecting and efficiently purchasing the right ingredients is complex. Additionally, if meal suggestions do not address individual circumstances, user satisfaction may suffer.

[0639] 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.

[0640] In this invention, the server includes a device for collecting the user's health-related information, a device for analyzing the user's nutritional needs using a generative AI model, a device for generating meal suggestions using prompt sentences, a device for selecting necessary ingredients and performing purchase procedures via a communication network, and a device for identifying relevant facilities using the user's current location information. This enables personalized nutritional suggestions, rapid ingredient purchases, and identification of optimal relevant facilities.

[0641] "User health-related information" refers to various health-related data such as the user's weight, diet, exercise level, and sleep duration.

[0642] A "cloud environment" is an environment that utilizes computing resources and storage capacity provided via the internet to store and process data.

[0643] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to perform analysis and provide insights into a user's health status and nutritional needs.

[0644] A "prompt statement" is an input statement used to give instructions to a generative AI model and obtain a specific output.

[0645] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's nutritional needs.

[0646] A "communication network" refers to the network infrastructure used to send and receive data.

[0647] "Related facilities" refer to stores and facilities that provide food and services suitable for the user, identified based on the user's current location information.

[0648] This invention is a system that efficiently supports users' health management. This system is centered around a software program that runs on the cloud and operates in conjunction with the user's device. Users can input health-related information daily using a device with a health management application installed. Specifically, this includes information such as weight, diet, exercise levels, and sleep duration.

[0649] The device transmits this health-related information to a cloud server via a secure communication method (e.g., an encrypted internet connection). The information is encrypted during this process to protect the user's data privacy. The server stores the received information in a dedicated database in preparation for subsequent analysis.

[0650] The server performs analysis using a generated AI model based on the stored data. This AI model has the ability to diagnose personalized nutritional needs by taking into account the user's past health data and lifestyle. For example, a prompt such as "Please suggest an optimal low-calorie, nutritionally balanced dinner plan based on the user's current lifestyle and health status" is used as an instruction to the AI ​​model. Based on this prompt, the AI ​​generates specific meal suggestions.

[0651] Furthermore, the server collaborates with food supply services on the network to select the necessary ingredients for the suggested meal. Users can then easily purchase the necessary items online using the generated ingredient list. The server also has the ability to use the user's real-time location information to search for nearby restaurants and food stores and present appropriate options to the user. For example, if a user requests a low-carb menu, the server can guide them to nearby restaurants.

[0652] This allows users to take an interest in their own health and smoothly manage their health in their daily lives.

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

[0654] Step 1:

[0655] Users input daily health-related information, such as weight, diet, exercise levels, and sleep duration, into their devices using a health management application. The input data is organized within the application, and its integrity is checked. As output, health-related information that has been verified for integrity is generated.

[0656] Step 2:

[0657] The device sends verified health information to the cloud server using a secure protocol (e.g., HTTPS). During this process, the device encrypts the data to protect user privacy. The output is an encrypted health data packet.

[0658] Step 3:

[0659] The server decrypts the received encrypted data and stores it in the database. During storage, the data is indexed to improve searchability. The output is a grouped health information record stored in the database.

[0660] Step 4:

[0661] The server starts an analysis using a generative AI model based on health information stored in the database. Specifically, it uses prompts to assess the user's health status and diagnoses their individualized nutritional needs. The output is the nutritional needs and diagnosis results based on the analysis.

[0662] Step 5:

[0663] The server automatically generates specific meal suggestions based on the nutritional needs assessment. The generated meal suggestions are created using an algorithm that selects the optimal menu from a large number of options. The output is a user-optimized list of meal suggestions.

[0664] Step 6:

[0665] Based on the generated meal suggestions, the server accesses a database of food supply services on the network and selects the necessary ingredients. The selected ingredient information considers the most economical and nutritious choices for the user. The output is a list of the required ingredients.

[0666] Step 7:

[0667] The server uses the user's location services to identify the nearest suitable restaurants and establishments. Prioritizing establishments that offer the suggested meal options, the server provides a list of nearby relevant establishments.

[0668] Step 8:

[0669] Users receive meal suggestions and facility information on their devices, and can purchase ingredients online or check information for visiting stores. The output consists of options for actions that help users manage their health.

[0670] (Application Example 1)

[0671] 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".

[0672] In modern society, personalized health management tailored to individual lifestyles and dietary recommendations based on those lifestyles are crucial. However, many systems lack sufficient data collection and analysis capabilities, making it difficult to provide personalized dietary recommendations. Furthermore, the cumbersome process of actually obtaining food based on these recommendations is another challenge. Additionally, the system fails to accurately identify appropriate dining facilities based on the user's current location, resulting in a less-than-ideal user experience.

[0673] 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.

[0674] In this invention, the server includes means for collecting user health-related information, means for analyzing the user's nutritional needs based on said information, means for generating meal suggestions corresponding to the analyzed needs, means for executing food delivery procedures based on the generated meal suggestions, and means for identifying appropriate dining establishments using the user's current location information. This enables personalized health management and meal suggestions, as well as easy food retrieval based on them. It also enables guidance to appropriate dining establishments according to the user's current location, improving the user experience.

[0675] "User health-related information" refers to data that indicates the user's health status in their daily life, and specifically includes weight, diet, exercise level, and sleep duration.

[0676] "Means of analyzing nutritional needs" refer to technologies and methods for analyzing the nutrients and dietary content that users need based on collected health-related information, and for providing appropriate dietary suggestions.

[0677] "Methods for generating meal suggestions" refer to technologies and methods for creating and providing specific content tailored to the analyzed nutritional needs of users.

[0678] "Means for carrying out food delivery procedures" refers to technologies and methods for carrying out procedures to deliver suggested food items to users based on generated meal suggestions.

[0679] "Methods for identifying appropriate dining establishments using current location information" refers to technologies and methods that search for nearby restaurants and other establishments based on the user's current location, and identify facilities that meet the user's needs.

[0680] To realize this invention, a dedicated application is installed on the user's device to collect health-related information. The collected data is then securely transmitted to a cloud server. The cloud server receives various health data and uses a generative AI model to analyze the user's nutritional needs in detail. This generative AI model is built using a solution such as PyTorch and operates based on the Python language.

[0681] The server generates personalized meal suggestions based on the analysis results and sends the information to the user's device. It also collaborates with a food delivery network to arrange for the delivery of necessary food items based on the generated meal suggestions. This allows users to easily obtain the ingredients for their suggested meals online.

[0682] Furthermore, the server uses the user's current location information to identify and provide the most suitable dining establishments based on the user's health needs. This process utilizes advanced location services to pinpoint nearby dining establishments.

[0683] As a concrete example of its use, when a user enters their weight and exercise level from the previous day into the app in the morning, the server creates an optimal nutrition plan through a generative AI model. For example, it might suggest, "You're lacking protein, so we recommend a high-protein recipe for lunch today." Furthermore, based on that suggestion, it will guide the user to high-protein menus offered at nearby restaurants, and if the user wishes, they can order those menus through a food delivery service.

[0684] An example of a prompt to input into the generating AI model is, "Based on recent exercise and dietary data, please create a suggestion for the best late-night snack."

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

[0686] Step 1:

[0687] Users input health-related information into their devices. Specifically, users enter daily health data such as weight, diet, exercise levels, and sleep duration into a dedicated application. This information is then transmitted from the device to a cloud server via secure communication.

[0688] Step 2:

[0689] The server stores received health-related information and analyzes it using a generative AI model. The input is the user's health data, and the model uses this to diagnose the individual's nutritional needs. Data processing includes comparison with the user's past records and analysis using statistical methods. The output is a nutrition plan based on the analysis.

[0690] Step 3:

[0691] The server generates personalized meal suggestions based on the analysis results. A prompt message is sent to the generating AI model: "Create meal suggestions to compensate for the user's exercise level and nutritional deficiencies," and the suggested menu is retrieved. The input for this step is the output of step 2, and the output is a specific meal menu.

[0692] Step 4:

[0693] The server prepares to execute food delivery procedures based on the generated meal suggestions. The input is the meal suggestions generated in step 3. The server identifies the necessary ingredients for the suggested menu and integrates them with online sales services to make them available for order. The output is the relevant information for the delivery procedures.

[0694] Step 5:

[0695] Based on the user's current location, the system identifies suitable restaurants and bars. The server receives the location information, queries the relevant database, and recommends a suitable establishment for the user. The input for this step is the user's current location, and the output is a list of recommended restaurants and bars.

[0696] Step 6:

[0697] The terminal displays meal suggestions, food delivery information, and restaurant information sent from the server to the user. The user can select a suggested menu item and proceed directly to the ordering process. The input is the output of steps 4 and 5, and the output is the information presented to the user.

[0698] 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.

[0699] This invention is a system that supports users' health management, and is particularly specialized in providing meal suggestions that take into account the user's emotional state. This system operates through the collaboration of a complex software program installed on a cloud server and an application on the user's terminal.

[0700] The user's device collects daily health data such as weight, diet, exercise levels, and sleep duration through health-related applications. In addition, the device is equipped with emotion recognition capabilities, detecting emotional states using user-input text, voice, and the device's sensors. Users can manually input this data, or the information can be automatically acquired using the sensors.

[0701] The server receives collected health and emotional state data and stores it in a database. The server's built-in emotion engine analyzes the received emotional state data and generates a user emotional profile. This profile is integrated with health data to contribute to creating personalized meal suggestions based on the user's nutritional needs and emotions.

[0702] For example, if a user is feeling stressed, the server generates a meal plan that incorporates ingredients to help with relaxation. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online supermarket database. The user can then purchase the suggested ingredients online with just a few clicks.

[0703] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their emotional state. For example, if a user wants a change of pace, it can recommend cafes and restaurants that are perfect for refreshing themselves.

[0704] This makes it possible to realize more precise and adaptive health management and dietary suggestions that take into account the user's emotional state in their daily life.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The user's device collects health data such as weight, diet, exercise levels, and sleep duration entered by the user through health-related applications. Furthermore, it automatically recognizes the user's emotions from text and voice data using the device's camera and microphone.

[0708] Step 2:

[0709] The device transmits collected health and emotional data to a cloud server using an encrypted communication protocol. Real-time data transmission is desirable.

[0710] Step 3:

[0711] The server stores the received health and emotional data in a database. Based on the received emotional data, the emotion engine generates an emotional profile.

[0712] Step 4:

[0713] The server analyzes health data and emotional profiles to generate a customized meal plan based on the user's nutritional needs and current emotional state. For example, it might recommend foods rich in B vitamins to a user who is feeling down.

[0714] Step 5:

[0715] The server identifies the necessary ingredients for the generated meal plan from the online supermarket database and provides the user with online purchasing options. The user selects the ingredients suggested by the server and proceeds with the purchase.

[0716] Step 6:

[0717] The server obtains the user's current location via GPS and searches its database for stores and restaurants that match the user's emotional state. For example, if the user is looking to relax, it will recommend a restaurant that uses natural ingredients.

[0718] Step 7:

[0719] The server sends customized meal plans, information on available ingredients, and store locations to the user's terminal and displays these suggestions to the user. Based on the information provided, the user makes emotionally responsive and healthy choices in their daily life.

[0720] (Example 2)

[0721] 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".

[0722] In modern society, managing users' health is a crucial issue. However, conventional health management systems rarely provide nutritional suggestions that adequately consider the user's emotional state. As a result, it is difficult to mitigate the impact of emotions such as stress on health. Therefore, there is a need to provide more precise and individualized health management and dietary suggestions that take emotional states into account.

[0723] 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.

[0724] In this invention, the server includes means for collecting the user's biometric data and psychological state, means for generating the user's psychological profile based on said information, and means for analyzing the user's nutritional needs based on said profile and health-related information. This enables personalized nutritional suggestions that take into account the user's emotional state.

[0725] "User biometric data" refers to information about the user's physical health, such as weight, diet, exercise level, and sleep duration.

[0726] "Psychological state" refers to information that indicates the user's mental state, such as their emotions and stress levels.

[0727] A "psychological profile" is a collection of data that analyzes a user's emotional state and captures its characteristics.

[0728] "Health-related information" refers to a set of data related to the user's physical and mental health status.

[0729] "Nutritional needs" refer to the user's requirements for nutrients necessary to maintain or improve their health.

[0730] "Meal suggestions" refer to meal plans and recommendations provided based on the user's nutritional needs and psychological state.

[0731] A "communication network" refers to network infrastructure used for exchanging information, such as the internet.

[0732] "Purchase procedure" refers to the series of actions taken to select and purchase a product in online shopping, etc.

[0733] "Location information" refers to data about a user's current location, which is obtained using technologies such as GPS.

[0734] "Means of conducting purchase procedures via a communication network" refers to a system that uses networks such as the internet to purchase necessary food items online.

[0735] This invention is a system that supports users' health management, and in particular aims to provide meal suggestions that take into account the user's emotional state. This system operates in conjunction with a complex software program installed on a cloud server and an application on the user's terminal.

[0736] The user terminal uses health-related applications to collect daily biometric data such as weight, diet, exercise levels, and sleep duration. The terminal also features emotion recognition capabilities, allowing it to detect the user's psychological state using text and voice input, as well as sensors built into the device. Users can manually input this data or have it automatically acquired using the sensors.

[0737] The server receives collected biometric and psychological data and stores it in a database. The server's built-in emotion engine analyzes the emotional data and generates a user psychological profile. This profile is integrated with health-related information and used to create personalized dietary recommendations based on the user's nutritional needs and psychological state.

[0738] For example, if a user is experiencing stress, the server generates a meal plan incorporating stress-relieving ingredients. Based on the analysis, a personalized nutrition plan is proposed, and the necessary ingredients are selected from an online marketplace database. Users can then purchase the suggested ingredients online with just a few clicks.

[0739] Furthermore, the server obtains the user's real-time location information and searches for shops and restaurants that match their psychological state. For example, if a user needs a change of pace, it can guide them to a cafe or restaurant suitable for regaining their energy.

[0740] Examples of prompts to input into the generating AI model include, "Create the optimal meal plan for when the user is feeling stressed," and "Suggest restaurants that match the user's current emotional state." In this way, more sophisticated and adaptive health management and meal suggestions that take into account the user's psychological state in daily life can be realized.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The user's device uses health-related applications to collect daily biometric data such as weight, diet, exercise level, and sleep duration. Input includes data manually entered by the user and data automatically collected from the device's sensors. This data is converted into an internal data format and prepared for subsequent processing. Specifically, when a user takes a photo of their meal and registers the information in the app, the nutritional data of the food is automatically recorded.

[0744] Step 2:

[0745] The user terminal uses emotion recognition to detect the user's psychological state based on text, voice data, and data acquired from built-in sensors. Inputs include the user's emotional diary and voice memos, which are analyzed to quantify the emotional state and store as part of the psychological profile. Specifically, speech recognition technology is used to analyze keywords in conversations and measure stress levels.

[0746] Step 3:

[0747] The server receives biometric and psychological state data transmitted from user terminals and stores them in a database. The input is a data stream from the end-user terminal, and the output generates a record of daily health and emotional states in the integrated database. Specifically, data is uploaded in real time and stored in server storage for analysis.

[0748] Step 4:

[0749] The server uses an emotion engine to analyze psychological data and generate a user's psychological profile. It takes past and current emotional data as input and uses this data to computationally detect the user's individual emotional tendencies. The output includes user characteristics such as "tendency to show high stress levels on weekends." The emotion engine combines natural language processing and machine learning to create the profile.

[0750] Step 5:

[0751] The server analyzes the user's nutritional needs based on their psychological profile and biometric data, and generates customized meal suggestions. It uses integrated user data as input to calculate the optimal balance of nutrients. The output is a nutrition plan tailored to the user's emotions and health status. Specific operations include selecting food groups using a nutrition algorithm.

[0752] Step 6:

[0753] The server configures the system to allow users to purchase necessary ingredients via the network based on the generated meal suggestions. The output connects to the online supermarket's database and provides users with links to purchase items. For example, users can easily proceed with the purchase process by simply pressing the "Add to Cart" button on the app.

[0754] Step 7:

[0755] The server obtains the user's current location information and identifies stores and restaurants that match their psychological state. The input is the user's real-time location information, and the system searches a store database based on the generated psychological profile, providing recommended store information as output. Specifically, this includes displaying nearby relaxing cafes on a map.

[0756] (Application Example 2)

[0757] 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".

[0758] Conventional health management systems lack the ability to support users' mental health by providing meal suggestions that take into account the user's emotional state or identifying appropriate dining facilities. Therefore, there is a need for more sophisticated and personalized services based on the user's emotions and health data.

[0759] 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.

[0760] In this invention, the server includes means for collecting user health-related information, means for detecting the user's emotional state, and means for suggesting foods that promote relaxation and mood improvement based on the detected emotional state. This enables adaptive, personalized meal suggestions and identification of dining establishments that take the user's mental health into consideration.

[0761] "Means of collecting user health-related information" refers to functions that acquire health-related data such as weight, diet, exercise levels, and sleep duration from the user's device.

[0762] A "means for analyzing nutritional needs" is a mechanism that analyzes the nutrients and dietary content required by users based on collected health-related information, and identifies specific needs.

[0763] A "means for generating meal suggestions" is a system that creates meal menus suitable for the user based on analyzed nutritional needs and emotional state.

[0764] "Means of conducting purchase procedures via a communication network" refers to the process of using a network to place an order for the proposed ingredients to be purchased online.

[0765] "Means for detecting a user's emotional state" refers to technologies that recognize and classify a user's emotions using text, voice, or sensors.

[0766] "A means of suggesting ingredients that promote relaxation and mood improvement" refers to a system that takes into account the user's current emotional state and selects and suggests ingredients that contribute to stress reduction and mood improvement.

[0767] "Means for identifying appropriate dining establishments or offices" refers to a function that searches for and selects suitable facilities from a relevant database based on the user's location information and emotional state.

[0768] This invention relates to a system that supports user health management and provides meal suggestions that take into account the user's emotional state. The server receives health-related information and emotional state data transmitted from the user terminal and analyzes it. The specific program functions as follows:

[0769] First, the user's device collects health-related information. This is done by inputting data such as weight, diet, exercise levels, and sleep duration via a dedicated application or by automatically acquiring it through sensors. The device also uses emotion recognition technology to analyze the user's emotions from text input, voice, and sensor data. Emotion recognition software such as Google Cloud AI and IBM Watson can be used for emotion analysis.

[0770] The server stores this data in a database on Amazon Web Services (AWS) and then analyzes the user's nutritional needs. This analysis uses Python to integrate health and emotional data and generates personalized meal suggestions using a generative AI model.

[0771] The suggested meals include information on the necessary ingredients, which is transmitted via a communication network to online grocery sales services. This allows users to easily purchase the recommended ingredients.

[0772] Furthermore, based on the user's emotional state, food and beverage options related to disaster prevention and stress reduction are suggested. The server utilizes the user's location information to search for the most suitable restaurants or offices from a relevant database and guide the user there.

[0773] For example, if a user tells the robot, "I'm feeling a bit down today," the server will analyze that emotion, play mood-enhancing music, suggest a chocolate dessert recipe, and, if necessary, automatically order the ingredients.

[0774] An example of a prompt message for the generating AI model would be: "Please suggest an energy-boosting meal plan recommended for the afternoon when the user is feeling tired."

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

[0776] Step 1:

[0777] The user's device collects health-related information. Users manually input data such as weight, diet, exercise levels, and sleep duration using an application, or the device's sensors automatically acquire this information. As a result, health-related data is input, and a health information file containing that data is output.

[0778] Step 2:

[0779] The user's device performs emotion recognition. It passes the user's text input or voice to emotion recognition software for analysis of their emotional state. The input is text or voice data, and the output is an emotion profile indicating the user's emotional state. This process utilizes emotion analysis APIs such as Google Cloud AI.

[0780] Step 3:

[0781] The server receives health information files and emotional profiles sent from the user's terminal. The input data includes health information and emotional profiles, which the server stores in a database on AWS. The output is the identification information for the stored data.

[0782] Step 4:

[0783] The server analyzes health information and emotional profiles to identify the user's nutritional needs. Using Python scripts, it integrates and analyzes the data, and a generated AI model suggests appropriate nutrients and meal plans based on the input data. The output is a meal plan optimized for the user.

[0784] Step 5:

[0785] The server processes the purchase of ingredients online via the communication network based on the generated meal plan. The input is the ingredient information specified in the meal plan, and the output is the purchase confirmation information from the online store.

[0786] Step 6:

[0787] The server uses the user's emotional state and location information to search for the most suitable restaurants and offices. It searches relevant databases, receiving location information and emotional status data as input, and outputs a list of recommended establishments.

[0788] Step 7:

[0789] Users can choose actions based on information provided by the server. They execute their actual meal plans by purchasing suggested ingredients or visiting recommended restaurants. The input is suggestions from the server, and the output is a log of the user's actions.

[0790] 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.

[0791] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] Means for collecting users' health-related information,

[0814] A means for analyzing the user's nutritional needs based on the said information,

[0815] A means for generating meal suggestions that address analyzed needs,

[0816] A means of selecting the necessary ingredients based on the proposal and carrying out the purchase procedure via the network,

[0817] A method for identifying appropriate restaurants and shops using the user's current location information,

[0818] A system that includes means of presenting this information to the user.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the meal suggestion generation means is configured to generate personalized menus taking into account the user's past meal history and health data.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the means for identifying restaurants and shops is configured to obtain recommended shop information from a relevant database based on the user's location information and to present the most suitable shop to the user.

[0823] "Example 1"

[0824] (Claim 1)

[0825] A device that collects user health-related information,

[0826] A device that encrypts the information and transmits it to a cloud environment,

[0827] A device that stores information received in a cloud environment,

[0828] A device that analyzes users' nutritional needs using a generative AI model,

[0829] A device that generates meal suggestions based on analyzed needs using prompt sentences,

[0830] A device that selects the necessary ingredients based on the proposal and carries out the purchase procedure via a communication network,

[0831] A device that uses the user's current location information to identify relevant facilities,

[0832] A system that includes a device for presenting this information to the user.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the meal suggestion generation device is configured to generate an individualized meal plan taking into account the user's past meal history and health data.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein the device for identifying related facilities is configured to obtain appropriate facility information from a related database based on the user's location information and to present the user with the most suitable facility.

[0837] "Application Example 1"

[0838] (Claim 1)

[0839] Means for collecting users' health-related information,

[0840] A means for analyzing the user's nutritional needs based on the said information,

[0841] A means for generating meal suggestions that address analyzed needs,

[0842] A means of selecting the necessary ingredients based on the proposal and carrying out the purchase procedure via a communication network,

[0843] A means of identifying appropriate dining establishments using the user's current location information,

[0844] A means of carrying out food delivery procedures based on the generated meal proposals,

[0845] A system that includes means of presenting this information to the user.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the meal suggestion generation means is configured to generate personalized menus taking into account the user's past meal history and health data, and the sales procedure is performed based on the generated menu.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the means for identifying the food and beverage establishment is configured to obtain recommended establishment information from relevant information sources based on the user's location information and to present the most suitable establishment to the user.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] Means for collecting users' biometric data and psychological state,

[0853] A means for generating a user's psychological profile based on the said information,

[0854] A means for analyzing the user's nutritional needs based on the profile and health-related information,

[0855] A means for generating meal suggestions that correspond to analyzed needs and emotional information,

[0856] A means of selecting the necessary ingredients based on the proposal and carrying out the purchase procedure via a communication network,

[0857] A means of identifying stores that are suitable for a user's psychological state using their location information,

[0858] A system that includes means of presenting this information to the user.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the meal suggestion generation means is configured to generate an individualized nutrition plan taking into account the user's past eating habits and biometric data.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the means for identifying the store is configured to obtain suitable store information from a relevant database based on the user's location information and psychological state, and to present the most suitable store to the user.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] Means for collecting users' health-related information,

[0866] A means for analyzing the user's nutritional needs based on the said information,

[0867] A means for generating meal suggestions that address analyzed needs,

[0868] A means of selecting the necessary ingredients based on the proposal and carrying out the purchase procedure via a communication network,

[0869] A means of detecting the user's emotional state,

[0870] A means of suggesting foods that promote relaxation and mood improvement based on detected emotional states,

[0871] A means of identifying appropriate restaurants and offices using the user's current location information,

[0872] A system that includes means of presenting this information to the user.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the meal suggestion generation means is configured to generate personalized menus taking into account the user's past meal history, health data, and emotional profile.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein the means for identifying the aforementioned food and beverage establishment or office is configured to obtain recommended facility information from a relevant database based on the user's location information and to present the most suitable facility according to the user's emotional state. [Explanation of Symbols]

[0877] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting users' health-related information, A means for analyzing the user's nutritional needs based on the said information, A means for generating meal suggestions that address analyzed needs, A means of selecting the necessary ingredients based on the proposal and carrying out the purchase procedure via the network, A method for identifying appropriate restaurants and shops using the user's current location information, A system that includes means of presenting this information to the user.

2. The system according to claim 1, wherein the meal suggestion generation means is configured to generate personalized menus taking into account the user's past meal history and health data.

3. The system according to claim 1, wherein the means for identifying restaurants and shops is configured to obtain recommended shop information from a relevant database based on the user's location information and to present the most suitable shop to the user.

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