System

Abstract

To provide a system.SOLUTION: A system includes: first means for collecting personal data indicating information about an action pattern, a custom, a living situation, and a health condition of a user; second means for analyzing the collected personal data; third means for generating a first prompt sentence for outputting an advice corresponding to the action pattern and the living situation of the user on the basis of an analysis result of the personal data; fourth means for generating the advice by inputting the generated first prompt sentence into a generative AI model; and fifth means for transmitting the generated advice to a terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a lack of efficient means to provide personalized instruction tailored to the goals and needs of each individual user. [Means for solving the problem]

[0005] The company will introduce an AI personal trainer that collects and analyzes personal data to provide customized instruction tailored to each individual's goals and needs. Furthermore, this AI personal trainer will understand each individual's behavioral patterns, habits, and lifestyle, and provide optimal advice and guidance based on that understanding. [Brief explanation of the drawings]

[0006] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a 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.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0020] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, the AI ​​personal trainer collects personal data from the user's smartphone or wearable device. The collected data covers a wide range of topics, including the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details. This data is sent to a cloud server and analyzed by the AI ​​personal trainer.

[0029] "Example 2"

[0030] The AI ​​personal trainer uses the collected data to provide customized guidance tailored to the user's goals and needs. For example, if a user is trying to lose weight, it will suggest appropriate dietary and exercise regimes. If a user is trying to maintain their health, it will suggest appropriate lifestyle and exercise habits.

[0031] "Example 3"

[0032] Furthermore, the AI ​​personal trainer understands the user's behavioral patterns and lifestyle and provides optimal advice and guidance based on that understanding. For example, if the user has a nocturnal lifestyle, it will suggest exercise and meal times that suit that lifestyle. Also, if the user has specific dietary restrictions, it will suggest meals that fit those restrictions.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Collect personal data from the user's smartphone or wearable device. The collected data covers a wide range of topics, including the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details.

[0036] Step 2: The collected data is sent to a server in the cloud.

[0037] Step 3: The AI ​​personal trainer analyzes the data sent to the cloud server.

[0038] "Example 2"

[0039] Step 1: The AI ​​personal trainer uses the collected data to provide custom coaching tailored to the user's goals and needs.

[0040] Step 2: For example, if the user is trying to lose weight, the app will suggest appropriate dietary and exercise regimes.

[0041] Step 3: If the user is aiming to maintain their health, the app will suggest appropriate lifestyle and exercise habits.

[0042] "Example 3"

[0043] Step 1: The AI ​​personal trainer understands the user's behavioral patterns and lifestyle.

[0044] Step 2: Use that understanding to provide the best advice and guidance.

[0045] Step 3: For example, if the user has a nocturnal lifestyle, the system will suggest exercise and meal times that fit that lifestyle. Also, if the user has specific dietary restrictions, the system will suggest meals that fit those restrictions.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0048] Conventional personal trainer systems have difficulty fully understanding an individual's behavioral patterns and lifestyle habits and providing appropriate advice. Furthermore, data collection and analysis are often done manually, making it difficult to provide efficient feedback.

[0049] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to a server on a cloud, means for storing the transmitted data in a database, means for analyzing the stored data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for transmitting generated feedback to a terminal, and means for displaying the feedback on the terminal. This makes it possible to automatically collect and analyze an individual's behavioral patterns and lifestyle habits and provide efficient and appropriate feedback.

[0050] "Individual personal data" refers to information such as an individual's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[0051] "Means of collection" refers to methods and devices for obtaining personal data of individuals using smartphones or wearable devices.

[0052] "Cloud servers" refer to remote servers accessible via the Internet that provide infrastructure for storing and processing data.

[0053] "Transmitting means" refers to a method or device for transferring collected data to a server on the cloud via the Internet.

[0054] "Means for storing data in a database" refers to a system or method for storing received data in a structured format.

[0055] "Means of analysis" refers to the algorithms and software used to analyze stored data and assess an individual's behavioral patterns and health status.

[0056] "Means for providing customized instruction" refers to a method or apparatus for generating advice or guidance based on the analysis results that is tailored to individual goals and needs.

[0057] "Means for sending feedback" refers to a method or device for transferring the generated feedback to the terminal.

[0058] "Means for displaying feedback" refers to a method or device for visually presenting feedback to a user at a terminal.

[0059] This invention relates to an AI personal trainer system that collects and analyzes personal data of individuals and provides customized instruction tailored to their individual goals and needs. Specific embodiments of this system will be described below.

[0060] Users use smartphones and wearable devices to collect personal data such as behavioral patterns, lifestyle habits, health status, exercise volume, and dietary details. Specifically, they use smartphone applications and wearable devices. These devices record data such as the number of steps taken, heart rate, sleep patterns, and dietary details.

[0061] The device sends the collected data to a cloud server. The data is encrypted and sent over an internet connection. For example, a smartphone application sends step count data to a cloud server.

[0062] The server stores the received data in a database. Specifically, the server uses a database system such as MySQL (registered trademark) to store the user's data in a structured format.

[0063] The server then passes the stored data to an AI personal trainer for analysis. The AI ​​personal trainer analyzes the data using machine learning libraries such as Python® and TENSORFLOW®. For example, the server inputs the user's step count data into an AI model to evaluate exercise progress.

[0064] The server generates feedback based on the analysis results. For example, an AI personal trainer might generate feedback such as, "You haven't reached your goal of 10,000 steps today. I recommend walking another 2,000 steps."

[0065] The generated feedback is sent from the server to the device, which then displays the feedback to the user. For example, a smartphone application might display a notification to the user saying, "You haven't reached your goal of 10,000 steps today. We recommend that you walk another 2,000 steps."

[0066] Specific examples

[0067] Example 1: Exercise advice

[0068] Suppose a user uses a wearable device to record the number of steps they take each day. The wearable device collects the user's step count data and sends it to a smartphone application. The application sends this data to a server on the cloud, which stores the data in a database. The AI ​​personal trainer analyzes the stored data and generates feedback for the user, such as, "You haven't reached your goal of 10,000 steps today. I recommend walking another 2,000 steps." The server sends this feedback to the smartphone application, which then displays a notification to the user.

[0069] Example 2: Dietary advice

[0070] Suppose a user is using a smartphone application to record their diet. The user enters their dietary information into the application, which then sends this data to a server in the cloud. The server stores the data in a database, and an AI personal trainer analyzes the data. As a result of the analysis, feedback is generated for the user, such as "Today's meal lacks vegetables. We recommend adding a salad to your next meal." The server then sends this feedback to the smartphone application, which then displays a notification to the user.

[0071] Prompt Sentence Examples

[0072] "Please describe an AI personal trainer system that analyzes a user's behavioral patterns and health status and generates appropriate exercise and dietary advice. Please include the names of any specific devices or software."

[0073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0074] Step 1: Data collection

[0075] Users use smartphones or wearable devices to collect personal data such as behavioral patterns, lifestyle habits, health status, exercise amount, and dietary details. Specifically, they use smartphone applications or wearable devices. The input is the user's behavioral data, and the output is the collected personal data.

[0076] Step 2: Send data

[0077] The device sends the collected data to a server in the cloud. At this time, the data is encrypted and sent via an internet connection. Specifically, the smartphone application sends step count data to the cloud server. The input is the collected personal data, and the output is the data sent to the server.

[0078] Step 3: Save data

[0079] The server stores the received data in a database. Specifically, the server uses a database system such as MySQL to store the user's data in a structured format. The input is the data sent to the server, and the output is the data stored in the database.

[0080] Step 4: Data analysis

[0081] The server passes the stored data to the AI ​​personal trainer for analysis. The AI ​​personal trainer analyzes the data using machine learning libraries such as Python and TensorFlow. Specifically, the server inputs the user's step count data into an AI model to evaluate exercise progress. The input is the data stored in the database, and the output is the analysis results.

[0082] Step 5: Feedback generation

[0083] The server generates feedback based on the analysis results. Specifically, the AI ​​personal trainer generates feedback such as, "You haven't reached your goal of 10,000 steps today. I recommend you walk another 2,000 steps." The input is the analysis results, and the output is the generated feedback.

[0084] Step 6: Send your feedback

[0085] The server sends the generated feedback to the device. Specifically, the server sends the generated feedback to the smartphone application. The input is the generated feedback, and the output is the feedback sent to the device.

[0086] Step 7: Feedback display

[0087] The device displays feedback to the user. Specifically, a smartphone application displays a notification to the user saying, "You haven't reached your goal of 10,000 steps today. We recommend you walk another 2,000 steps." The input is the feedback sent to the device, and the output is the feedback displayed to the user.

[0088] (Application example 1)

[0089] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] While existing personal trainer systems can provide guidance based on an individual's health status and lifestyle habits, they lack the ability to provide meal plans or link with food delivery services, making it difficult for users to easily consume healthy meals. Furthermore, there is also the issue of not being able to fully meet the needs of users due to a lack of data analysis to generate individual meal plans.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0092] In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for generating an optimal meal plan based on the analysis results, and means for providing meals based on the generated meal plan. This makes it possible to provide an optimal meal plan based on the user's health condition and lifestyle habits, and to link with food delivery services.

[0093] "Individual personal data" refers to information such as an individual's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[0094] "Means of collection" refers to methods of obtaining individuals' personal data using smartphones, wearable devices, etc.

[0095] "Means of analysis" refers to the method of sending collected personal data to a server on the cloud and having AI analyze the data.

[0096] "Means for providing customized instruction" refers to methods for providing advice and guidance tailored to individual goals and needs based on the analysis results.

[0097] "Method of generating meal plans" refers to the method by which the AI ​​creates optimal meal plans for users based on the analysis results.

[0098] "Means of providing meals" refers to the method of delivering meals to users through affiliated food delivery services based on the generated meal plan.

[0099] The system for implementing this invention includes a series of processes for collecting and analyzing personal data of an individual, generating an optimal meal plan, and providing meals through a food delivery service. A specific embodiment of this system is described below.

[0100] First, personal data is collected from the user's smartphone or wearable device. This data includes the user's behavioral patterns, lifestyle habits, health status, amount of exercise, dietary content, etc. This data is obtained, for example, using sensors in the smartphone or the data collection function of the wearable device.

[0101] The collected data is then sent to a server on the cloud. The server can be a cloud service such as AWS (registered trademark) or Amazon Web Services. On the server, the collected data is analyzed using AI. The AI ​​model uses a machine learning framework such as TensorFlow.

[0102] Based on the analysis results, the server generates an optimal meal plan for the user. This meal plan is customized based on the user's health condition and lifestyle habits. The generated meal plan is sent to a partner food delivery service. The food delivery service can accept orders via API communication, for example.

[0103] For example, a user opens an app on their smartphone and enters their daily activity data and food preferences. The app sends this data to the cloud, where AI analyzes it. The AI ​​then generates a meal plan optimized for the user and sends an order to partner restaurants. The user can then receive healthy meals at home.

[0104] Examples of prompts for generative AI models include:

[0105] "Generate optimal meal plans by taking into account the user's activity level, food preferences, health status, and daily calorie intake."

[0106] In this way, it becomes possible to provide optimal meal plans based on the user's health condition and lifestyle habits, and to link with food delivery services.

[0107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0108] Step 1:

[0109] Users collect personal data using smartphones and wearable devices.

[0110] Input: Data such as user behavior patterns, lifestyle habits, health status, exercise amount, and dietary details.

[0111] Data processing: Acquire data from sensors on smartphones and wearable devices and convert it into an appropriate format.

[0112] Output: Collected personal data.

[0113] Step 2:

[0114] The device sends the collected personal data to a server on the cloud.

[0115] Input: Collected personal data.

[0116] Data processing: The data is packaged using the appropriate protocol (e.g. HTTPS) to send it to the cloud server.

[0117] Output: Personal data sent to cloud server.

[0118] Step 3:

[0119] The server analyzes the received personal data.

[0120] Input: Personal data sent to the cloud server.

[0121] Data Computing: Using AI models (e.g., TensorFlow) to analyze data and assess the user's health and lifestyle.

[0122] Output: Analysis results (e.g., user's health status, lifestyle assessment).

[0123] Step 4:

[0124] The server generates an optimal meal plan based on the analysis results.

[0125] Input: Analysis results.

[0126] Data Computation: Generative AI models are used to generate optimal meal plans based on the user's health and lifestyle habits.

[0127] Output: The generated meal plan.

[0128] Step 5:

[0129] The server sends the generated meal plan to a partner food delivery service.

[0130] Input: The generated meal plan.

[0131] Data processing: Convert the meal plan into a format suitable for the food delivery service's API and send it.

[0132] Output: Meal plan sent to food delivery service.

[0133] Step 6:

[0134] The food delivery service prepares and delivers meals to users based on their meal plan.

[0135] Input: The meal plan sent to the food delivery service.

[0136] What happens: The food delivery service prepares the meal and delivers it to you at the specified time and location.

[0137] Output: The meal delivered to the user.

[0138] In this way, optimal meal plans based on the user's health condition and lifestyle habits can be provided, and collaboration with food delivery services can be achieved.

[0139] Example 2

[0140] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] Conventional personal trainer systems lack the data collection and analysis required to provide customized training tailored to individual goals and needs, resulting in insufficient individualized training. Additionally, it was difficult to understand the user's behavioral patterns, habits, and lifestyle, and provide optimal advice based on that.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0143] In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to the server, means for the server to store the received data in a database, means for the server to analyze the data using a generative AI model, means for the server to generate customized instruction tailored to individual goals and needs based on the analysis results, means for transmitting the generated customized instruction to a terminal, and means for the terminal to display the customized instruction to the user. This makes it possible to provide highly accurate customized instruction tailored to the user's goals and needs.

[0144] "Individual personal data" refers to information about an individual, such as the user's goals, weight, height, diet, and exercise habits.

[0145] A "server" is a computer system that stores data, analyzes it, runs generative AI models, etc.

[0146] A "terminal" is a device (e.g., smartphone, tablet, PC) that a user uses to enter data and receive customized instruction.

[0147] A "database" is a system that allows a server to store and manage users' personal data.

[0148] A "generative AI model" is an artificial intelligence model that analyzes user data and generates custom instruction.

[0149] "Customized instruction" is personalized advice and guidance generated based on a user's goals and needs.

[0150] A "prompt" is a textual instruction to be input to a generative AI model.

[0151] An "HTTP POST request" is a type of communication protocol that allows a terminal to send data to a server.

[0152] An "INSERT query" is an SQL statement used to add new data to a database.

[0153] A "SELECT query" is an SQL statement used to retrieve data from a database.

[0154] This invention is a system that collects personal data of an individual, analyzes it using a generative AI model, and provides customized instruction tailored to the user's goals and needs. Specific embodiments of this system are described below.

[0155] Hardware and software used

[0156] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0157] Software: Generative AI models (e.g., GPT-4®), database management systems (e.g., MySQL), and user interfaces

[0158] Data collection and transmission

[0159] The user uses the device to input personal data such as goals (e.g., diet, maintaining health), current weight, height, dietary habits, exercise habits, etc. The device then sends the input data to the server using an HTTP POST request.

[0160] Data storage and management

[0161] The server stores the received data in a database. The database management system (e.g. MySQL) organizes the data by user and executes INSERT queries to store the data.

[0162] Analyzing data and generating custom instruction

[0163] The server retrieves the user's data from the database using a SELECT query and generates prompts to input into a generative AI model (e.g., GPT-4). The generative AI model then generates custom instruction tailored to the user's goals and needs based on the prompts.

[0164] Sending and Viewing Custom Instructions

[0165] The server sends the generated custom instruction to the terminal as an HTTP response, and the terminal displays the received custom instruction to the user.

[0166] Specific examples

[0167] For users who want to lose weight

[0168] 1. User enters data:

[0169] The user enters the following information into the application form: "Goal weight: 60 kg, Current weight: 70 kg, Diet: Bread and coffee for breakfast, salad and chicken for lunch, fish and vegetables for dinner, Exercise habits: Jogging three times a week" and presses the "Submit" button.

[0170] 2. The device sends the data to the server:

[0171] The terminal transmits the input data to the server.

[0172] 3. The server saves the data to the database:

[0173] The server stores user data in a database.

[0174] 4. The server analyzes the data using the generative AI model:

[0175] The server retrieves user data from the database and generates prompt sentences for the generative AI model.

[0176] 5. The server generates the custom instructions:

[0177] The generative AI model suggests calorie restrictions and specific exercise plans based on the following: target weight: 60 kg, current weight: 70 kg, diet: bread and coffee for breakfast, salad and chicken for lunch, fish and vegetables for dinner, exercise habits: jogging three times a week.

[0178] 6. The server sends the generated instructions to the device:

[0179] The server transmits the generated instructions to the terminal.

[0180] 7. The device displays instructions to the user:

[0181] The device displays instructions to the user such as, "We recommend limiting your daily calorie intake to 1,500 kcal and continuing to jog three times a week. Add a protein shake to your breakfast and eat plenty of vegetables at lunch."

[0182] Prompt Sentence Examples

[0183] For users looking to lose weight:

[0184] "My current weight is 70 kg, and my goal weight is 60 kg. My daily diet consists of bread and coffee for breakfast, salad and chicken for lunch, and fish and vegetables for dinner. I go jogging three times a week. Please suggest an appropriate diet and exercise plan."

[0185] For users looking to stay healthy:

[0186] "I currently weigh 65 kg and don't have any specific weight loss goals. I try to eat a balanced diet each day, but I'd like some specific advice. I do yoga twice a week. Could you suggest a lifestyle and exercise plan to help me stay healthy?"

[0187] In this way, the system performs specific actions at each step and provides customized instruction to the user.

[0188] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0189] Step 1:

[0190] The user enters the data.

[0191] The user uses the device to input personal data such as goals (e.g., diet, maintaining health), current weight, height, dietary habits, and exercise habits. Specifically, the user enters the required information into a form in a dedicated application and presses the "Submit" button. The input data includes the target weight, current weight, dietary habits, and exercise habits.

[0192] Step 2:

[0193] The device sends the data to the server.

[0194] The terminal sends the data entered by the user to the server using an HTTP POST request. Specifically, the terminal converts the entered data into JSON format and sends it to the server's API endpoint. The input is the personal data entered by the user, and the output is the data sent to the server.

[0195] Step 3:

[0196] The server stores the data in a database.

[0197] The server stores the received data in a database. Specifically, the server uses a database management system (e.g., MySQL) to organize the data for each user and executes INSERT queries to store the data. The input is the data sent to the server, and the output is the data stored in the database.

[0198] Step 4:

[0199] The server analyzes the data using the generative AI model.

[0200] The server retrieves the user's data from the database using a SELECT query and generates a prompt to input to the generative AI model. Specifically, the server creates a prompt based on the user's goals and current situation and inputs it into the generative AI model (e.g., GPT-4). The input is the user's data retrieved from the database, and the output is the prompt to input to the generative AI model.

[0201] Step 5:

[0202] The server generates the custom instructions.

[0203] The generative AI model generates custom guidance tailored to the user's goals and needs based on the prompt. Specifically, the generative AI model analyzes the prompt and generates appropriate dietary and exercise plans and lifestyle advice. The input is the prompt, and the output is the generated custom guidance.

[0204] Step 6:

[0205] The server transmits the generated instruction to the terminal.

[0206] The server sends the generated custom instruction to the terminal as an HTTP response. Specifically, the server converts the generated instruction into JSON format and sends it to the terminal. The input is the generated custom instruction, and the output is the data sent to the terminal.

[0207] Step 7:

[0208] The terminal displays instructions to the user.

[0209] The device displays the customized instruction received from the server to the user. Specifically, the device updates the application UI and displays the generated instruction content in a user-friendly format. The input is the customized instruction received from the server, and the output is the instruction content displayed to the user.

[0210] (Application example 2)

[0211] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0212] While conventional personal trainer systems can provide customized instruction tailored to the user's goals and needs, they lack functions such as meal menu suggestions, ordering, and progress tracking, resulting in insufficient comprehensive support for users to achieve their goals. In particular, because there is no system that integrates meal suggestions and ordering, it takes time for users to actually consume the suggested meals, making continuous health management difficult.

[0213] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0214] In this invention, the server includes means for collecting personal data of an individual, means for analyzing the collected personal data, means for providing customized instruction tailored to individual goals and needs based on the analysis results, means for suggesting optimal meal menus based on the customized instruction, means for ordering the suggested meal menus in cooperation with an external meal provision service, and means for tracking the user's progress and providing feedback, thereby enabling the user to receive consistent health management, easily order the suggested meal menus, and continuously track their health status.

[0215] "Individual personal data" refers to individual information about a user, such as their weight, height, age, activity level, and goals.

[0216] "Means of collection" refers to the methods and devices used to obtain personal data from users.

[0217] "Means for analysis" refers to the methods and devices used to analyze collected personal data and extract information based on the user's goals and needs.

[0218] "Customized Instruction" refers to instruction or advice tailored to a user's individual goals and needs.

[0219] "AI personal trainer" refers to a system that uses artificial intelligence to analyze a user's personal data and provide customized instruction.

[0220] "Means for suggesting meal menus" refers to a method or device for generating and suggesting optimal meal menus based on the user's personal data and custom instructions.

[0221] "External Meal Delivery Service" refers to an external service provider that actually delivers the proposed meal menu.

[0222] "Means for ordering" refers to a method or device for placing an order for a proposed meal menu from an external meal service.

[0223] "Progress tracking means" means a method or device that continuously monitors and records a user's health status and progress toward goals.

[0224] "Means for providing feedback" refers to a method or device for providing appropriate advice or information to a user based on tracked progress data.

[0225] The system for implementing the present invention includes a series of means for collecting and analyzing personal data of an individual and providing customized guidance. Specific embodiments will be described below.

[0226] First, the user enters their personal data (weight, height, age, activity level, goals, etc.) through a smartphone application, which is then sent to a server and stored.

[0227] The server then analyzes the collected personal data using a generative AI model to generate custom guidance based on the user's goals and needs. For example, if the user is trying to lose weight, it will suggest low-calorie, nutritionally balanced meals.

[0228] The proposed meal menu can be ordered in cooperation with an external meal service. The user can easily order the proposed menu through the application. This ordering process is executed by the server using the API of the external meal service.

[0229] Furthermore, the server tracks the user's progress and provides feedback. It periodically collects data such as the user's weight and body fat percentage, and uses a generative AI model to evaluate the user's progress. Based on the evaluation results, the server provides appropriate advice and guidance to the user.

[0230] The following hardware and software are used to implement this system:

[0231] Smartphone (as a user interface)

[0232] Server (for storing and analyzing data)

[0233] Generative AI models (to generate custom instruction)

[0234] External meal service API (to order meals)

[0235] For example, if a user is trying to lose weight, they can input the following prompts into the generative AI model:

[0236] "Recommend optimal meals based on the user's weight, height, age, activity level, and goals. Make the suggested meals available for ordering through a food delivery service."

[0237] Based on this prompt, the generative AI model will suggest the optimal meal plan for the user, and the server will then order the plan from an external meal delivery service. Through this process, users can receive consistent health care.

[0238] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0239] Step 1:

[0240] The user inputs personal data through a smartphone application.

[0241] Input: Data such as the user's weight, height, age, activity level, and goals.

[0242] Output: The entered personal data is sent to the server.

[0243] Specific operation: The user enters their personal data into the application's input form and presses the "Submit" button. The application then sends the input data to the server.

[0244] Step 2:

[0245] The server stores the received personal data.

[0246] Input: Personal data submitted by the user.

[0247] Output: Saved personal data.

[0248] Specific operation: The server stores the received data in a database for later analysis.

[0249] Step 3:

[0250] The server analyzes the stored personal data.

[0251] Input: Stored personal data.

[0252] Output: Analysis results (customized instruction based on user goals and needs).

[0253] How it works: The server uses a generative AI model to analyze stored personal data and generate custom coaching based on the user's goals and needs.

[0254] Step 4:

[0255] The server will suggest the best meal menu based on your custom instructions.

[0256] Input: Analysis results (custom instruction).

[0257] Output: A suggested meal menu.

[0258] Specific operation: The server inputs a prompt into the generative AI model to generate the optimal meal menu for the user. Example prompt: "Please suggest the optimal meal menu based on the user's weight, height, age, activity level, and goals."

[0259] Step 5:

[0260] The server orders the proposed meal menu in cooperation with an external meal provision service.

[0261] Input: A suggested meal menu.

[0262] Output: Order confirmation information.

[0263] Specific operation: The server uses the API of the external meal service to order the proposed meal menu. If the order is successful, order confirmation information is returned to the server.

[0264] Step 6:

[0265] The server tracks the user's progress and provides feedback.

[0266] Input: Progress data such as the user's weight and body fat percentage.

[0267] Output: Feedback information.

[0268] Specific operation: The server periodically collects progress data from the user, evaluates the progress using a generative AI model, and provides appropriate advice and guidance to the user based on the evaluation results.

[0269] Example 3

[0270] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0271] Conventional personal trainer systems have difficulty fully understanding an individual's behavioral patterns and lifestyle and providing optimal advice and guidance based on that. Furthermore, insufficient pre-processing and analysis of collected data has led to problems with the accuracy of the advice provided.

[0272] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for preprocessing the collected personal data, means for analyzing the preprocessed data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for generating optimal advice and guidance using a generative AI model, and means for providing the generated advice to the user's terminal. This makes it possible to understand an individual's behavioral patterns and living situation in detail and provide highly accurate advice and guidance based on that understanding.

[0273] "Individual personal data" refers to information about an individual's behavioral patterns, habits, living conditions, health status, etc.

[0274] "Means of collection" refers to the hardware and software functions used to obtain an individual's personal data.

[0275] "Preprocessing means" refers to data cleansing and filtering functions to convert collected data into a format suitable for analysis.

[0276] "Means of analysis" refers to algorithms and machine learning models that use preprocessed data to analyze individuals' behavioral patterns and living conditions.

[0277] "Means for providing customized coaching" refers to the ability to generate advice and guidance tailored to individual goals and needs based on the analysis results.

[0278] "Generative AI model" refers to an artificial intelligence model that generates optimal advice and guidance based on a user's behavioral patterns and living situation.

[0279] "Means for generating advice or guidance" refers to the ability to use a generative AI model to create optimal advice or guidance for a user.

[0280] "Means for providing to the user's device" refers to the function of sending the generated advice and guidance to the user's device, such as a smartphone or tablet.

[0281] This invention relates to an AI personal trainer system that understands an individual's behavioral patterns and lifestyle and provides optimal advice and guidance based on that understanding. Specific embodiments of this system are described below.

[0282] System configuration

[0283] This system includes a server, a user's device, and a generative AI model. The server collects, preprocesses, and analyzes the user's personal data to generate optimal advice and guidance. The user's device is a device such as a smartphone or tablet that receives the advice and guidance provided by the server.

[0284] Hardware and software used

[0285] Hardware: Smartwatches, smartphones, tablets

[0286] Software: Python, TensorFlow, Database Management System

[0287] Data collection and preprocessing

[0288] The server collects lifestyle data from users' devices. Specifically, it obtains data such as sleep time, exercise time, and meal times from sensors on smartwatches and smartphones. The collected data is stored in a database and preprocessed. This preprocessing includes filling in missing values ​​and removing outliers.

[0289] Analyzing the data

[0290] The pre-processed data is then analyzed using Python and TensorFlow. The server then applies machine learning models to analyze the user's behavioral patterns and lifestyle. For example, an algorithm is used to determine whether the user has a night-owl lifestyle.

[0291] Generating Advice

[0292] Based on the analysis results, the server uses a generative AI model to generate optimal advice and guidance tailored to the user's behavioral patterns and lifestyle. For example, for a user with a nocturnal lifestyle, the server suggests optimal times for exercise and meals at night.

[0293] Providing advice

[0294] The generated advice is sent from the server to the user's device, where the user can check the advice on their smartphone, tablet, or other device. Specific examples of advice provided include the following:

[0295] Suggested exercise time: "Since you have a night owl lifestyle, I recommend exercising between 8:00 and 9:00 PM."

[0296] Mealtime suggestion: "In keeping with your night-owl lifestyle, we recommend eating dinner between 7:00 and 8:00 p.m."

[0297] Prompt Sentence Examples

[0298] An example of a prompt sentence to input to the generative AI model is as follows:

[0299] User behavior: Night owl

[0300] User's life situation: Work continues late into the night

[0301] User dietary restrictions: Gluten-free

[0302] Generated advice: Suggested exercise and meal times to suit night-owl lifestyles, gluten-free meal suggestions

[0303] In this way, the AI ​​personal trainer can understand the user's behavioral patterns and lifestyle in detail and provide highly accurate advice and guidance based on that understanding. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[0304] Step 1: Collect user data

[0305] The server collects lifestyle data from the user's device. As input, it receives data such as sleep time, exercise time, and meal time obtained from sensors on the smartwatch or smartphone. Specifically, the server obtains this data through an API and stores it in a database. As output, the collected raw data is stored in the database.

[0306] Step 2: Preprocessing the data

[0307] The server preprocesses the collected data. It uses the raw data stored in the database as input. Specifically, the server performs functions such as filling in missing values ​​and removing outliers. For example, if a user forgets to record some data, it applies an algorithm to fill in the missing data. It also detects and removes outliers such as abnormally high heart rates or step counts. The output is clean, preprocessed data.

[0308] Step 3: Analyze behavioral patterns

[0309] The server uses the preprocessed data to analyze the user's behavioral patterns. The preprocessed, clean data is used as input. Specifically, the server applies a machine learning model using Python and TensorFlow to determine whether the user has a night-owl lifestyle. For example, if the user's sleep time is from late at night to late in the morning, the server determines that the user has a night-owl lifestyle. The output is an analysis result of the user's behavioral patterns.

[0310] Step 4: Generating Advice

[0311] The server uses a generative AI model to generate optimal advice and guidance based on the analysis results. The behavioral pattern analysis results are used as input. Specifically, the server inputs prompt statements into the generative AI model to generate optimal advice for the user. For example, for a user with a nocturnal lifestyle, it would suggest appropriate exercise and meal times at night. The generated advice and guidance is obtained as output.

[0312] Step 5: Providing advice

[0313] The server provides the generated advice to the user's device. The generated advice and guidance are used as input. Specifically, the server sends the advice to the user's smartphone or tablet. The user can check the advice on their device. For example, if the user has a nocturnal lifestyle, the following notification will be displayed on the device: "You have a nocturnal lifestyle, so we recommend that you exercise between 8:00 and 9:00 p.m." The output is the advice displayed on the user's device.

[0314] (Application example 3)

[0315] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0316] Traditional personal trainer systems struggled to fully understand users' behavioral patterns and lifestyles and provide customized instruction tailored to their individual needs. They also lacked the functionality to provide real-time feedback and advice, preventing them from effectively supporting users in managing their health and achieving their fitness goals. Furthermore, they were inadequate in proposing optimal exercise and meal plans based on users' lifestyle habits and dietary restrictions.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means including an artificial intelligence personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, means for learning the user's behavioral patterns and lifestyle and proposing optimal exercise and meal plans, and means for providing real-time feedback and advice. This makes it possible to propose optimal exercise and meal plans based on the user's behavioral patterns and lifestyle, and to effectively support the user in managing their health and achieving their fitness goals through real-time feedback and advice.

[0318] "Individual personal data" refers to individual information such as a user's behavioral patterns, lifestyle habits, health status, and dietary restrictions.

[0319] "Means of collection" refers to methods of obtaining individuals' personal data using sensors, smartphone applications, wearable devices, etc.

[0320] "Means of analysis" refers to the algorithms and software used to process collected personal data and understand users' behavioral patterns and living situations.

[0321] An "artificial intelligence personal trainer" is a system that uses machine learning and artificial intelligence technology to provide custom instruction tailored to a user's individual goals and needs.

[0322] "Means of learning user behavior patterns and lifestyle conditions" refers to algorithms and software that understand and learn from users' lifestyle habits and behavior patterns based on collected data.

[0323] "Means for suggesting optimal exercise and meal plans" refers to algorithms and software that generate and suggest individually customized exercise and meal plans based on the user's behavioral patterns and lifestyle.

[0324] "Means of providing real-time feedback and advice" refers to algorithms and software that provide immediate and appropriate feedback and advice based on the user's current state and behavior.

[0325] A system for implementing this invention includes an AI personal trainer that collects and analyzes personal data of an individual and proposes optimal exercise and meal plans to the user. Specific embodiments of this system are described below.

[0326] 1. System Configuration

[0327] The system consists of the following main components:

[0328] Data collection methods: Smartphones, wearable devices, sensors, etc. are used to collect data on users' behavioral patterns and living conditions.

[0329] Data analysis methods: Using algorithms and software (e.g., Python, machine learning libraries) to process collected data and understand users' behavioral patterns and life situations.

[0330] AI Personal Trainer: A system that uses machine learning and artificial intelligence technology to provide custom coaching tailored to a user's individual goals and needs.

[0331] Recommendation method: Algorithms and software that generate and suggest optimal exercise and meal plans based on the user's behavioral patterns and lifestyle.

[0332] Feedback tools: Algorithms and software that provide real-time feedback and advice.

[0333] 2. Data Collection and Analysis

[0334] The server collects data on the user's behavioral patterns and lifestyle from smartphones and wearable devices, including sleep patterns, exercise history, dietary habits, etc. The collected data is processed using data analysis tools to generate a model for understanding the user's behavioral patterns and lifestyle.

[0335] 3. Providing custom instruction

[0336] Based on the generated model, the AI ​​personal trainer will provide customized guidance tailored to the user's individual goals and needs. For example, if the user has a nocturnal lifestyle, it will suggest evening jogging or stretching. If the user is vegan, it will suggest a menu centered on tofu and vegetables.

[0337] 4. Real-time feedback

[0338] The server provides instant and appropriate feedback and advice based on the user's current condition and behavior, allowing users to receive real-time support and effective support in managing their health and achieving their fitness goals.

[0339] Specific examples

[0340] For example, if a user has a night owl lifestyle, the system will suggest evening jogging or stretching, or if the user is vegan, it will suggest tofu and vegetable-based meals.

[0341] Prompt Sentence Examples

[0342] "Recommend optimal exercise and meal plans based on the user's behavioral patterns and dietary restrictions. For example, if the user is a night owl, suggest evening jogging or stretching. If the user is vegan, suggest a menu centered around tofu and vegetables."

[0343] In this way, the system can provide users with advice tailored to their individual needs and effectively support them in managing their health and fitness goals.

[0344] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0345] Step 1:

[0346] The server collects data on the user's behavioral patterns and lifestyle from smartphones and wearable devices. Specifically, it acquires data on the user's sleep patterns, exercise history, dietary details, etc. The input is the data sent from the smartphone or wearable device, and the output is the collected personal data.

[0347] Step 2:

[0348] The server stores the collected personal data in a database. Specifically, it uses a database management system (e.g., MySQL) to organize and store data for each user. The input is the collected personal data, and the output is the data stored in the database.

[0349] Step 3:

[0350] The server analyzes the stored personal data. Specifically, it uses machine learning algorithms to generate a model for understanding the user's behavioral patterns and living situations. The input is the personal data stored in the database, and the output is a model for understanding the user's behavioral patterns and living situations.

[0351] Step 4:

[0352] The server then uses the generated model to provide customized guidance tailored to the user's individual goals and needs. Specifically, it proposes optimal exercise and meal plans based on the user's behavioral patterns and lifestyle. The input is the generated model, and the output is the content of the customized guidance.

[0353] Step 5:

[0354] The server provides real-time feedback and advice to users. Specifically, it instantly generates appropriate feedback and advice based on the user's current state and behavior, and notifies the user through a smartphone application. The input is the user's current state and behavior data, and the output is real-time feedback and advice.

[0355] Step 6:

[0356] The user receives and implements the feedback and advice provided by the server. Specifically, the user implements the exercise and meal plans notified to them through the smartphone application and sends the results back to the server. The input is the feedback and advice from the server, and the output is the data of the implementation results.

[0357] In this way, the system can provide users with advice tailored to their individual needs and effectively support them in managing their health and fitness goals.

[0358] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0359] "Example 1"

[0360] In one embodiment of the present invention, the AI ​​personal trainer includes an emotion engine that recognizes a user's emotions and tailors custom instruction based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it can suggest relaxation and exercises that contribute to relaxation. Alternatively, if the emotion engine recognizes that the user is feeling joy, it can suggest activities to maintain that joy.

[0361] "Example 2"

[0362] The emotion engine also provides optimal advice and guidance based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling sad, it will provide advice to alleviate that sadness. This advice could include appropriate exercise, diet, or relaxation techniques.

[0363] "Example 3"

[0364] Additionally, the emotion engine tracks a user's emotional state over time and analyzes changes. Based on this analysis, the emotion engine provides customized guidance that responds to changes in the user's emotional state. For example, if the emotion engine recognizes that a user has been feeling stressed for a period of time, it will suggest a long-term plan for stress management.

[0365] The processing flow of each embodiment will be described below.

[0366] "Example 1"

[0367] Step 1: The AI ​​personal trainer recognizes the user's emotions using an emotion engine.

[0368] Step 2: The emotion engine tailors custom instruction based on the perceived emotional state.

[0369] Step 3: For example, if the emotion engine recognizes that the user is feeling stressed, it will suggest relaxation and exercises that will help with relaxation.

[0370] "Example 2"

[0371] Step 1: The emotion engine provides optimal advice and guidance based on the user's emotional state.

[0372] Step 2: For example, if the emotion engine recognizes that the user is feeling sad, it will provide advice to alleviate that sadness.

[0373] Step 3: This advice may include proper exercise, diet, or relaxation techniques.

[0374] "Example 3"

[0375] Step 1: The emotion engine tracks the user's emotional state over time and analyzes its changes.

[0376] Step 2: Based on this analysis, the emotion engine provides custom guidance that responds to changes in the user's emotional state.

[0377] Step 3: For example, if the user recognizes that they have been feeling stressed for a period of time, the emotion engine will suggest a long-term plan for stress management.

[0378] Example 1

[0379] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0380] Conventional personal trainer systems have difficulty fully understanding a user's behavioral patterns and emotional state and providing customized instruction based on that. Furthermore, they have been unable to provide appropriate advice in real time according to the user's emotional state. This has limited the effectiveness of the system in managing the user's health and improving their lifestyle.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0382] In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to a server on the cloud, means for storing the transmitted personal data in a database, means for analyzing the stored personal data, means for generating customized instruction tailored to individual goals and needs based on the analysis results, means for transmitting the generated customized instruction to the terminal and providing it to the user, and means for recognizing the user's emotions and adjusting the customized instruction based on the user's emotional state. This makes it possible to understand the user's behavioral patterns and emotional state in real time and provide optimal customized instruction based on that.

[0383] "Individual personal data" refers to individual information such as a user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details.

[0384] A "cloud server" is a remote server accessible via the Internet, and refers to a computer system for storing and processing data.

[0385] A "database" refers to a collection of information that stores collected data in an organized manner and can be searched and updated as needed.

[0386] "Means for analysis" refers to the algorithms and software used to analyze the collected data and assess the user's behavioral patterns and health status.

[0387] "Customized Instruction" refers to specific advice and guidance tailored to a user's individual goals and needs.

[0388] A "terminal" is a device that can be directly operated by a user, such as a smartphone or wearable device.

[0389] "Means for recognizing emotions" refers to technology that analyzes a user's voice data and text data to determine the user's emotional state.

[0390] "Adjustment" refers to algorithms or software that alter the content of the custom instruction provided based on the perceived emotional state.

[0391] This invention is a system that collects personal data using a user's smartphone or wearable device, analyzes the data on a cloud server, and provides optimal customized instruction to the user. Specific embodiments of this system are described below.

[0392] Data collection

[0393] A user's smartphone or wearable device collects data through built-in sensors and applications. For example, a smartphone application may record a user's diet, while a wearable device may measure heart rate or the number of steps taken. These devices include standard smartphones and wearable devices.

[0394] Data transmission

[0395] The data collected by the device is sent via the internet to a server in the cloud. The data is encrypted before transmission to ensure security. A common cloud platform is used for the cloud service.

[0396] Data storage

[0397] The server stores the received data in a database. A typical relational database is used for the database. The stored data is organized by user and prepared for later analysis.

[0398] Data analysis

[0399] The server then passes the stored data to an AI personal trainer, who analyzes it using machine learning algorithms, such as TensorFlow, to analyze the user's exercise patterns and dietary habits and assess their health.

[0400] emotion recognition

[0401] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data and determines their emotions using natural language processing technology. For example, if a user enters "I'm tired today," the emotion engine will recognize that the user is feeling stressed.

[0402] Generate custom instruction

[0403] The server generates custom guidance based on the user's emotional state. For example, if it detects that the user is feeling stressed, it will suggest yoga poses or deep breathing exercises to help with relaxation. Conversely, if it detects that the user is feeling joy, it will suggest activities to maintain that joy.

[0404] Providing guidance

[0405] The server then sends the custom instruction to the device, which then displays it to the user via notifications or in-app messages. For example, a smartphone notification could display a message like, "Take deep breaths to relax."

[0406] Specific examples

[0407] Example 1: A stressed user

[0408] When a user types "I'm very tired today" into their smartphone, the device sends this data to a server. The server uses an emotion engine to recognize that the user is feeling stressed and generates yoga poses and deep breathing exercises to help with relaxation. The generated guidance is sent to the device and notified to the user.

[0409] Example 2: Delighted users

[0410] When a user inputs "Today was a great day" through a wearable device, the device sends this data to the server. The server uses an emotion engine to recognize that the user is feeling joy and generates an activity to maintain that joy. The generated guidance is sent to the device and notified to the user.

[0411] Prompt Sentence Examples

[0412] "Generate a list of suggested relaxation exercises for users when they are feeling stressed."

[0413] "Generate a list of suggested activities for users who experience joy."

[0414] The above is a specific embodiment for carrying out the invention.

[0415] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0416] Step 1: Data collection

[0417] A user's smartphone or wearable device collects data through built-in sensors and applications. For example, a smartphone application records the user's diet, while a wearable device measures heart rate and steps. The input is data about the user's behavioral patterns and health status, and the output is the collected personal data.

[0418] Step 2: Send data

[0419] The data collected by the device is sent via the Internet to a server on the cloud. The data is encrypted before being sent, ensuring security. The input is the collected personal data, and the output is the data sent to the server.

[0420] Step 3: Save data

[0421] The server stores the received data in a database. A typical relational database is used for the database. The input is the data sent to the server, and the output is the data stored in the database.

[0422] Step 4: Data analysis

[0423] The server passes the stored data to an AI personal trainer, which analyzes it using machine learning algorithms. For example, TensorFlow is used to analyze the user's exercise patterns and dietary habits to assess their health. The input is the data stored in the database, and the output is the analysis results.

[0424] Step 5: Emotion Recognition

[0425] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data and determines the emotion using natural language processing technology. The input is the user's voice and text data, and the output is the recognized emotional state.

[0426] Step 6: Generate custom instruction

[0427] The server generates custom guidance based on the user's emotional state. For example, if it recognizes that the user is feeling stressed, it will suggest yoga poses or deep breathing exercises to help with relaxation. The input is the recognized emotional state and the analysis results, and the output is the generated custom guidance.

[0428] Step 7: Provide guidance

[0429] The server sends the generated custom instruction to the device and provides it to the user. The device displays the instruction to the user through notifications or in-app messages. For example, a smartphone notification function could be used to display a message such as "Take deep breaths to relax." The input is the generated custom instruction, and the output is the instruction provided to the user.

[0430] (Application example 1)

[0431] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0432] Conventional food delivery services do not offer personalized meal suggestions based on the user's health or emotional state, making it difficult to contribute to maintaining the user's health and emotional stability. Furthermore, there is a need to utilize users' personal data to provide more personalized services.

[0433] 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. In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means including an AI personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, means for recognizing the user's emotional state, means for proposing an optimal meal menu based on the recognized emotional state, and means for ordering the suggested meal menu from a food delivery service. This makes it possible to suggest optimal meals based on the user's health condition and emotional state, contributing to the user's maintenance of health and emotional stability.

[0434] "Individual personal data" refers to individual information such as a user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[0435] "Means of collection" refers to the function for obtaining users' personal data through smartphones and wearable devices.

[0436] "Means for analysis" refers to the function of analyzing collected personal data on a cloud server to evaluate the user's health condition and behavioral patterns.

[0437] "AI personal trainer providing customized instruction" refers to an artificial intelligence system that provides advice and guidance tailored to a user's individual goals and needs based on analysis results.

[0438] "Means for recognizing the user's emotional state" refers to a function for identifying the user's emotions using an emotion engine based on the user's personal data.

[0439] "Means for suggesting optimal meal menus" refers to a function for selecting and suggesting appropriate meal menus based on the user's health and emotional state.

[0440] "Means for ordering from a food delivery service" refers to the functionality for ordering suggested meal menus online from a food delivery service.

[0441] A system for implementing this invention collects personal data from users' smartphones and wearable devices and analyzes it on a cloud server. A specific embodiment of this system will be described below.

[0442] First, the system uses the user's smartphone or wearable device (e.g., smartwatch or fitness tracker) to collect personal data such as the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details. This is done using the smartphone's sensors and the wearable device's API.

[0443] The collected personal data is then sent to a cloud-based server, which uses machine learning libraries (e.g., TensorFlow) to analyze the collected data, assessing the user's health and behavioral patterns and providing custom guidance tailored to their individual goals and needs.

[0444] Furthermore, the server recognizes the user's emotional state using an emotion engine, which identifies the emotion the user is currently feeling (e.g., stress, joy, fatigue, etc.) based on the user's personal data.

[0445] Once the user's emotional state is recognized, the server will suggest the optimal meal plan based on that emotional state. For example, if the user is feeling stressed, the server will suggest a meal plan containing ingredients that have a relaxing effect. This suggestion is based on a nutrition database and past user preference data.

[0446] Finally, the suggested meal menu is ordered from a food delivery service, and the server sends the order via the food delivery service's API online, allowing users to easily receive the meal that best suits their health and emotional state.

[0447] For example, if a user is feeling stressed, the emotion engine will recognize this state and suggest a menu that includes relaxing chamomile tea and dark chocolate. This suggestion is made by inputting the following prompt into the generative AI model:

[0448] "Recommend optimal meal plans based on the user's health data and emotional state. If the user is stressed, suggest a meal plan that includes ingredients that have a relaxing effect."

[0449] In this way, a system is realized that can contribute to maintaining the user's health and emotional stability.

[0450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0451] Step 1:

[0452] A user's smartphone or wearable device collects personal data such as the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content. This is done using the smartphone's sensors and the wearable device's API. The input is the user's personal data, and the output is the collected data.

[0453] Step 2:

[0454] The device sends the collected personal data to a server on the cloud. The input is the collected personal data, and the output is the data sent to the server.

[0455] Step 3:

[0456] The server analyzes the received personal data. It uses a machine learning library (e.g., TensorFlow) to evaluate the user's health status and behavioral patterns. The input is the personal data sent to the server, and the output is the analysis results.

[0457] Step 4:

[0458] The server uses an emotion engine to recognize the user's emotional state. The emotion engine identifies the emotion the user is currently feeling (e.g., stress, joy, fatigue, etc.) based on the user's personal data. The input is the analysis result, and the output is the recognized emotional state.

[0459] Step 5:

[0460] The server proposes an optimal meal menu based on the recognized emotional state. It selects an appropriate meal menu using a nutrition database and past user preference data. The input is the recognized emotional state, the nutrition database, and past preference data, and the output is a suggested meal menu.

[0461] Step 6:

[0462] The server orders the suggested meal menu from a food delivery service. The order is sent online using the food delivery service's API. The input is the suggested meal menu, and the output is the order sent to the food delivery service.

[0463] Step 7:

[0464] A user receives a suggested meal from a food delivery service. The input is the delivery from the food delivery service, and the output is the meal received by the user.

[0465] Example 2

[0466] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0467] Conventional personal trainer systems can provide guidance based on the user's physical data and behavioral patterns, but they cannot provide advice that takes into account the user's emotional state. Therefore, there is a need for systems that provide optimal guidance and advice based on the user's emotional state.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0469] In this invention, the server includes means for collecting personal data of an individual, means for analyzing the collected personal data, means for providing customized instruction tailored to individual goals and needs based on the analysis results, means for collecting emotional states of the individual, means for analyzing the collected emotional states, and means for providing advice optimal for the emotional states based on the analysis results, thereby enabling comprehensive instruction and advice that takes into account both the user's physical data and emotional states.

[0470] "Individual personal data" refers to information relating to the user's physical and lifestyle habits, such as weight, diet, and amount of exercise.

[0471] The "means for collecting" is a function for transmitting data entered by a user through a terminal to a server.

[0472] The "analysis means" is a function for analyzing collected data and generating guidance based on the user's goals and needs.

[0473] The "means for providing customized guidance" is a function for suggesting optimal dietary content and exercise amount to the user based on the analysis results.

[0474] "Personal emotional state" is information about the emotions the user is feeling (e.g., sad, happy).

[0475] The "means for collecting emotional states" is a function for transmitting emotional data input by the user through the terminal to the server.

[0476] The "means for analyzing emotional state" is a function for analyzing collected emotional data and identifying the emotional state of the user.

[0477] The "means of providing advice optimal for the user's emotional state" is a function that suggests relaxation techniques and exercise plans according to the user's emotional state based on the analysis results.

[0478] "Behavioral patterns" are information about the user's daily actions and habits.

[0479] "Lifestyle" is information about the user's living environment and lifestyle habits.

[0480] MODE FOR CARRYING OUT THE INVENTION

[0481] This invention is a system that collects and analyzes personal data and emotional states of individuals to provide optimal customized guidance and advice to users. This system consists of three main components: a server, a terminal, and a user.

[0482] 1. Data Collection and Storage

[0483] The user inputs personal data such as weight, dietary habits, and exercise amount through the terminal. The terminal sends the input data to the server. The server stores the received data in a database (e.g., MySQL).

[0484] 2. Data Analysis

[0485] The server uses Python libraries to analyze the collected personal data and generate customized guidance based on the user's goals (e.g., dieting, maintaining health).

[0486] 3. Providing custom instruction

[0487] The server sends the generated custom instructions to the user's device, which then displays the received custom instructions to the user. The user can then confirm and implement the suggested diet and exercise regimen.

[0488] 4. Emotional Data Collection and Analysis

[0489] The user inputs their current emotional state (e.g., sad, happy) through the device. The device then sends the input emotional data to the server. The server then analyzes the emotional data using a natural language processing library. Based on the analysis results, the server generates advice that best suits the user's emotional state.

[0490] 5. Providing advice

[0491] The server sends the generated advice to the user's device, which then displays the received advice to the user. The user can then confirm and implement the suggested relaxation techniques and exercise plans.

[0492] Specific examples

[0493] For weight loss goals:

[0494] The user inputs their weight and dietary information.

[0495] The terminal transmits the input data to the server.

[0496] The server analyzes the data and suggests calorie restriction and exercise plans.

[0497] The server transmits the generated instruction content to the terminal, which displays it to the user.

[0498] For example: "Eat oatmeal for breakfast, a salad for lunch, and chicken breast and vegetables for dinner. Jogging 30 minutes each day."

[0499] If your emotional state is sad:

[0500] The user types "sad."

[0501] The terminal transmits the input data to the server.

[0502] The server analyzes emotional data and suggests relaxation techniques.

[0503] The server sends the generated advice to the terminal, which displays it to the user.

[0504] For example: "Try some deep breathing and yoga to help you relax."

[0505] Prompt Sentence Examples

[0506] Diet Goal Prompt:

[0507] "If the user is trying to lose weight, please suggest appropriate dietary and exercise regimes."

[0508] Prompt for sad emotional state:

[0509] "If the user is experiencing sadness, provide advice to alleviate that sadness."

[0510] In this way, comprehensive guidance and advice can be provided that takes into account both the user's physical data and emotional state.

[0511] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0512] Step 1:

[0513] The user inputs personal data such as weight, dietary habits, and exercise amount through the terminal.

[0514] Input: The user inputs data such as weight, diet, and exercise.

[0515] What happens: A user opens a smartphone app, enters their weight, and records what they had for breakfast.

[0516] Output: The entered data is saved on the device.

[0517] Step 2:

[0518] The terminal transmits the input data to the server.

[0519] Input: Personal data entered by the user.

[0520] Specific operation: The app sends the entered data to the server in real time.

[0521] Output: Data is sent to the server.

[0522] Step 3:

[0523] The server stores the received data in a database.

[0524] Input: Personal data sent from the device.

[0525] What happens: The server adds a new record to the database, storing the user's weight and dietary information.

[0526] Output: Personal data stored in a database.

[0527] Step 4:

[0528] The server uses Python libraries to analyze the collected data.

[0529] Input: Personal data stored in a database.

[0530] Specific operation: The server reads data from the database, converts it into a data frame, and performs statistical analysis.

[0531] Output: Analysis results.

[0532] Step 5:

[0533] The server generates custom instruction based on the analysis results and is tailored to the user's goals.

[0534] Input: Analysis results.

[0535] What it does: The server calculates the user's calorie consumption and generates an appropriate meal plan and exercise plan.

[0536] Output: Custom instruction.

[0537] Step 6:

[0538] The server transmits the generated custom instruction to the user's terminal.

[0539] Input: Custom Instruction.

[0540] Specific operation: The server generates instruction content and sends it to the terminal in JSON format.

[0541] Output: Custom instruction sent to the terminal.

[0542] Step 7:

[0543] The terminal displays the received custom instruction to the user.

[0544] Input: Custom instructions sent from the server.

[0545] What it does: The app parses the data it receives and displays it in a user-friendly format.

[0546] Output: The custom instruction displayed to the user.

[0547] Step 8:

[0548] The user inputs his / her current emotional state through the terminal.

[0549] Input: The emotion the user is feeling (e.g. sad, happy).

[0550] Specific action: The user enters "sad" into an emotion input form within the app.

[0551] Output: The input emotion data is saved on the device.

[0552] Step 9:

[0553] The terminal transmits the input emotion data to the server.

[0554] Input: Emotion data entered by the user.

[0555] Specific operation: The app sends the input emotion data to the server in real time.

[0556] Output: Data is sent to the server.

[0557] Step 10:

[0558] The server uses a natural language processing library to analyze the sentiment data.

[0559] Input: Emotion data sent from the device.

[0560] Specific operation: The server performs text analysis of the emotion data to identify the user's emotional state.

[0561] Output: Emotion analysis results.

[0562] Step 11:

[0563] Based on the analysis results, the server generates advice that is best suited to the user's emotional state.

[0564] Input: Sentiment analysis results.

[0565] Specific actions: The server generates relaxation techniques and exercise plans according to the user's emotional state.

[0566] Output: Emotion-based advice.

[0567] Step 12:

[0568] The server transmits the generated advice to the user's terminal.

[0569] Input: Emotion-based advice.

[0570] Specific operation: The server sends the generated advice in JSON format to the device.

[0571] Output: Advice sent to the terminal.

[0572] Step 13:

[0573] The terminal displays the received advice to the user.

[0574] Input: Advice sent by the server.

[0575] What it does: The app parses the data it receives and displays it in a user-friendly format.

[0576] Output: The advice displayed to the user.

[0577] (Application example 2)

[0578] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0579] While conventional personal trainer systems can provide customized training based on a user's goals and needs, they are unable to provide advice or guidance that takes into account the user's emotional state. Furthermore, there is a lack of means to provide customized training based on a user's health status and goals in physical stores. This has resulted in insufficient comprehensive health management and emotional support for users.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0581] In this invention, the server includes a means for collecting personal data of individuals, a means for analyzing the collected personal data, an AI personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, a means for analyzing the user's emotional state, a means for providing optimal advice and guidance based on the emotional state, a means for providing customized instruction based on the user's health status and goals in a physical store, and a means for including an application to be installed on a smartphone. This makes it possible to provide not only customized instruction based on the user's goals and needs, but also advice and guidance based on the user's emotional state. Furthermore, comprehensive support for the user's health management can be provided even in a physical store.

[0582] "Individual personal data" refers to individual information such as a user's health status, behavioral patterns, lifestyle habits, and emotional state.

[0583] "Means of collection" refers to devices and software such as sensors, input devices, and applications used to obtain personal data from users.

[0584] "Analytical tools" refers to the algorithms and software used to analyze collected personal data and understand users' goals and needs.

[0585] "Customized Coaching" refers to personalized advice and guidance tailored to a user's individual goals and needs.

[0586] "AI personal trainer" refers to a system that uses artificial intelligence to analyze a user's personal data and provide customized instruction.

[0587] "Emotional state" refers to a user's current emotional or psychological state.

[0588] "Means for analyzing emotional state" refers to sensors and algorithms for detecting and analyzing the user's emotional state.

[0589] "Optimal advice and guidance" refers to the most appropriate instruction and support provided based on the user's goals and emotional state.

[0590] "Brick and mortar" refers to physical stores that users can visit in person, such as fitness gyms or health food stores.

[0591] "Applications installed on smartphones" refers to software that is downloaded and executed on a user's smartphone.

[0592] This invention is a system that collects and analyzes personal data of individuals to provide customized guidance tailored to the user's goals and needs. Furthermore, it can analyze the user's emotional state and provide optimal advice and guidance based on that emotional state. This system can also provide customized guidance based on the user's health status and goals in brick-and-mortar stores.

[0593] System configuration

[0594] The system consists of the following major components:

[0595] 1. Personal data collection methods:

[0596] This includes sensors and input devices that collect data on a user's health, behavioral patterns, lifestyle habits, emotional state, etc. Examples include smartphone applications and wearable devices.

[0597] 2. Data analysis methods:

[0598] It includes algorithms and software that analyze collected personal data to understand users' goals and needs, and is implemented using programming languages ​​such as Python.

[0599] 3. AI Personal Trainer:

[0600] A system that uses artificial intelligence to analyze users' personal data and provide custom guidance. It uses generative AI models to generate the best advice for users.

[0601] 4. Emotional state analysis tools:

[0602] It includes sensors and algorithms to detect and analyze a user's emotional state, for example, by using a smartphone's camera and microphone to analyze emotions.

[0603] 5. Custom in-store instruction:

[0604] This system allows users to receive customized guidance based on their health status and goals at physical locations such as fitness gyms and health food stores, and is implemented through an application installed on a smartphone.

[0605] Program processing explanation

[0606] The server receives the personal data collected from the user and analyzes it using a data analysis means. Based on the analysis results, the AI ​​personal trainer generates customized instruction tailored to the user's goals and needs. Furthermore, the server analyzes the user's emotional state using an emotional state analysis means and provides optimal advice and guidance based on the user's emotional state.

[0607] The hardware used includes smartphones, wearable devices, sensors, etc. The software includes data analysis algorithms using Python, generative AI models, and smartphone applications.

[0608] Specific examples

[0609] For example, if a user sets a goal of "dieting" and feels "sad," the system operates as follows:

[0610] 1. The user sets a goal of "dieting" through a smartphone application.

[0611] 2. The emotional state analysis means analyzes the user's emotional state as "sadness."

[0612] 3. The AI ​​personal trainer generates fitness guidance such as, "I recommend a low-calorie diet and aerobic exercise."

[0613] 4. Offer emotional support based on their emotional state: "Try yoga or meditation to relax."

[0614] Example prompt for a generative AI model:

[0615] The user's goal is to lose weight and they are currently experiencing sadness. Provide them with appropriate fitness guidance and emotional support.

[0616] By inputting this prompt into a generative AI model, optimal advice can be generated for the user.

[0617] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0618] Step 1:

[0619] A user starts a smartphone application and sets a goal (e.g., dieting).

[0620] Input: The target data that the user enters into the application.

[0621] Output: The set target data is sent to the server.

[0622] Specific actions: The user selects a goal such as "diet" through the application interface and presses the submit button.

[0623] Step 2:

[0624] The server receives the user's goal data and stores it in a database.

[0625] Input: Goal data submitted by the user.

[0626] Output: Goal data stored in a database.

[0627] Specific operation: The server receives the HTTP request and stores the target data in the database.

[0628] Step 3:

[0629] The user inputs the emotional state through a smartphone application, or the emotional state analysis means automatically detects the emotion.

[0630] Input: Emotional data entered by the user or detected by a sensor.

[0631] Output: The detected or input emotion data is sent to the server.

[0632] Specific behavior: The user selects an emotion, such as "sadness," or the smartphone's camera or microphone detects the emotion.

[0633] Step 4:

[0634] The server receives the emotion data and stores it in a database.

[0635] Input: Emotion data submitted by the user.

[0636] Output: Emotion data stored in a database.

[0637] Specific operation: The server receives an HTTP request and stores the emotion data in a database.

[0638] Step 5:

[0639] The server generates a prompt sentence for the generative AI model based on the goal data and emotion data.

[0640] Input: Goal data and emotion data stored in a database.

[0641] Output: The generated prompt statement.

[0642] Specific behavior: The server combines the goal data and emotion data to generate a prompt sentence: "The user's goal is to lose weight and they are currently feeling sad. Please provide appropriate fitness guidance and emotional support."

[0643] Step 6:

[0644] The server inputs prompts into the generative AI model to generate optimal advice and guidance.

[0645] Input: The generated prompt statement.

[0646] Output: Advice or guidance from the generative AI model.

[0647] How it works: The server sends a prompt to the generative AI model, which then generates advice such as "We recommend a low-calorie diet and aerobic exercise" or "Try yoga or meditation to relax."

[0648] Step 7:

[0649] The server sends the generated advice and guidance to the user's smartphone application.

[0650] Input: Advice or guidance from a generative AI model.

[0651] Output: Advice and guidance displayed in the user's smartphone application.

[0652] What it does: The server sends advice and guidance to the user's smartphone via an HTTP response, which is then displayed by the application.

[0653] Step 8:

[0654] The user acts according to the advice and guidance and enters the results into a smartphone application.

[0655] Input: Data that the user enters as the result of an action.

[0656] Output: Action result data sent to the server.

[0657] Specific operation: The user enters the results of the exercise and diet he or she performed through the application interface and presses the submit button.

[0658] Step 9:

[0659] The server receives the behavior result data and stores it in a database.

[0660] Input: Action result data sent by the user.

[0661] Output: Behavioral outcome data stored in a database.

[0662] Specific operation: The server receives the HTTP request and stores the action result data in the database.

[0663] Step 10:

[0664] The server periodically analyzes the user's personal data and generates new advice and guidance as needed.

[0665] Input: Personal data stored in a database.

[0666] Output: The newly generated advice or guidance.

[0667] How it works: The server periodically scans the database and sends prompts to the generative AI model based on the latest data to generate new advice and guidance.

[0668] Example 3

[0669] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0670] Conventional personal trainer systems have difficulty fully understanding a user's behavioral patterns and lifestyle and providing optimal advice and guidance based on that. They also lack the functionality to track a user's emotional state and provide customized instruction in response to those changes. This has led to the issue of being unable to provide effective instruction tailored to the individual needs of each user.

[0671] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for preprocessing the collected personal data, means for analyzing the preprocessed data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for tracking the user's emotional state and analyzing changes therein, and means for providing customized guidance based on changes in the emotional state. This makes it possible to understand the user's behavioral patterns and living situation and provide optimal advice and guidance based thereon. Furthermore, tracking the user's emotional state and providing customized guidance in response to changes therein enables effective guidance tailored to the user's individual needs.

[0672] "Individual personal data" refers to individual information such as a user's behavioral patterns, living situation, emotional state, exercise history, and dietary history.

[0673] "Means of collection" refers to the function that allows users to input data through dedicated applications or devices and send that data to a server.

[0674] "Preprocessing means" refers to functions for imputing missing values ​​in collected data, normalizing data, and detecting and correcting outliers.

[0675] "Means of analysis" refers to generative AI models and algorithms that use pre-processed data to analyze users' behavioral patterns and lifestyles.

[0676] "Means for providing customized guidance" refers to functionality that generates and provides advice and guidance tailored to a user's individual goals and needs based on the analysis results.

[0677] "Means for tracking emotional state" refers to a function for periodically recording a user's emotional state and transmitting that data to a server.

[0678] "Means for analyzing changes in emotional state" refers to a function that uses an emotion engine to analyze changes in a user's emotional state and provide customized guidance based on the results.

[0679] MODE FOR CARRYING OUT THE INVENTION

[0680] This invention relates to an AI personal trainer system that collects and analyzes personal data of individuals and provides customized instruction. Specific embodiments of this system will be described below.

[0681] Hardware and Software Configuration

[0682] The server uses high-performance hardware, specifically a GPU-equipped server, and uses deep learning frameworks such as TensorFlow and natural language processing libraries for software.

[0683] Data collection

[0684] Users input data about their behavioral patterns and lifestyles through a dedicated application, such as exercise history, diet history, sleep time, and emotional state. The device then transmits this data to a server in real time.

[0685] Data Preprocessing

[0686] The server preprocesses the received user data by filling in missing values, normalizing the data, and detecting and correcting outliers. For example, if there are missing values ​​in the user's diet history, the server fills them in based on past data.

[0687] Analyzing the data

[0688] The server uses the preprocessed data to analyze the user's behavioral patterns and lifestyle. Specifically, it uses a generative AI model (e.g., a deep learning model using TensorFlow) to cluster the user's behavioral patterns and classify their lifestyle. For example, it determines whether the user has a night-owl lifestyle.

[0689] Providing custom instruction

[0690] The server generates optimal advice and guidance based on the analysis results. Specifically, it inputs prompts into the generative AI model to suggest exercise times and meal menus suitable for the user. For example, the prompt might read, "The user has a nocturnal lifestyle and gluten-free diet. Please suggest suitable exercise times and gluten-free meal menus suitable for the night."

[0691] Tracking and analyzing emotional states

[0692] The device periodically records the user's emotional state and sends it to a server. The server uses an emotion engine to track the user's emotional state over time and analyzes changes. For example, if the user has been feeling stressed for a certain period of time, the server can use that data to identify the cause of the stress.

[0693] Providing custom guidance (based on emotional state)

[0694] The server provides customized guidance based on the analysis results of the emotion engine. Specifically, if the user is feeling stressed, it suggests a long-term plan for stress management. For example, it provides guidance such as, "The user has shown high stress levels over the past month. To manage stress, we suggest increasing daily meditation time and engaging in relaxation activities once a week."

[0695] In this way, a system is realized in which the server, the terminal, and the user work together to provide the user with the most suitable advice and guidance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0696] Step 1: Collect user data

[0697] Users input data about their behavioral patterns and lifestyle through a dedicated application. For example, they input information such as exercise history, diet history, sleep time, and emotional state. The device then transmits this data to a server in real time. The input data is information about the user's behavioral patterns and lifestyle, and the output is the raw data transmitted to the server.

[0698] Step 2: Preprocessing the data

[0699] The server preprocesses the received user data. Specifically, it performs missing value imputation, data normalization, and outlier detection and correction. For example, if there are missing values ​​in the diet history entered by the user, the server imputes them based on past data. The input data is raw data, and the output is preprocessed, clean data.

[0700] Step 3: Analyze your behavioral patterns and living situations

[0701] The server uses the preprocessed data to analyze the user's behavioral patterns and lifestyle. Specifically, it uses a generative AI model (e.g., a deep learning model using TensorFlow) to cluster the user's behavioral patterns and classify their lifestyle. For example, it determines whether the user has a night-owl lifestyle. The input data is preprocessed, clean data, and the output is the analysis results.

[0702] Step 4: Generate optimal advice and guidance

[0703] The server generates optimal advice and guidance based on the analysis results. Specifically, it inputs prompt statements into the generative AI model to suggest exercise times and meal menus suitable for the user. For example, the prompt statement is input as follows: "The user has a nocturnal lifestyle and has gluten-free dietary restrictions. Please suggest appropriate exercise times and gluten-free meal menus suitable for nighttime." The input data are the analysis results and prompt statements, and the output is the generated advice and guidance.

[0704] Step 5: Track and analyze your emotional state

[0705] The device periodically records the user's emotional state and sends it to the server. The server uses an emotion engine to track the user's emotional state over time and analyzes changes. For example, if a user has been feeling stressed for a certain period of time, the data can be used to identify the cause of the stress. The input data is a record of the emotional state, and the output is the analysis of the emotional state.

[0706] Step 6: Providing custom guidance (based on emotional state)

[0707] The server provides custom guidance based on the analysis results of the emotion engine. Specifically, if the user is feeling stressed, it suggests a long-term plan for stress management. For example, it provides guidance such as, "The user has shown high stress levels over the past month. To manage stress, we suggest increasing daily meditation time and engaging in relaxation activities once a week." The input data is the analysis result of the emotional state, and the output is custom guidance.

[0708] (Application example 3)

[0709] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0710] Conventional food delivery services do not offer personalized meal suggestions based on the user's behavioral patterns, lifestyle, or emotional state, making it difficult to provide meals optimal for the user's health and emotional state. Furthermore, there is a lack of meal suggestions tailored to specific dietary restrictions or lifestyle habits, making it difficult to improve user satisfaction. Furthermore, there are no meal suggestions for stress management based on the user's emotional state, resulting in insufficient overall health management for the user.

[0711] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0712] In this invention, the server includes means for collecting personal data of an individual, means for analyzing the collected personal data, means including an artificial intelligence personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, means for understanding the individual's behavioral patterns, habits, and living situation, means for providing optimal advice and guidance based on the understood behavioral patterns, habits, and living situation, means for tracking the individual's emotional state and analyzing changes therein, means for providing customized instruction corresponding to changes in the emotional state, and means for a food delivery service that makes optimal meal suggestions based on the user's behavioral patterns, living situation, and emotional state. This makes it possible to make optimal meal suggestions based on the user's behavioral patterns, living situation, and emotional state, and to provide meals that are optimal for the user's health and emotional state.

[0713] "Individual personal data" refers to individual information such as an individual's behavioral patterns, living situation, and emotional state.

[0714] "Means of collection" refers to the devices and methods used to obtain an individual's personal data.

[0715] "Means for analysis" refers to devices and methods for analyzing collected personal data and extracting meaningful information.

[0716] "Artificial Intelligence Personal Trainer" refers to an artificial intelligence system that provides custom coaching tailored to individual goals and needs.

[0717] "Behavioral patterns" refer to an individual's daily behavioral tendencies and habits.

[0718] "Living conditions" refers to an individual's living environment and lifestyle.

[0719] "Emotional state" refers to an individual's current emotional or mood state.

[0720] "Tracking means" refers to devices or methods for continuously monitoring an individual's emotional state.

[0721] "Means for analyzing changes" refers to devices or methods for analyzing changes in emotional state and understanding their meaning.

[0722] "Customized instruction" refers to specific advice and guidance tailored to individual goals and needs.

[0723] "Food delivery service" refers to a service that delivers meals ordered by users to designated locations.

[0724] "Meal suggestions" refers to recommending optimal meals based on a user's behavioral patterns, lifestyle, and emotional state.

[0725] A system for implementing the present invention is configured as follows.

[0726] System Program

[0727] The server runs a program that collects and analyzes personal data, including an AI personal trainer that understands the user's behavioral patterns, lifestyle, and emotional state and then makes optimal dietary recommendations based on that information.

[0728] Hardware and software used

[0729] Hardware: Smartphones, servers

[0730] Software: Python, EmotionEngine

[0731] Data processing and calculation

[0732] 1. User profile acquisition: The server acquires the user's behavioral patterns, life situation, and emotional state using smartphone sensors and user input data.

[0733] 2. Get the current time: The server gets the current time using the datetime module.

[0734] 3. Meal suggestion logic: The server suggests optimal meals based on the user profile and the current time, taking into account the user's lifestyle habits, dietary restrictions, and emotional state.

[0735] 4. Using Emotion Engine: The server uses the EmotionEngine class to analyze the user's emotional state and detect stress state.

[0736] Specific examples

[0737] User profile: A user with a night owl lifestyle, gluten-free diet, and who has recently been feeling stressed.

[0738] Suggested meals: A healthy salad bowl, gluten-free pasta, and avocado toast with chamomile tea to help reduce stress, even late at night.

[0739] Prompt Sentence Examples

[0740] Create a Python program that takes a user's behavioral patterns, lifestyle, and emotional state as input and makes optimal meal recommendations. If the user has a nocturnal lifestyle, suggest healthy meals even late at night. If the user has specific dietary restrictions, suggest meals that fit those restrictions. Also, if the emotion engine detects the user's stress level, suggest meals that will help reduce stress.

[0741] This system makes it possible to suggest optimal meals based on the user's behavioral patterns, lifestyle, and emotional state, providing meals that are optimal for the user's health and emotional state.

[0742] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0743] Step 1:

[0744] The server collects personal data of the user.

[0745] Input: User behavioral patterns, life situations, emotional state

[0746] Output: Collected personal data

[0747] Specific operation: Using smartphone sensors and user input data, the system collects user behavioral patterns (e.g., exercise history, sleep time), lifestyle conditions (e.g., dietary history, daily rhythm), and emotional state (e.g., stress level, mood).

[0748] Step 2:

[0749] The server analyzes the collected personal data.

[0750] Input: Collected personal data

[0751] Output: Analysis results (understanding user behavior patterns, life situations, and emotional states)

[0752] What it does: Uses Python to analyze collected data and apply statistical analysis and machine learning models to understand users' behavioral patterns, life situations, and emotional states.

[0753] Step 3:

[0754] The server gets the current time.

[0755] Input: None

[0756] Output: Current time

[0757] Specific behavior: Uses the datetime module to get the current time.

[0758] Step 4:

[0759] The server makes optimal meal suggestions based on the user's behavioral patterns and living situation.

[0760] Input: Analysis results, current time

[0761] Output: Optimal meal suggestions

[0762] What it does: If the user has a night owl lifestyle, it will suggest healthy meals even late at night, and if the user has specific dietary restrictions, it will suggest menu items that fit those restrictions, such as a healthy salad bowl or gluten-free pasta even late at night.

[0763] Step 5:

[0764] The server uses an emotion engine to analyze the user's emotional state.

[0765] Input: A history of the user's emotional state

[0766] Output: Analysis of changes in emotional state

[0767] Specific behavior: Uses the EmotionEngine class to analyze the user's emotional state history and detect stress levels.

[0768] Step 6:

[0769] The server provides custom instruction that responds to changes in emotional state.

[0770] Input: Analysis of changes in emotional state

[0771] Output: Meal suggestions for stress management

[0772] What it does: If the user is feeling stressed, suggest a meal that will help reduce stress (e.g., chamomile tea and avocado toast).

[0773] Step 7:

[0774] The server notifies the user of the final meal suggestions.

[0775] Input: Optimal dietary suggestions, dietary suggestions for stress management

[0776] Output: User notification

[0777] Specific operation: Notifies users of optimal meal suggestions via a smartphone app.

[0778] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0779] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0781] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0782] [Second embodiment]

[0783] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0784] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0785] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0786] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0787] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0788] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0789] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0790] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0791] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0792] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0793] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0794] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0795] "Example 1"

[0796] In one embodiment of the present invention, the AI ​​personal trainer collects personal data from the user's smartphone or wearable device. The collected data covers a wide range of topics, including the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details. This data is sent to a cloud server and analyzed by the AI ​​personal trainer.

[0797] "Example 2"

[0798] The AI ​​personal trainer uses the collected data to provide customized guidance tailored to the user's goals and needs. For example, if a user is trying to lose weight, it will suggest appropriate dietary and exercise regimes. If a user is trying to maintain their health, it will suggest appropriate lifestyle and exercise habits.

[0799] "Example 3"

[0800] Furthermore, the AI ​​personal trainer understands the user's behavioral patterns and lifestyle and provides optimal advice and guidance based on that understanding. For example, if the user has a nocturnal lifestyle, it will suggest exercise and meal times that suit that lifestyle. Also, if the user has specific dietary restrictions, it will suggest meals that fit those restrictions.

[0801] The processing flow of each embodiment will be described below.

[0802] "Example 1"

[0803] Step 1: Collect personal data from the user's smartphone or wearable device. The collected data covers a wide range of topics, including the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details.

[0804] Step 2: The collected data is sent to a server in the cloud.

[0805] Step 3: The AI ​​personal trainer analyzes the data sent to the cloud server.

[0806] "Example 2"

[0807] Step 1: The AI ​​personal trainer uses the collected data to provide custom coaching tailored to the user's goals and needs.

[0808] Step 2: For example, if the user is trying to lose weight, the app will suggest appropriate dietary and exercise regimes.

[0809] Step 3: If the user is aiming to maintain their health, the app will suggest appropriate lifestyle and exercise habits.

[0810] "Example 3"

[0811] Step 1: The AI ​​personal trainer understands the user's behavioral patterns and lifestyle.

[0812] Step 2: Use that understanding to provide the best advice and guidance.

[0813] Step 3: For example, if the user has a nocturnal lifestyle, the system will suggest exercise and meal times that fit that lifestyle. Also, if the user has specific dietary restrictions, the system will suggest meals that fit those restrictions.

[0814] Example 1

[0815] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0816] Conventional personal trainer systems have difficulty fully understanding an individual's behavioral patterns and lifestyle habits and providing appropriate advice. Furthermore, data collection and analysis are often done manually, making it difficult to provide efficient feedback.

[0817] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to a server on a cloud, means for storing the transmitted data in a database, means for analyzing the stored data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for transmitting generated feedback to a terminal, and means for displaying the feedback on the terminal. This makes it possible to automatically collect and analyze an individual's behavioral patterns and lifestyle habits and provide efficient and appropriate feedback.

[0818] "Individual personal data" refers to information such as an individual's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[0819] "Means of collection" refers to methods and devices for obtaining personal data of individuals using smartphones or wearable devices.

[0820] "Cloud servers" refer to remote servers accessible via the Internet that provide infrastructure for storing and processing data.

[0821] "Transmitting means" refers to a method or device for transferring collected data to a server on the cloud via the Internet.

[0822] "Means for storing data in a database" refers to a system or method for storing received data in a structured format.

[0823] "Means of analysis" refers to the algorithms and software used to analyze stored data and assess an individual's behavioral patterns and health status.

[0824] "Means for providing customized instruction" refers to a method or apparatus for generating advice or guidance based on the analysis results that is tailored to individual goals and needs.

[0825] "Means for sending feedback" refers to a method or device for transferring the generated feedback to the terminal.

[0826] "Means for displaying feedback" refers to a method or device for visually presenting feedback to a user at a terminal.

[0827] This invention relates to an AI personal trainer system that collects and analyzes personal data of individuals and provides customized instruction tailored to their individual goals and needs. Specific embodiments of this system will be described below.

[0828] Users use smartphones and wearable devices to collect personal data such as behavioral patterns, lifestyle habits, health status, exercise volume, and dietary details. Specifically, they use smartphone applications and wearable devices. These devices record data such as the number of steps taken, heart rate, sleep patterns, and dietary details.

[0829] The device sends the collected data to a cloud server. The data is encrypted and sent over an internet connection. For example, a smartphone application sends step count data to a cloud server.

[0830] The server stores the received data in a database. Specifically, the server uses a database system such as MySQL to store the user's data in a structured format.

[0831] The server then passes the stored data to an AI personal trainer for analysis. The AI ​​personal trainer uses machine learning libraries such as Python and TensorFlow to analyze the data. For example, the server inputs the user's step count data into an AI model to evaluate the user's exercise progress.

[0832] The server generates feedback based on the analysis results. For example, an AI personal trainer might generate feedback such as, "You haven't reached your goal of 10,000 steps today. I recommend walking another 2,000 steps."

[0833] The generated feedback is sent from the server to the device, which then displays the feedback to the user. For example, a smartphone application might display a notification to the user saying, "You haven't reached your goal of 10,000 steps today. We recommend that you walk another 2,000 steps."

[0834] Specific examples

[0835] Example 1: Exercise advice

[0836] Suppose a user uses a wearable device to record the number of steps they take each day. The wearable device collects the user's step count data and sends it to a smartphone application. The application sends this data to a server on the cloud, which stores the data in a database. The AI ​​personal trainer analyzes the stored data and generates feedback for the user, such as, "You haven't reached your goal of 10,000 steps today. I recommend walking another 2,000 steps." The server sends this feedback to the smartphone application, which then displays a notification to the user.

[0837] Example 2: Dietary advice

[0838] Suppose a user is using a smartphone application to record their diet. The user enters their dietary information into the application, which then sends this data to a server in the cloud. The server stores the data in a database, and an AI personal trainer analyzes the data. As a result of the analysis, feedback is generated for the user, such as "Today's meal lacks vegetables. We recommend adding a salad to your next meal." The server then sends this feedback to the smartphone application, which then displays a notification to the user.

[0839] Prompt Sentence Examples

[0840] "Please describe an AI personal trainer system that analyzes a user's behavioral patterns and health status and generates appropriate exercise and dietary advice. Please include the names of any specific devices or software."

[0841] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0842] Step 1: Data collection

[0843] Users use smartphones or wearable devices to collect personal data such as behavioral patterns, lifestyle habits, health status, exercise amount, and dietary details. Specifically, they use smartphone applications or wearable devices. The input is the user's behavioral data, and the output is the collected personal data.

[0844] Step 2: Send data

[0845] The device sends the collected data to a server in the cloud. At this time, the data is encrypted and sent via an internet connection. Specifically, the smartphone application sends step count data to the cloud server. The input is the collected personal data, and the output is the data sent to the server.

[0846] Step 3: Save data

[0847] The server stores the received data in a database. Specifically, the server uses a database system such as MySQL to store the user's data in a structured format. The input is the data sent to the server, and the output is the data stored in the database.

[0848] Step 4: Data analysis

[0849] The server passes the stored data to the AI ​​personal trainer for analysis. The AI ​​personal trainer analyzes the data using machine learning libraries such as Python and TensorFlow. Specifically, the server inputs the user's step count data into an AI model to evaluate exercise progress. The input is the data stored in the database, and the output is the analysis results.

[0850] Step 5: Feedback generation

[0851] The server generates feedback based on the analysis results. Specifically, the AI ​​personal trainer generates feedback such as, "You haven't reached your goal of 10,000 steps today. I recommend you walk another 2,000 steps." The input is the analysis results, and the output is the generated feedback.

[0852] Step 6: Send your feedback

[0853] The server sends the generated feedback to the device. Specifically, the server sends the generated feedback to the smartphone application. The input is the generated feedback, and the output is the feedback sent to the device.

[0854] Step 7: Feedback display

[0855] The device displays feedback to the user. Specifically, a smartphone application displays a notification to the user saying, "You haven't reached your goal of 10,000 steps today. We recommend you walk another 2,000 steps." The input is the feedback sent to the device, and the output is the feedback displayed to the user.

[0856] (Application example 1)

[0857] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0858] While existing personal trainer systems can provide guidance based on an individual's health status and lifestyle habits, they lack the ability to provide meal plans or link with food delivery services, making it difficult for users to easily consume healthy meals. Furthermore, there is also the issue of not being able to fully meet the needs of users due to a lack of data analysis to generate individual meal plans.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0860] In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for generating an optimal meal plan based on the analysis results, and means for providing meals based on the generated meal plan. This makes it possible to provide an optimal meal plan based on the user's health condition and lifestyle habits, and to link with food delivery services.

[0861] "Individual personal data" refers to information such as an individual's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[0862] "Means of collection" refers to methods of obtaining individuals' personal data using smartphones, wearable devices, etc.

[0863] "Means of analysis" refers to the method of sending collected personal data to a server on the cloud and having AI analyze the data.

[0864] "Means for providing customized instruction" refers to methods for providing advice and guidance tailored to individual goals and needs based on the analysis results.

[0865] "Method of generating meal plans" refers to the method by which the AI ​​creates optimal meal plans for users based on the analysis results.

[0866] "Means of providing meals" refers to the method of delivering meals to users through affiliated food delivery services based on the generated meal plan.

[0867] The system for implementing this invention includes a series of processes for collecting and analyzing personal data of an individual, generating an optimal meal plan, and providing meals through a food delivery service. A specific embodiment of this system is described below.

[0868] First, personal data is collected from the user's smartphone or wearable device. This data includes the user's behavioral patterns, lifestyle habits, health status, amount of exercise, dietary content, etc. This data is obtained, for example, using sensors in the smartphone or the data collection function of the wearable device.

[0869] The collected data is then sent to a server in the cloud. The server can be a cloud service such as AWS (Amazon Web Services). On the server, the collected data is analyzed using AI. The AI ​​model uses a machine learning framework such as TensorFlow.

[0870] Based on the analysis results, the server generates an optimal meal plan for the user. This meal plan is customized based on the user's health condition and lifestyle habits. The generated meal plan is sent to a partner food delivery service. The food delivery service can accept orders via API communication, for example.

[0871] For example, a user opens an app on their smartphone and enters their daily activity data and food preferences. The app sends this data to the cloud, where AI analyzes it. The AI ​​then generates a meal plan optimized for the user and sends an order to partner restaurants. The user can then receive healthy meals at home.

[0872] Examples of prompts for generative AI models include:

[0873] "Generate optimal meal plans by taking into account the user's activity level, food preferences, health status, and daily calorie intake."

[0874] In this way, it becomes possible to provide optimal meal plans based on the user's health condition and lifestyle habits, and to link with food delivery services.

[0875] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0876] Step 1:

[0877] Users collect personal data using smartphones and wearable devices.

[0878] Input: Data such as user behavior patterns, lifestyle habits, health status, exercise amount, and dietary details.

[0879] Data processing: Acquire data from sensors on smartphones and wearable devices and convert it into an appropriate format.

[0880] Output: Collected personal data.

[0881] Step 2:

[0882] The device sends the collected personal data to a server on the cloud.

[0883] Input: Collected personal data.

[0884] Data processing: The data is packaged using the appropriate protocol (e.g. HTTPS) to send it to the cloud server.

[0885] Output: Personal data sent to cloud server.

[0886] Step 3:

[0887] The server analyzes the received personal data.

[0888] Input: Personal data sent to the cloud server.

[0889] Data Computing: Using AI models (e.g., TensorFlow) to analyze data and assess the user's health and lifestyle.

[0890] Output: Analysis results (e.g., user's health status, lifestyle assessment).

[0891] Step 4:

[0892] The server generates an optimal meal plan based on the analysis results.

[0893] Input: Analysis results.

[0894] Data Computation: Generative AI models are used to generate optimal meal plans based on the user's health and lifestyle habits.

[0895] Output: The generated meal plan.

[0896] Step 5:

[0897] The server sends the generated meal plan to a partner food delivery service.

[0898] Input: The generated meal plan.

[0899] Data processing: Convert the meal plan into a format suitable for the food delivery service's API and send it.

[0900] Output: Meal plan sent to food delivery service.

[0901] Step 6:

[0902] The food delivery service prepares and delivers meals to users based on their meal plan.

[0903] Input: The meal plan sent to the food delivery service.

[0904] What happens: The food delivery service prepares the meal and delivers it to you at the specified time and location.

[0905] Output: The meal delivered to the user.

[0906] In this way, optimal meal plans based on the user's health condition and lifestyle habits can be provided, and collaboration with food delivery services can be achieved.

[0907] Example 2

[0908] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0909] Conventional personal trainer systems lack the data collection and analysis required to provide customized training tailored to individual goals and needs, resulting in insufficient individualized training. Additionally, it was difficult to understand the user's behavioral patterns, habits, and lifestyle, and provide optimal advice based on that.

[0910] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0911] In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to the server, means for the server to store the received data in a database, means for the server to analyze the data using a generative AI model, means for the server to generate customized instruction tailored to individual goals and needs based on the analysis results, means for transmitting the generated customized instruction to a terminal, and means for the terminal to display the customized instruction to the user. This makes it possible to provide highly accurate customized instruction tailored to the user's goals and needs.

[0912] "Individual personal data" refers to information about an individual, such as the user's goals, weight, height, diet, and exercise habits.

[0913] A "server" is a computer system that stores data, analyzes it, runs generative AI models, etc.

[0914] A "terminal" is a device (e.g., smartphone, tablet, PC) that a user uses to enter data and receive customized instruction.

[0915] A "database" is a system that allows a server to store and manage users' personal data.

[0916] A "generative AI model" is an artificial intelligence model that analyzes user data and generates custom instruction.

[0917] "Customized instruction" is personalized advice and guidance generated based on a user's goals and needs.

[0918] A "prompt" is a textual instruction to be input to a generative AI model.

[0919] An "HTTP POST request" is a type of communication protocol that allows a terminal to send data to a server.

[0920] An "INSERT query" is an SQL statement used to add new data to a database.

[0921] A "SELECT query" is an SQL statement used to retrieve data from a database.

[0922] This invention is a system that collects personal data of an individual, analyzes it using a generative AI model, and provides customized instruction tailored to the user's goals and needs. Specific embodiments of this system are described below.

[0923] Hardware and software used

[0924] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0925] Software: Generative AI models (e.g., GPT-4), database management systems (e.g., MySQL), and user interfaces

[0926] Data collection and transmission

[0927] The user uses the device to input personal data such as goals (e.g., diet, maintaining health), current weight, height, dietary habits, exercise habits, etc. The device then sends the input data to the server using an HTTP POST request.

[0928] Data storage and management

[0929] The server stores the received data in a database. The database management system (e.g. MySQL) organizes the data by user and executes INSERT queries to store the data.

[0930] Analyzing data and generating custom instruction

[0931] The server retrieves the user's data from the database using a SELECT query and generates prompts to input into a generative AI model (e.g., GPT-4). The generative AI model then generates custom instruction tailored to the user's goals and needs based on the prompts.

[0932] Sending and Viewing Custom Instructions

[0933] The server sends the generated custom instruction to the terminal as an HTTP response, and the terminal displays the received custom instruction to the user.

[0934] Specific examples

[0935] For users who want to lose weight

[0936] 1. User enters data:

[0937] The user enters the following information into the application form: "Goal weight: 60 kg, Current weight: 70 kg, Diet: Bread and coffee for breakfast, salad and chicken for lunch, fish and vegetables for dinner, Exercise habits: Jogging three times a week" and presses the "Submit" button.

[0938] 2. The device sends the data to the server:

[0939] The terminal transmits the input data to the server.

[0940] 3. The server saves the data to the database:

[0941] The server stores user data in a database.

[0942] 4. The server analyzes the data using the generative AI model:

[0943] The server retrieves user data from the database and generates prompt sentences for the generative AI model.

[0944] 5. The server generates the custom instructions:

[0945] The generative AI model suggests calorie restrictions and specific exercise plans based on the following: target weight: 60 kg, current weight: 70 kg, diet: bread and coffee for breakfast, salad and chicken for lunch, fish and vegetables for dinner, exercise habits: jogging three times a week.

[0946] 6. The server sends the generated instructions to the device:

[0947] The server transmits the generated instructions to the terminal.

[0948] 7. The device displays instructions to the user:

[0949] The device displays instructions to the user such as, "We recommend limiting your daily calorie intake to 1,500 kcal and continuing to jog three times a week. Add a protein shake to your breakfast and eat plenty of vegetables at lunch."

[0950] Prompt Sentence Examples

[0951] For users looking to lose weight:

[0952] "My current weight is 70 kg, and my goal weight is 60 kg. My daily diet consists of bread and coffee for breakfast, salad and chicken for lunch, and fish and vegetables for dinner. I go jogging three times a week. Please suggest an appropriate diet and exercise plan."

[0953] For users looking to stay healthy:

[0954] "I currently weigh 65 kg and don't have any specific weight loss goals. I try to eat a balanced diet each day, but I'd like some specific advice. I do yoga twice a week. Could you suggest a lifestyle and exercise plan to help me stay healthy?"

[0955] In this way, the system performs specific actions at each step and provides customized instruction to the user.

[0956] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0957] Step 1:

[0958] The user enters the data.

[0959] The user uses the device to input personal data such as goals (e.g., diet, maintaining health), current weight, height, dietary habits, and exercise habits. Specifically, the user enters the required information into a form in a dedicated application and presses the "Submit" button. The input data includes the target weight, current weight, dietary habits, and exercise habits.

[0960] Step 2:

[0961] The device sends the data to the server.

[0962] The terminal sends the data entered by the user to the server using an HTTP POST request. Specifically, the terminal converts the entered data into JSON format and sends it to the server's API endpoint. The input is the personal data entered by the user, and the output is the data sent to the server.

[0963] Step 3:

[0964] The server stores the data in a database.

[0965] The server stores the received data in a database. Specifically, the server uses a database management system (e.g., MySQL) to organize the data for each user and executes INSERT queries to store the data. The input is the data sent to the server, and the output is the data stored in the database.

[0966] Step 4:

[0967] The server analyzes the data using the generative AI model.

[0968] The server retrieves the user's data from the database using a SELECT query and generates a prompt to input to the generative AI model. Specifically, the server creates a prompt based on the user's goals and current situation and inputs it into the generative AI model (e.g., GPT-4). The input is the user's data retrieved from the database, and the output is the prompt to input to the generative AI model.

[0969] Step 5:

[0970] The server generates the custom instructions.

[0971] The generative AI model generates custom guidance tailored to the user's goals and needs based on the prompt. Specifically, the generative AI model analyzes the prompt and generates appropriate dietary and exercise plans and lifestyle advice. The input is the prompt, and the output is the generated custom guidance.

[0972] Step 6:

[0973] The server transmits the generated instruction to the terminal.

[0974] The server sends the generated custom instruction to the terminal as an HTTP response. Specifically, the server converts the generated instruction into JSON format and sends it to the terminal. The input is the generated custom instruction, and the output is the data sent to the terminal.

[0975] Step 7:

[0976] The terminal displays instructions to the user.

[0977] The device displays the customized instruction received from the server to the user. Specifically, the device updates the application UI and displays the generated instruction content in a user-friendly format. The input is the customized instruction received from the server, and the output is the instruction content displayed to the user.

[0978] (Application example 2)

[0979] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0980] While conventional personal trainer systems can provide customized instruction tailored to the user's goals and needs, they lack functions such as meal menu suggestions, ordering, and progress tracking, resulting in insufficient comprehensive support for users to achieve their goals. In particular, because there is no system that integrates meal suggestions and ordering, it takes time for users to actually consume the suggested meals, making continuous health management difficult.

[0981] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0982] In this invention, the server includes means for collecting personal data of an individual, means for analyzing the collected personal data, means for providing customized instruction tailored to individual goals and needs based on the analysis results, means for suggesting optimal meal menus based on the customized instruction, means for ordering the suggested meal menus in cooperation with an external meal provision service, and means for tracking the user's progress and providing feedback, thereby enabling the user to receive consistent health management, easily order the suggested meal menus, and continuously track their health status.

[0983] "Individual personal data" refers to individual information about a user, such as their weight, height, age, activity level, and goals.

[0984] "Means of collection" refers to the methods and devices used to obtain personal data from users.

[0985] "Means for analysis" refers to the methods and devices used to analyze collected personal data and extract information based on the user's goals and needs.

[0986] "Customized Instruction" refers to instruction or advice tailored to a user's individual goals and needs.

[0987] "AI personal trainer" refers to a system that uses artificial intelligence to analyze a user's personal data and provide customized instruction.

[0988] "Means for suggesting meal menus" refers to a method or device for generating and suggesting optimal meal menus based on the user's personal data and custom instructions.

[0989] "External Meal Delivery Service" refers to an external service provider that actually delivers the proposed meal menu.

[0990] "Means for ordering" refers to a method or device for placing an order for a proposed meal menu from an external meal service.

[0991] "Progress tracking means" means a method or device that continuously monitors and records a user's health status and progress toward goals.

[0992] "Means for providing feedback" refers to a method or device for providing appropriate advice or information to a user based on tracked progress data.

[0993] The system for implementing the present invention includes a series of means for collecting and analyzing personal data of an individual and providing customized guidance. Specific embodiments will be described below.

[0994] First, the user enters their personal data (weight, height, age, activity level, goals, etc.) through a smartphone application, which is then sent to a server and stored.

[0995] The server then analyzes the collected personal data using a generative AI model to generate custom guidance based on the user's goals and needs. For example, if the user is trying to lose weight, it will suggest low-calorie, nutritionally balanced meals.

[0996] The proposed meal menu can be ordered in cooperation with an external meal service. The user can easily order the proposed menu through the application. This ordering process is executed by the server using the API of the external meal service.

[0997] Furthermore, the server tracks the user's progress and provides feedback. It periodically collects data such as the user's weight and body fat percentage, and uses a generative AI model to evaluate the user's progress. Based on the evaluation results, the server provides appropriate advice and guidance to the user.

[0998] The following hardware and software are used to implement this system:

[0999] Smartphone (as a user interface)

[1000] Server (for storing and analyzing data)

[1001] Generative AI models (to generate custom instruction)

[1002] External meal service API (to order meals)

[1003] For example, if a user is trying to lose weight, they can input the following prompts into the generative AI model:

[1004] "Recommend optimal meals based on the user's weight, height, age, activity level, and goals. Make the suggested meals available for ordering through a food delivery service."

[1005] Based on this prompt, the generative AI model will suggest the optimal meal plan for the user, and the server will then order the plan from an external meal delivery service. Through this process, users can receive consistent health care.

[1006] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1007] Step 1:

[1008] The user inputs personal data through a smartphone application.

[1009] Input: Data such as the user's weight, height, age, activity level, and goals.

[1010] Output: The entered personal data is sent to the server.

[1011] Specific operation: The user enters their personal data into the application's input form and presses the "Submit" button. The application then sends the input data to the server.

[1012] Step 2:

[1013] The server stores the received personal data.

[1014] Input: Personal data submitted by the user.

[1015] Output: Saved personal data.

[1016] Specific operation: The server stores the received data in a database for later analysis.

[1017] Step 3:

[1018] The server analyzes the stored personal data.

[1019] Input: Stored personal data.

[1020] Output: Analysis results (customized instruction based on user goals and needs).

[1021] How it works: The server uses a generative AI model to analyze stored personal data and generate custom coaching based on the user's goals and needs.

[1022] Step 4:

[1023] The server will suggest the best meal menu based on your custom instructions.

[1024] Input: Analysis results (custom instruction).

[1025] Output: A suggested meal menu.

[1026] Specific operation: The server inputs a prompt into the generative AI model to generate the optimal meal menu for the user. Example prompt: "Please suggest the optimal meal menu based on the user's weight, height, age, activity level, and goals."

[1027] Step 5:

[1028] The server orders the proposed meal menu in cooperation with an external meal provision service.

[1029] Input: A suggested meal menu.

[1030] Output: Order confirmation information.

[1031] Specific operation: The server uses the API of the external meal service to order the proposed meal menu. If the order is successful, order confirmation information is returned to the server.

[1032] Step 6:

[1033] The server tracks the user's progress and provides feedback.

[1034] Input: Progress data such as the user's weight and body fat percentage.

[1035] Output: Feedback information.

[1036] Specific operation: The server periodically collects progress data from the user, evaluates the progress using a generative AI model, and provides appropriate advice and guidance to the user based on the evaluation results.

[1037] Example 3

[1038] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1039] Conventional personal trainer systems have difficulty fully understanding an individual's behavioral patterns and lifestyle and providing optimal advice and guidance based on that. Furthermore, insufficient pre-processing and analysis of collected data has led to problems with the accuracy of the advice provided.

[1040] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for preprocessing the collected personal data, means for analyzing the preprocessed data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for generating optimal advice and guidance using a generative AI model, and means for providing the generated advice to the user's terminal. This makes it possible to understand an individual's behavioral patterns and living situation in detail and provide highly accurate advice and guidance based on that understanding.

[1041] "Individual personal data" refers to information about an individual's behavioral patterns, habits, living conditions, health status, etc.

[1042] "Means of collection" refers to the hardware and software functions used to obtain an individual's personal data.

[1043] "Preprocessing means" refers to data cleansing and filtering functions to convert collected data into a format suitable for analysis.

[1044] "Means of analysis" refers to algorithms and machine learning models that use preprocessed data to analyze individuals' behavioral patterns and living conditions.

[1045] "Means for providing customized coaching" refers to the ability to generate advice and guidance tailored to individual goals and needs based on the analysis results.

[1046] "Generative AI model" refers to an artificial intelligence model that generates optimal advice and guidance based on a user's behavioral patterns and living situation.

[1047] "Means for generating advice or guidance" refers to the ability to use a generative AI model to create optimal advice or guidance for a user.

[1048] "Means for providing to the user's device" refers to the function of sending the generated advice and guidance to the user's device, such as a smartphone or tablet.

[1049] This invention relates to an AI personal trainer system that understands an individual's behavioral patterns and lifestyle and provides optimal advice and guidance based on that understanding. Specific embodiments of this system are described below.

[1050] System configuration

[1051] This system includes a server, a user's device, and a generative AI model. The server collects, preprocesses, and analyzes the user's personal data to generate optimal advice and guidance. The user's device is a device such as a smartphone or tablet that receives the advice and guidance provided by the server.

[1052] Hardware and software used

[1053] Hardware: Smartwatches, smartphones, tablets

[1054] Software: Python, TensorFlow, Database Management System

[1055] Data collection and preprocessing

[1056] The server collects lifestyle data from users' devices. Specifically, it obtains data such as sleep time, exercise time, and meal times from sensors on smartwatches and smartphones. The collected data is stored in a database and preprocessed. This preprocessing includes filling in missing values ​​and removing outliers.

[1057] Analyzing the data

[1058] The pre-processed data is then analyzed using Python and TensorFlow. The server then applies machine learning models to analyze the user's behavioral patterns and lifestyle. For example, an algorithm is used to determine whether the user has a night-owl lifestyle.

[1059] Generating Advice

[1060] Based on the analysis results, the server uses a generative AI model to generate optimal advice and guidance tailored to the user's behavioral patterns and lifestyle. For example, for a user with a nocturnal lifestyle, the server suggests optimal times for exercise and meals at night.

[1061] Providing advice

[1062] The generated advice is sent from the server to the user's device, where the user can check the advice on their smartphone, tablet, or other device. Specific examples of advice provided include the following:

[1063] Suggested exercise time: "Since you have a night owl lifestyle, I recommend exercising between 8:00 and 9:00 PM."

[1064] Mealtime suggestion: "In keeping with your night-owl lifestyle, we recommend eating dinner between 7:00 and 8:00 p.m."

[1065] Prompt Sentence Examples

[1066] An example of a prompt sentence to input to the generative AI model is as follows:

[1067] User behavior: Night owl

[1068] User's life situation: Work continues late into the night

[1069] User dietary restrictions: Gluten-free

[1070] Generated advice: Suggested exercise and meal times to suit night-owl lifestyles, gluten-free meal suggestions

[1071] In this way, the AI ​​personal trainer can understand the user's behavioral patterns and lifestyle in detail and provide highly accurate advice and guidance based on that understanding. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[1072] Step 1: Collect user data

[1073] The server collects lifestyle data from the user's device. As input, it receives data such as sleep time, exercise time, and meal time obtained from sensors on the smartwatch or smartphone. Specifically, the server obtains this data through an API and stores it in a database. As output, the collected raw data is stored in the database.

[1074] Step 2: Preprocessing the data

[1075] The server preprocesses the collected data. It uses the raw data stored in the database as input. Specifically, the server performs functions such as filling in missing values ​​and removing outliers. For example, if a user forgets to record some data, it applies an algorithm to fill in the missing data. It also detects and removes outliers such as abnormally high heart rates or step counts. The output is clean, preprocessed data.

[1076] Step 3: Analyze behavioral patterns

[1077] The server uses the preprocessed data to analyze the user's behavioral patterns. The preprocessed, clean data is used as input. Specifically, the server applies a machine learning model using Python and TensorFlow to determine whether the user has a night-owl lifestyle. For example, if the user's sleep time is from late at night to late in the morning, the server determines that the user has a night-owl lifestyle. The output is an analysis result of the user's behavioral patterns.

[1078] Step 4: Generating Advice

[1079] The server uses a generative AI model to generate optimal advice and guidance based on the analysis results. The behavioral pattern analysis results are used as input. Specifically, the server inputs prompt statements into the generative AI model to generate optimal advice for the user. For example, for a user with a nocturnal lifestyle, it would suggest appropriate exercise and meal times at night. The generated advice and guidance is obtained as output.

[1080] Step 5: Providing advice

[1081] The server provides the generated advice to the user's device. The generated advice and guidance are used as input. Specifically, the server sends the advice to the user's smartphone or tablet. The user can check the advice on their device. For example, if the user has a nocturnal lifestyle, the following notification will be displayed on the device: "You have a nocturnal lifestyle, so we recommend that you exercise between 8:00 and 9:00 p.m." The output is the advice displayed on the user's device.

[1082] (Application example 3)

[1083] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1084] Traditional personal trainer systems struggled to fully understand users' behavioral patterns and lifestyles and provide customized instruction tailored to their individual needs. They also lacked the functionality to provide real-time feedback and advice, preventing them from effectively supporting users in managing their health and achieving their fitness goals. Furthermore, they were inadequate in proposing optimal exercise and meal plans based on users' lifestyle habits and dietary restrictions.

[1085] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means including an artificial intelligence personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, means for learning the user's behavioral patterns and lifestyle and proposing optimal exercise and meal plans, and means for providing real-time feedback and advice. This makes it possible to propose optimal exercise and meal plans based on the user's behavioral patterns and lifestyle, and to effectively support the user in managing their health and achieving their fitness goals through real-time feedback and advice.

[1086] "Individual personal data" refers to individual information such as a user's behavioral patterns, lifestyle habits, health status, and dietary restrictions.

[1087] "Means of collection" refers to methods of obtaining individuals' personal data using sensors, smartphone applications, wearable devices, etc.

[1088] "Means of analysis" refers to the algorithms and software used to process collected personal data and understand users' behavioral patterns and living situations.

[1089] An "artificial intelligence personal trainer" is a system that uses machine learning and artificial intelligence technology to provide custom instruction tailored to a user's individual goals and needs.

[1090] "Means of learning user behavior patterns and lifestyle conditions" refers to algorithms and software that understand and learn from users' lifestyle habits and behavior patterns based on collected data.

[1091] "Means for suggesting optimal exercise and meal plans" refers to algorithms and software that generate and suggest individually customized exercise and meal plans based on the user's behavioral patterns and lifestyle.

[1092] "Means of providing real-time feedback and advice" refers to algorithms and software that provide immediate and appropriate feedback and advice based on the user's current state and behavior.

[1093] A system for implementing this invention includes an AI personal trainer that collects and analyzes personal data of an individual and proposes optimal exercise and meal plans to the user. Specific embodiments of this system are described below.

[1094] 1. System Configuration

[1095] The system consists of the following main components:

[1096] Data collection methods: Smartphones, wearable devices, sensors, etc. are used to collect data on users' behavioral patterns and living conditions.

[1097] Data analysis methods: Using algorithms and software (e.g., Python, machine learning libraries) to process collected data and understand users' behavioral patterns and life situations.

[1098] AI Personal Trainer: A system that uses machine learning and artificial intelligence technology to provide custom coaching tailored to a user's individual goals and needs.

[1099] Recommendation method: Algorithms and software that generate and suggest optimal exercise and meal plans based on the user's behavioral patterns and lifestyle.

[1100] Feedback tools: Algorithms and software that provide real-time feedback and advice.

[1101] 2. Data Collection and Analysis

[1102] The server collects data on the user's behavioral patterns and lifestyle from smartphones and wearable devices, including sleep patterns, exercise history, dietary habits, etc. The collected data is processed using data analysis tools to generate a model for understanding the user's behavioral patterns and lifestyle.

[1103] 3. Providing custom instruction

[1104] Based on the generated model, the AI ​​personal trainer will provide customized guidance tailored to the user's individual goals and needs. For example, if the user has a nocturnal lifestyle, it will suggest evening jogging or stretching. If the user is vegan, it will suggest a menu centered on tofu and vegetables.

[1105] 4. Real-time feedback

[1106] The server provides instant and appropriate feedback and advice based on the user's current condition and behavior, allowing users to receive real-time support and effective support in managing their health and achieving their fitness goals.

[1107] Specific examples

[1108] For example, if a user has a night owl lifestyle, the system will suggest evening jogging or stretching, or if the user is vegan, it will suggest tofu and vegetable-based meals.

[1109] Prompt Sentence Examples

[1110] "Recommend optimal exercise and meal plans based on the user's behavioral patterns and dietary restrictions. For example, if the user is a night owl, suggest evening jogging or stretching. If the user is vegan, suggest a menu centered around tofu and vegetables."

[1111] In this way, the system can provide users with advice tailored to their individual needs and effectively support them in managing their health and fitness goals.

[1112] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1113] Step 1:

[1114] The server collects data on the user's behavioral patterns and lifestyle from smartphones and wearable devices. Specifically, it acquires data on the user's sleep patterns, exercise history, dietary details, etc. The input is the data sent from the smartphone or wearable device, and the output is the collected personal data.

[1115] Step 2:

[1116] The server stores the collected personal data in a database. Specifically, it uses a database management system (e.g., MySQL) to organize and store data for each user. The input is the collected personal data, and the output is the data stored in the database.

[1117] Step 3:

[1118] The server analyzes the stored personal data. Specifically, it uses machine learning algorithms to generate a model for understanding the user's behavioral patterns and living situations. The input is the personal data stored in the database, and the output is a model for understanding the user's behavioral patterns and living situations.

[1119] Step 4:

[1120] The server then uses the generated model to provide customized guidance tailored to the user's individual goals and needs. Specifically, it proposes optimal exercise and meal plans based on the user's behavioral patterns and lifestyle. The input is the generated model, and the output is the content of the customized guidance.

[1121] Step 5:

[1122] The server provides real-time feedback and advice to users. Specifically, it instantly generates appropriate feedback and advice based on the user's current state and behavior, and notifies the user through a smartphone application. The input is the user's current state and behavior data, and the output is real-time feedback and advice.

[1123] Step 6:

[1124] The user receives and implements the feedback and advice provided by the server. Specifically, the user implements the exercise and meal plans notified to them through the smartphone application and sends the results back to the server. The input is the feedback and advice from the server, and the output is the data of the implementation results.

[1125] In this way, the system can provide users with advice tailored to their individual needs and effectively support them in managing their health and fitness goals.

[1126] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1127] "Example 1"

[1128] In one embodiment of the present invention, the AI ​​personal trainer includes an emotion engine that recognizes a user's emotions and tailors custom instruction based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it can suggest relaxation and exercises that contribute to relaxation. Alternatively, if the emotion engine recognizes that the user is feeling joy, it can suggest activities to maintain that joy.

[1129] "Example 2"

[1130] The emotion engine also provides optimal advice and guidance based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling sad, it will provide advice to alleviate that sadness. This advice could include appropriate exercise, diet, or relaxation techniques.

[1131] "Example 3"

[1132] Additionally, the emotion engine tracks a user's emotional state over time and analyzes changes. Based on this analysis, the emotion engine provides customized guidance that responds to changes in the user's emotional state. For example, if the emotion engine recognizes that a user has been feeling stressed for a period of time, it will suggest a long-term plan for stress management.

[1133] The processing flow of each embodiment will be described below.

[1134] "Example 1"

[1135] Step 1: The AI ​​personal trainer recognizes the user's emotions using an emotion engine.

[1136] Step 2: The emotion engine tailors custom instruction based on the perceived emotional state.

[1137] Step 3: For example, if the emotion engine recognizes that the user is feeling stressed, it will suggest relaxation and exercises that will help with relaxation.

[1138] "Example 2"

[1139] Step 1: The emotion engine provides optimal advice and guidance based on the user's emotional state.

[1140] Step 2: For example, if the emotion engine recognizes that the user is feeling sad, it will provide advice to alleviate that sadness.

[1141] Step 3: This advice may include proper exercise, diet, or relaxation techniques.

[1142] "Example 3"

[1143] Step 1: The emotion engine tracks the user's emotional state over time and analyzes its changes.

[1144] Step 2: Based on this analysis, the emotion engine provides custom guidance that responds to changes in the user's emotional state.

[1145] Step 3: For example, if the user recognizes that they have been feeling stressed for a period of time, the emotion engine will suggest a long-term plan for stress management.

[1146] Example 1

[1147] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1148] Conventional personal trainer systems have difficulty fully understanding a user's behavioral patterns and emotional state and providing customized instruction based on that. Furthermore, they have been unable to provide appropriate advice in real time according to the user's emotional state. This has limited the effectiveness of the system in managing the user's health and improving their lifestyle.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1150] In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to a server on the cloud, means for storing the transmitted personal data in a database, means for analyzing the stored personal data, means for generating customized instruction tailored to individual goals and needs based on the analysis results, means for transmitting the generated customized instruction to the terminal and providing it to the user, and means for recognizing the user's emotions and adjusting the customized instruction based on the user's emotional state. This makes it possible to understand the user's behavioral patterns and emotional state in real time and provide optimal customized instruction based on that.

[1151] "Individual personal data" refers to individual information such as a user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details.

[1152] A "cloud server" is a remote server accessible via the Internet, and refers to a computer system for storing and processing data.

[1153] A "database" refers to a collection of information that stores collected data in an organized manner and can be searched and updated as needed.

[1154] "Means for analysis" refers to the algorithms and software used to analyze the collected data and assess the user's behavioral patterns and health status.

[1155] "Customized Instruction" refers to specific advice and guidance tailored to a user's individual goals and needs.

[1156] A "terminal" is a device that can be directly operated by a user, such as a smartphone or wearable device.

[1157] "Means for recognizing emotions" refers to technology that analyzes a user's voice data and text data to determine the user's emotional state.

[1158] "Adjustment" refers to algorithms or software that alter the content of the custom instruction provided based on the perceived emotional state.

[1159] This invention is a system that collects personal data using a user's smartphone or wearable device, analyzes the data on a cloud server, and provides optimal customized instruction to the user. Specific embodiments of this system are described below.

[1160] Data collection

[1161] A user's smartphone or wearable device collects data through built-in sensors and applications. For example, a smartphone application may record a user's diet, while a wearable device may measure heart rate or the number of steps taken. These devices include standard smartphones and wearable devices.

[1162] Data transmission

[1163] The data collected by the device is sent via the internet to a server in the cloud. The data is encrypted before transmission to ensure security. A common cloud platform is used for the cloud service.

[1164] Data storage

[1165] The server stores the received data in a database. A typical relational database is used for the database. The stored data is organized by user and prepared for later analysis.

[1166] Data analysis

[1167] The server then passes the stored data to an AI personal trainer, who analyzes it using machine learning algorithms, such as TensorFlow, to analyze the user's exercise patterns and dietary habits and assess their health.

[1168] emotion recognition

[1169] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data and determines their emotions using natural language processing technology. For example, if a user enters "I'm tired today," the emotion engine will recognize that the user is feeling stressed.

[1170] Generate custom instruction

[1171] The server generates custom guidance based on the user's emotional state. For example, if it detects that the user is feeling stressed, it will suggest yoga poses or deep breathing exercises to help with relaxation. Conversely, if it detects that the user is feeling joy, it will suggest activities to maintain that joy.

[1172] Providing guidance

[1173] The server then sends the custom instruction to the device, which then displays it to the user via notifications or in-app messages. For example, a smartphone notification could display a message like, "Take deep breaths to relax."

[1174] Specific examples

[1175] Example 1: A stressed user

[1176] When a user types "I'm very tired today" into their smartphone, the device sends this data to a server. The server uses an emotion engine to recognize that the user is feeling stressed and generates yoga poses and deep breathing exercises to help with relaxation. The generated guidance is sent to the device and notified to the user.

[1177] Example 2: Delighted users

[1178] When a user inputs "Today was a great day" through a wearable device, the device sends this data to the server. The server uses an emotion engine to recognize that the user is feeling joy and generates an activity to maintain that joy. The generated guidance is sent to the device and notified to the user.

[1179] Prompt Sentence Examples

[1180] "Generate a list of suggested relaxation exercises for users when they are feeling stressed."

[1181] "Generate a list of suggested activities for users who experience joy."

[1182] The above is a specific embodiment for carrying out the invention.

[1183] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1184] Step 1: Data collection

[1185] A user's smartphone or wearable device collects data through built-in sensors and applications. For example, a smartphone application records the user's diet, while a wearable device measures heart rate and steps. The input is data about the user's behavioral patterns and health status, and the output is the collected personal data.

[1186] Step 2: Send data

[1187] The data collected by the device is sent via the Internet to a server on the cloud. The data is encrypted before being sent, ensuring security. The input is the collected personal data, and the output is the data sent to the server.

[1188] Step 3: Save data

[1189] The server stores the received data in a database. A typical relational database is used for the database. The input is the data sent to the server, and the output is the data stored in the database.

[1190] Step 4: Data analysis

[1191] The server passes the stored data to an AI personal trainer, which analyzes it using machine learning algorithms. For example, TensorFlow is used to analyze the user's exercise patterns and dietary habits to assess their health. The input is the data stored in the database, and the output is the analysis results.

[1192] Step 5: Emotion Recognition

[1193] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data and determines the emotion using natural language processing technology. The input is the user's voice and text data, and the output is the recognized emotional state.

[1194] Step 6: Generate custom instruction

[1195] The server generates custom guidance based on the user's emotional state. For example, if it recognizes that the user is feeling stressed, it will suggest yoga poses or deep breathing exercises to help with relaxation. The input is the recognized emotional state and the analysis results, and the output is the generated custom guidance.

[1196] Step 7: Provide guidance

[1197] The server sends the generated custom instruction to the device and provides it to the user. The device displays the instruction to the user through notifications or in-app messages. For example, a smartphone notification function could be used to display a message such as "Take deep breaths to relax." The input is the generated custom instruction, and the output is the instruction provided to the user.

[1198] (Application example 1)

[1199] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1200] Conventional food delivery services do not offer personalized meal suggestions based on the user's health or emotional state, making it difficult to contribute to maintaining the user's health and emotional stability. Furthermore, there is a need to utilize users' personal data to provide more personalized services.

[1201] 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. In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means including an AI personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, means for recognizing the user's emotional state, means for proposing an optimal meal menu based on the recognized emotional state, and means for ordering the suggested meal menu from a food delivery service. This makes it possible to suggest optimal meals based on the user's health condition and emotional state, contributing to the user's maintenance of health and emotional stability.

[1202] "Individual personal data" refers to individual information such as a user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[1203] "Means of collection" refers to the function for obtaining users' personal data through smartphones and wearable devices.

[1204] "Means for analysis" refers to the function of analyzing collected personal data on a cloud server to evaluate the user's health condition and behavioral patterns.

[1205] "AI personal trainer providing customized instruction" refers to an artificial intelligence system that provides advice and guidance tailored to a user's individual goals and needs based on analysis results.

[1206] "Means for recognizing the user's emotional state" refers to a function for identifying the user's emotions using an emotion engine based on the user's personal data.

[1207] "Means for suggesting optimal meal menus" refers to a function for selecting and suggesting appropriate meal menus based on the user's health and emotional state.

[1208] "Means for ordering from a food delivery service" refers to the functionality for ordering suggested meal menus online from a food delivery service.

[1209] A system for implementing this invention collects personal data from users' smartphones and wearable devices and analyzes it on a cloud server. A specific embodiment of this system will be described below.

[1210] First, the system uses the user's smartphone or wearable device (e.g., smartwatch or fitness tracker) to collect personal data such as the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details. This is done using the smartphone's sensors and the wearable device's API.

[1211] The collected personal data is then sent to a cloud-based server, which uses machine learning libraries (e.g., TensorFlow) to analyze the collected data, assessing the user's health and behavioral patterns and providing custom guidance tailored to their individual goals and needs.

[1212] Furthermore, the server recognizes the user's emotional state using an emotion engine, which identifies the emotion the user is currently feeling (e.g., stress, joy, fatigue, etc.) based on the user's personal data.

[1213] Once the user's emotional state is recognized, the server will suggest the optimal meal plan based on that emotional state. For example, if the user is feeling stressed, the server will suggest a meal plan containing ingredients that have a relaxing effect. This suggestion is based on a nutrition database and past user preference data.

[1214] Finally, the suggested meal menu is ordered from a food delivery service, and the server sends the order via the food delivery service's API online, allowing users to easily receive the meal that best suits their health and emotional state.

[1215] For example, if a user is feeling stressed, the emotion engine will recognize this state and suggest a menu that includes relaxing chamomile tea and dark chocolate. This suggestion is made by inputting the following prompt into the generative AI model:

[1216] "Recommend optimal meal plans based on the user's health data and emotional state. If the user is stressed, suggest a meal plan that includes ingredients that have a relaxing effect."

[1217] In this way, a system is realized that can contribute to maintaining the user's health and emotional stability.

[1218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1219] Step 1:

[1220] A user's smartphone or wearable device collects personal data such as the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content. This is done using the smartphone's sensors and the wearable device's API. The input is the user's personal data, and the output is the collected data.

[1221] Step 2:

[1222] The device sends the collected personal data to a server on the cloud. The input is the collected personal data, and the output is the data sent to the server.

[1223] Step 3:

[1224] The server analyzes the received personal data. It uses a machine learning library (e.g., TensorFlow) to evaluate the user's health status and behavioral patterns. The input is the personal data sent to the server, and the output is the analysis results.

[1225] Step 4:

[1226] The server uses an emotion engine to recognize the user's emotional state. The emotion engine identifies the emotion the user is currently feeling (e.g., stress, joy, fatigue, etc.) based on the user's personal data. The input is the analysis result, and the output is the recognized emotional state.

[1227] Step 5:

[1228] The server proposes an optimal meal menu based on the recognized emotional state. It selects an appropriate meal menu using a nutrition database and past user preference data. The input is the recognized emotional state, the nutrition database, and past preference data, and the output is a suggested meal menu.

[1229] Step 6:

[1230] The server orders the suggested meal menu from a food delivery service. The order is sent online using the food delivery service's API. The input is the suggested meal menu, and the output is the order sent to the food delivery service.

[1231] Step 7:

[1232] A user receives a suggested meal from a food delivery service. The input is the delivery from the food delivery service, and the output is the meal received by the user.

[1233] Example 2

[1234] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1235] Conventional personal trainer systems can provide guidance based on the user's physical data and behavioral patterns, but they cannot provide advice that takes into account the user's emotional state. Therefore, there is a need for systems that provide optimal guidance and advice based on the user's emotional state.

[1236] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1237] In this invention, the server includes means for collecting personal data of an individual, means for analyzing the collected personal data, means for providing customized instruction tailored to individual goals and needs based on the analysis results, means for collecting emotional states of the individual, means for analyzing the collected emotional states, and means for providing advice optimal for the emotional states based on the analysis results, thereby enabling comprehensive instruction and advice that takes into account both the user's physical data and emotional states.

[1238] "Individual personal data" refers to information relating to the user's physical and lifestyle habits, such as weight, diet, and amount of exercise.

[1239] The "means for collecting" is a function for transmitting data entered by a user through a terminal to a server.

[1240] The "analysis means" is a function for analyzing collected data and generating guidance based on the user's goals and needs.

[1241] The "means for providing customized guidance" is a function for suggesting optimal dietary content and exercise amount to the user based on the analysis results.

[1242] "Personal emotional state" is information about the emotions the user is feeling (e.g., sad, happy).

[1243] The "means for collecting emotional states" is a function for transmitting emotional data input by the user through the terminal to the server.

[1244] The "means for analyzing emotional state" is a function for analyzing collected emotional data and identifying the emotional state of the user.

[1245] The "means of providing advice optimal for the user's emotional state" is a function that suggests relaxation techniques and exercise plans according to the user's emotional state based on the analysis results.

[1246] "Behavioral patterns" are information about the user's daily actions and habits.

[1247] "Lifestyle" is information about the user's living environment and lifestyle habits.

[1248] MODE FOR CARRYING OUT THE INVENTION

[1249] This invention is a system that collects and analyzes personal data and emotional states of individuals to provide optimal customized guidance and advice to users. This system consists of three main components: a server, a terminal, and a user.

[1250] 1. Data Collection and Storage

[1251] The user inputs personal data such as weight, dietary habits, and exercise amount through the terminal. The terminal sends the input data to the server. The server stores the received data in a database (e.g., MySQL).

[1252] 2. Data Analysis

[1253] The server uses Python libraries to analyze the collected personal data and generate customized guidance based on the user's goals (e.g., dieting, maintaining health).

[1254] 3. Providing custom instruction

[1255] The server sends the generated custom instructions to the user's device, which then displays the received custom instructions to the user. The user can then confirm and implement the suggested diet and exercise regimen.

[1256] 4. Emotional Data Collection and Analysis

[1257] The user inputs their current emotional state (e.g., sad, happy) through the device. The device then sends the input emotional data to the server. The server then analyzes the emotional data using a natural language processing library. Based on the analysis results, the server generates advice that best suits the user's emotional state.

[1258] 5. Providing advice

[1259] The server sends the generated advice to the user's device, which then displays the received advice to the user. The user can then confirm and implement the suggested relaxation techniques and exercise plans.

[1260] Specific examples

[1261] For weight loss goals:

[1262] The user inputs their weight and dietary information.

[1263] The terminal transmits the input data to the server.

[1264] The server analyzes the data and suggests calorie restriction and exercise plans.

[1265] The server transmits the generated instruction content to the terminal, which displays it to the user.

[1266] For example: "Eat oatmeal for breakfast, a salad for lunch, and chicken breast and vegetables for dinner. Jogging 30 minutes each day."

[1267] If your emotional state is sad:

[1268] The user types "sad."

[1269] The terminal transmits the input data to the server.

[1270] The server analyzes emotional data and suggests relaxation techniques.

[1271] The server sends the generated advice to the terminal, which displays it to the user.

[1272] For example: "Try some deep breathing and yoga to help you relax."

[1273] Prompt Sentence Examples

[1274] Diet Goal Prompt:

[1275] "If the user is trying to lose weight, please suggest appropriate dietary and exercise regimes."

[1276] Prompt for sad emotional state:

[1277] "If the user is experiencing sadness, provide advice to alleviate that sadness."

[1278] In this way, comprehensive guidance and advice can be provided that takes into account both the user's physical data and emotional state.

[1279] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1280] Step 1:

[1281] The user inputs personal data such as weight, dietary habits, and exercise amount through the terminal.

[1282] Input: The user inputs data such as weight, diet, and exercise.

[1283] What happens: A user opens a smartphone app, enters their weight, and records what they had for breakfast.

[1284] Output: The entered data is saved on the device.

[1285] Step 2:

[1286] The terminal transmits the input data to the server.

[1287] Input: Personal data entered by the user.

[1288] Specific operation: The app sends the entered data to the server in real time.

[1289] Output: Data is sent to the server.

[1290] Step 3:

[1291] The server stores the received data in a database.

[1292] Input: Personal data sent from the device.

[1293] What happens: The server adds a new record to the database, storing the user's weight and dietary information.

[1294] Output: Personal data stored in a database.

[1295] Step 4:

[1296] The server uses Python libraries to analyze the collected data.

[1297] Input: Personal data stored in a database.

[1298] Specific operation: The server reads data from the database, converts it into a data frame, and performs statistical analysis.

[1299] Output: Analysis results.

[1300] Step 5:

[1301] The server generates custom instruction based on the analysis results and is tailored to the user's goals.

[1302] Input: Analysis results.

[1303] What it does: The server calculates the user's calorie consumption and generates an appropriate meal plan and exercise plan.

[1304] Output: Custom instruction.

[1305] Step 6:

[1306] The server transmits the generated custom instruction to the user's terminal.

[1307] Input: Custom Instruction.

[1308] Specific operation: The server generates instruction content and sends it to the terminal in JSON format.

[1309] Output: Custom instruction sent to the terminal.

[1310] Step 7:

[1311] The terminal displays the received custom instruction to the user.

[1312] Input: Custom instructions sent from the server.

[1313] What it does: The app parses the data it receives and displays it in a user-friendly format.

[1314] Output: The custom instruction displayed to the user.

[1315] Step 8:

[1316] The user inputs his / her current emotional state through the terminal.

[1317] Input: The emotion the user is feeling (e.g. sad, happy).

[1318] Specific action: The user enters "sad" into an emotion input form within the app.

[1319] Output: The input emotion data is saved on the device.

[1320] Step 9:

[1321] The terminal transmits the input emotion data to the server.

[1322] Input: Emotion data entered by the user.

[1323] Specific operation: The app sends the input emotion data to the server in real time.

[1324] Output: Data is sent to the server.

[1325] Step 10:

[1326] The server uses a natural language processing library to analyze the sentiment data.

[1327] Input: Emotion data sent from the device.

[1328] Specific operation: The server performs text analysis of the emotion data to identify the user's emotional state.

[1329] Output: Emotion analysis results.

[1330] Step 11:

[1331] Based on the analysis results, the server generates advice that is best suited to the user's emotional state.

[1332] Input: Sentiment analysis results.

[1333] Specific actions: The server generates relaxation techniques and exercise plans according to the user's emotional state.

[1334] Output: Emotion-based advice.

[1335] Step 12:

[1336] The server transmits the generated advice to the user's terminal.

[1337] Input: Emotion-based advice.

[1338] Specific operation: The server sends the generated advice in JSON format to the device.

[1339] Output: Advice sent to the terminal.

[1340] Step 13:

[1341] The terminal displays the received advice to the user.

[1342] Input: Advice sent by the server.

[1343] What it does: The app parses the data it receives and displays it in a user-friendly format.

[1344] Output: The advice displayed to the user.

[1345] (Application example 2)

[1346] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1347] While conventional personal trainer systems can provide customized training based on a user's goals and needs, they are unable to provide advice or guidance that takes into account the user's emotional state. Furthermore, there is a lack of means to provide customized training based on a user's health status and goals in physical stores. This has resulted in insufficient comprehensive health management and emotional support for users.

[1348] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1349] In this invention, the server includes a means for collecting personal data of individuals, a means for analyzing the collected personal data, an AI personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, a means for analyzing the user's emotional state, a means for providing optimal advice and guidance based on the emotional state, a means for providing customized instruction based on the user's health status and goals in a physical store, and a means for including an application to be installed on a smartphone. This makes it possible to provide not only customized instruction based on the user's goals and needs, but also advice and guidance based on the user's emotional state. Furthermore, comprehensive support for the user's health management can be provided even in a physical store.

[1350] "Individual personal data" refers to individual information such as a user's health status, behavioral patterns, lifestyle habits, and emotional state.

[1351] "Means of collection" refers to devices and software such as sensors, input devices, and applications used to obtain personal data from users.

[1352] "Analytical tools" refers to the algorithms and software used to analyze collected personal data and understand users' goals and needs.

[1353] "Customized Coaching" refers to personalized advice and guidance tailored to a user's individual goals and needs.

[1354] "AI personal trainer" refers to a system that uses artificial intelligence to analyze a user's personal data and provide customized instruction.

[1355] "Emotional state" refers to a user's current emotional or psychological state.

[1356] "Means for analyzing emotional state" refers to sensors and algorithms for detecting and analyzing the user's emotional state.

[1357] "Optimal advice and guidance" refers to the most appropriate instruction and support provided based on the user's goals and emotional state.

[1358] "Brick and mortar" refers to physical stores that users can visit in person, such as fitness gyms or health food stores.

[1359] "Applications installed on smartphones" refers to software that is downloaded and executed on a user's smartphone.

[1360] This invention is a system that collects and analyzes personal data of individuals to provide customized guidance tailored to the user's goals and needs. Furthermore, it can analyze the user's emotional state and provide optimal advice and guidance based on that emotional state. This system can also provide customized guidance based on the user's health status and goals in brick-and-mortar stores.

[1361] System configuration

[1362] The system consists of the following major components:

[1363] 1. Personal data collection methods:

[1364] This includes sensors and input devices that collect data on a user's health, behavioral patterns, lifestyle habits, emotional state, etc. Examples include smartphone applications and wearable devices.

[1365] 2. Data analysis methods:

[1366] It includes algorithms and software that analyze collected personal data to understand users' goals and needs, and is implemented using programming languages ​​such as Python.

[1367] 3. AI Personal Trainer:

[1368] A system that uses artificial intelligence to analyze users' personal data and provide custom guidance. It uses generative AI models to generate the best advice for users.

[1369] 4. Emotional state analysis tools:

[1370] It includes sensors and algorithms to detect and analyze a user's emotional state, for example, by using a smartphone's camera and microphone to analyze emotions.

[1371] 5. Custom in-store instruction:

[1372] This system allows users to receive customized guidance based on their health status and goals at physical locations such as fitness gyms and health food stores, and is implemented through an application installed on a smartphone.

[1373] Program processing explanation

[1374] The server receives the personal data collected from the user and analyzes it using a data analysis means. Based on the analysis results, the AI ​​personal trainer generates customized instruction tailored to the user's goals and needs. Furthermore, the server analyzes the user's emotional state using an emotional state analysis means and provides optimal advice and guidance based on the user's emotional state.

[1375] The hardware used includes smartphones, wearable devices, sensors, etc. The software includes data analysis algorithms using Python, generative AI models, and smartphone applications.

[1376] Specific examples

[1377] For example, if a user sets a goal of "dieting" and feels "sad," the system operates as follows:

[1378] 1. The user sets a goal of "dieting" through a smartphone application.

[1379] 2. The emotional state analysis means analyzes the user's emotional state as "sadness."

[1380] 3. The AI ​​personal trainer generates fitness guidance such as, "I recommend a low-calorie diet and aerobic exercise."

[1381] 4. Offer emotional support based on their emotional state: "Try yoga or meditation to relax."

[1382] Example prompt for a generative AI model:

[1383] The user's goal is to lose weight and they are currently experiencing sadness. Provide them with appropriate fitness guidance and emotional support.

[1384] By inputting this prompt into a generative AI model, optimal advice can be generated for the user.

[1385] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1386] Step 1:

[1387] A user starts a smartphone application and sets a goal (e.g., dieting).

[1388] Input: The target data that the user enters into the application.

[1389] Output: The set target data is sent to the server.

[1390] Specific actions: The user selects a goal such as "diet" through the application interface and presses the submit button.

[1391] Step 2:

[1392] The server receives the user's goal data and stores it in a database.

[1393] Input: Goal data submitted by the user.

[1394] Output: Goal data stored in a database.

[1395] Specific operation: The server receives the HTTP request and stores the target data in the database.

[1396] Step 3:

[1397] The user inputs the emotional state through a smartphone application, or the emotional state analysis means automatically detects the emotion.

[1398] Input: Emotional data entered by the user or detected by a sensor.

[1399] Output: The detected or input emotion data is sent to the server.

[1400] Specific behavior: The user selects an emotion, such as "sadness," or the smartphone's camera or microphone detects the emotion.

[1401] Step 4:

[1402] The server receives the emotion data and stores it in a database.

[1403] Input: Emotion data submitted by the user.

[1404] Output: Emotion data stored in a database.

[1405] Specific operation: The server receives an HTTP request and stores the emotion data in a database.

[1406] Step 5:

[1407] The server generates a prompt sentence for the generative AI model based on the goal data and emotion data.

[1408] Input: Goal data and emotion data stored in a database.

[1409] Output: The generated prompt statement.

[1410] Specific behavior: The server combines the goal data and emotion data to generate a prompt sentence: "The user's goal is to lose weight and they are currently feeling sad. Please provide appropriate fitness guidance and emotional support."

[1411] Step 6:

[1412] The server inputs prompts into the generative AI model to generate optimal advice and guidance.

[1413] Input: The generated prompt statement.

[1414] Output: Advice or guidance from the generative AI model.

[1415] How it works: The server sends a prompt to the generative AI model, which then generates advice such as "We recommend a low-calorie diet and aerobic exercise" or "Try yoga or meditation to relax."

[1416] Step 7:

[1417] The server sends the generated advice and guidance to the user's smartphone application.

[1418] Input: Advice or guidance from a generative AI model.

[1419] Output: Advice and guidance displayed in the user's smartphone application.

[1420] What it does: The server sends advice and guidance to the user's smartphone via an HTTP response, which is then displayed by the application.

[1421] Step 8:

[1422] The user acts according to the advice and guidance and enters the results into a smartphone application.

[1423] Input: Data that the user enters as the result of an action.

[1424] Output: Action result data sent to the server.

[1425] Specific operation: The user enters the results of the exercise and diet he or she performed through the application interface and presses the submit button.

[1426] Step 9:

[1427] The server receives the behavior result data and stores it in a database.

[1428] Input: Action result data sent by the user.

[1429] Output: Behavioral outcome data stored in a database.

[1430] Specific operation: The server receives the HTTP request and stores the action result data in the database.

[1431] Step 10:

[1432] The server periodically analyzes the user's personal data and generates new advice and guidance as needed.

[1433] Input: Personal data stored in a database.

[1434] Output: The newly generated advice or guidance.

[1435] How it works: The server periodically scans the database and sends prompts to the generative AI model based on the latest data to generate new advice and guidance.

[1436] Example 3

[1437] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1438] Conventional personal trainer systems have difficulty fully understanding a user's behavioral patterns and lifestyle and providing optimal advice and guidance based on that. They also lack the functionality to track a user's emotional state and provide customized instruction in response to those changes. This has led to the issue of being unable to provide effective instruction tailored to the individual needs of each user.

[1439] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for preprocessing the collected personal data, means for analyzing the preprocessed data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for tracking the user's emotional state and analyzing changes therein, and means for providing customized guidance based on changes in the emotional state. This makes it possible to understand the user's behavioral patterns and living situation and provide optimal advice and guidance based thereon. Furthermore, tracking the user's emotional state and providing customized guidance in response to changes therein enables effective guidance tailored to the user's individual needs.

[1440] "Individual personal data" refers to individual information such as a user's behavioral patterns, living situation, emotional state, exercise history, and dietary history.

[1441] "Means of collection" refers to the function that allows users to input data through dedicated applications or devices and send that data to a server.

[1442] "Preprocessing means" refers to functions for imputing missing values ​​in collected data, normalizing data, and detecting and correcting outliers.

[1443] "Means of analysis" refers to generative AI models and algorithms that use pre-processed data to analyze users' behavioral patterns and lifestyles.

[1444] "Means for providing customized guidance" refers to functionality that generates and provides advice and guidance tailored to a user's individual goals and needs based on the analysis results.

[1445] "Means for tracking emotional state" refers to a function for periodically recording a user's emotional state and transmitting that data to a server.

[1446] "Means for analyzing changes in emotional state" refers to a function that uses an emotion engine to analyze changes in a user's emotional state and provide customized guidance based on the results.

[1447] MODE FOR CARRYING OUT THE INVENTION

[1448] This invention relates to an AI personal trainer system that collects and analyzes personal data of individuals and provides customized instruction. Specific embodiments of this system will be described below.

[1449] Hardware and Software Configuration

[1450] The server uses high-performance hardware, specifically a GPU-equipped server, and uses deep learning frameworks such as TensorFlow and natural language processing libraries for software.

[1451] Data collection

[1452] Users input data about their behavioral patterns and lifestyles through a dedicated application, such as exercise history, diet history, sleep time, and emotional state. The device then transmits this data to a server in real time.

[1453] Data Preprocessing

[1454] The server preprocesses the received user data by filling in missing values, normalizing the data, and detecting and correcting outliers. For example, if there are missing values ​​in the user's diet history, the server fills them in based on past data.

[1455] Analyzing the data

[1456] The server uses the preprocessed data to analyze the user's behavioral patterns and lifestyle. Specifically, it uses a generative AI model (e.g., a deep learning model using TensorFlow) to cluster the user's behavioral patterns and classify their lifestyle. For example, it determines whether the user has a night-owl lifestyle.

[1457] Providing custom instruction

[1458] The server generates optimal advice and guidance based on the analysis results. Specifically, it inputs prompts into the generative AI model to suggest exercise times and meal menus suitable for the user. For example, the prompt might read, "The user has a nocturnal lifestyle and gluten-free diet. Please suggest suitable exercise times and gluten-free meal menus suitable for the night."

[1459] Tracking and analyzing emotional states

[1460] The device periodically records the user's emotional state and sends it to a server. The server uses an emotion engine to track the user's emotional state over time and analyzes changes. For example, if the user has been feeling stressed for a certain period of time, the server can use that data to identify the cause of the stress.

[1461] Providing custom guidance (based on emotional state)

[1462] The server provides customized guidance based on the analysis results of the emotion engine. Specifically, if the user is feeling stressed, it suggests a long-term plan for stress management. For example, it provides guidance such as, "The user has shown high stress levels over the past month. To manage stress, we suggest increasing daily meditation time and engaging in relaxation activities once a week."

[1463] In this way, a system is realized in which the server, the terminal, and the user work together to provide the user with the most suitable advice and guidance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1464] Step 1: Collect user data

[1465] Users input data about their behavioral patterns and lifestyle through a dedicated application. For example, they input information such as exercise history, diet history, sleep time, and emotional state. The device then transmits this data to a server in real time. The input data is information about the user's behavioral patterns and lifestyle, and the output is the raw data transmitted to the server.

[1466] Step 2: Preprocessing the data

[1467] The server preprocesses the received user data. Specifically, it performs missing value imputation, data normalization, and outlier detection and correction. For example, if there are missing values ​​in the diet history entered by the user, the server imputes them based on past data. The input data is raw data, and the output is preprocessed, clean data.

[1468] Step 3: Analyze your behavioral patterns and living situations

[1469] The server uses the preprocessed data to analyze the user's behavioral patterns and lifestyle. Specifically, it uses a generative AI model (e.g., a deep learning model using TensorFlow) to cluster the user's behavioral patterns and classify their lifestyle. For example, it determines whether the user has a night-owl lifestyle. The input data is preprocessed, clean data, and the output is the analysis results.

[1470] Step 4: Generate optimal advice and guidance

[1471] The server generates optimal advice and guidance based on the analysis results. Specifically, it inputs prompt statements into the generative AI model to suggest exercise times and meal menus suitable for the user. For example, the prompt statement is input as follows: "The user has a nocturnal lifestyle and has gluten-free dietary restrictions. Please suggest appropriate exercise times and gluten-free meal menus suitable for nighttime." The input data are the analysis results and prompt statements, and the output is the generated advice and guidance.

[1472] Step 5: Track and analyze your emotional state

[1473] The device periodically records the user's emotional state and sends it to the server. The server uses an emotion engine to track the user's emotional state over time and analyzes changes. For example, if a user has been feeling stressed for a certain period of time, the data can be used to identify the cause of the stress. The input data is a record of the emotional state, and the output is the analysis of the emotional state.

[1474] Step 6: Providing custom guidance (based on emotional state)

[1475] The server provides custom guidance based on the analysis results of the emotion engine. Specifically, if the user is feeling stressed, it suggests a long-term plan for stress management. For example, it provides guidance such as, "The user has shown high stress levels over the past month. To manage stress, we suggest increasing daily meditation time and engaging in relaxation activities once a week." The input data is the analysis result of the emotional state, and the output is custom guidance.

[1476] (Application example 3)

[1477] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1478] Conventional food delivery services do not offer personalized meal suggestions based on the user's behavioral patterns, lifestyle, or emotional state, making it difficult to provide meals optimal for the user's health and emotional state. Furthermore, there is a lack of meal suggestions tailored to specific dietary restrictions or lifestyle habits, making it difficult to improve user satisfaction. Furthermore, there are no meal suggestions for stress management based on the user's emotional state, resulting in insufficient overall health management for the user.

[1479] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1480] In this invention, the server includes means for collecting personal data of an individual, means for analyzing the collected personal data, means including an artificial intelligence personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, means for understanding the individual's behavioral patterns, habits, and living situation, means for providing optimal advice and guidance based on the understood behavioral patterns, habits, and living situation, means for tracking the individual's emotional state and analyzing changes therein, means for providing customized instruction corresponding to changes in the emotional state, and means for a food delivery service that makes optimal meal suggestions based on the user's behavioral patterns, living situation, and emotional state. This makes it possible to make optimal meal suggestions based on the user's behavioral patterns, living situation, and emotional state, and to provide meals that are optimal for the user's health and emotional state.

[1481] "Individual personal data" refers to individual information such as an individual's behavioral patterns, living situation, and emotional state.

[1482] "Means of collection" refers to the devices and methods used to obtain an individual's personal data.

[1483] "Means for analysis" refers to devices and methods for analyzing collected personal data and extracting meaningful information.

[1484] "Artificial Intelligence Personal Trainer" refers to an artificial intelligence system that provides custom coaching tailored to individual goals and needs.

[1485] "Behavioral patterns" refer to an individual's daily behavioral tendencies and habits.

[1486] "Living conditions" refers to an individual's living environment and lifestyle.

[1487] "Emotional state" refers to an individual's current emotional or mood state.

[1488] "Tracking means" refers to devices or methods for continuously monitoring an individual's emotional state.

[1489] "Means for analyzing changes" refers to devices or methods for analyzing changes in emotional state and understanding their meaning.

[1490] "Customized instruction" refers to specific advice and guidance tailored to individual goals and needs.

[1491] "Food delivery service" refers to a service that delivers meals ordered by users to designated locations.

[1492] "Meal suggestions" refers to recommending optimal meals based on a user's behavioral patterns, lifestyle, and emotional state.

[1493] A system for implementing the present invention is configured as follows.

[1494] System Program

[1495] The server runs a program that collects and analyzes personal data, including an AI personal trainer that understands the user's behavioral patterns, lifestyle, and emotional state and then makes optimal dietary recommendations based on that information.

[1496] Hardware and software used

[1497] Hardware: Smartphones, servers

[1498] Software: Python, EmotionEngine

[1499] Data processing and calculation

[1500] 1. User profile acquisition: The server acquires the user's behavioral patterns, life situation, and emotional state using smartphone sensors and user input data.

[1501] 2. Get the current time: The server gets the current time using the datetime module.

[1502] 3. Meal suggestion logic: The server suggests optimal meals based on the user profile and the current time, taking into account the user's lifestyle habits, dietary restrictions, and emotional state.

[1503] 4. Using Emotion Engine: The server uses the EmotionEngine class to analyze the user's emotional state and detect stress state.

[1504] Specific examples

[1505] User profile: A user with a night owl lifestyle, gluten-free diet, and who has recently been feeling stressed.

[1506] Suggested meals: A healthy salad bowl, gluten-free pasta, and avocado toast with chamomile tea to help reduce stress, even late at night.

[1507] Prompt Sentence Examples

[1508] Create a Python program that takes a user's behavioral patterns, lifestyle, and emotional state as input and makes optimal meal recommendations. If the user has a nocturnal lifestyle, suggest healthy meals even late at night. If the user has specific dietary restrictions, suggest meals that fit those restrictions. Also, if the emotion engine detects the user's stress level, suggest meals that will help reduce stress.

[1509] This system makes it possible to suggest optimal meals based on the user's behavioral patterns, lifestyle, and emotional state, providing meals that are optimal for the user's health and emotional state.

[1510] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1511] Step 1:

[1512] The server collects personal data of the user.

[1513] Input: User behavioral patterns, life situations, emotional state

[1514] Output: Collected personal data

[1515] Specific operation: Using smartphone sensors and user input data, the system collects user behavioral patterns (e.g., exercise history, sleep time), lifestyle conditions (e.g., dietary history, daily rhythm), and emotional state (e.g., stress level, mood).

[1516] Step 2:

[1517] The server analyzes the collected personal data.

[1518] Input: Collected personal data

[1519] Output: Analysis results (understanding user behavior patterns, life situations, and emotional states)

[1520] What it does: Uses Python to analyze collected data and apply statistical analysis and machine learning models to understand users' behavioral patterns, life situations, and emotional states.

[1521] Step 3:

[1522] The server gets the current time.

[1523] Input: None

[1524] Output: Current time

[1525] Specific behavior: Uses the datetime module to get the current time.

[1526] Step 4:

[1527] The server makes optimal meal suggestions based on the user's behavioral patterns and living situation.

[1528] Input: Analysis results, current time

[1529] Output: Optimal meal suggestions

[1530] What it does: If the user has a night owl lifestyle, it will suggest healthy meals even late at night, and if the user has specific dietary restrictions, it will suggest menu items that fit those restrictions, such as a healthy salad bowl or gluten-free pasta even late at night.

[1531] Step 5:

[1532] The server uses an emotion engine to analyze the user's emotional state.

[1533] Input: A history of the user's emotional state

[1534] Output: Analysis of changes in emotional state

[1535] Specific behavior: Uses the EmotionEngine class to analyze the user's emotional state history and detect stress levels.

[1536] Step 6:

[1537] The server provides custom instruction that responds to changes in emotional state.

[1538] Input: Analysis of changes in emotional state

[1539] Output: Meal suggestions for stress management

[1540] What it does: If the user is feeling stressed, suggest a meal that will help reduce stress (e.g., chamomile tea and avocado toast).

[1541] Step 7:

[1542] The server notifies the user of the final meal suggestions.

[1543] Input: Optimal dietary suggestions, dietary suggestions for stress management

[1544] Output: User notification

[1545] Specific operation: Notifies users of optimal meal suggestions via a smartphone app.

[1546] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1547] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1548] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1549] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1550] [Third embodiment]

[1551] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1552] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1553] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1554] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1555] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1556] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1557] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1558] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1559] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1560] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1561] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1562] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1563] "Example 1"

[1564] In one embodiment of the present invention, the AI ​​personal trainer collects personal data from the user's smartphone or wearable device. The collected data covers a wide range of topics, including the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details. This data is sent to a cloud server and analyzed by the AI ​​personal trainer.

[1565] "Example 2"

[1566] The AI ​​personal trainer uses the collected data to provide customized guidance tailored to the user's goals and needs. For example, if a user is trying to lose weight, it will suggest appropriate dietary and exercise regimes. If a user is trying to maintain their health, it will suggest appropriate lifestyle and exercise habits.

[1567] "Example 3"

[1568] Furthermore, the AI ​​personal trainer understands the user's behavioral patterns and lifestyle and provides optimal advice and guidance based on that understanding. For example, if the user has a nocturnal lifestyle, it will suggest exercise and meal times that suit that lifestyle. Also, if the user has specific dietary restrictions, it will suggest meals that fit those restrictions.

[1569] The processing flow of each embodiment will be described below.

[1570] "Example 1"

[1571] Step 1: Collect personal data from the user's smartphone or wearable device. The collected data covers a wide range of topics, including the user's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary details.

[1572] Step 2: The collected data is sent to a server in the cloud.

[1573] Step 3: The AI ​​personal trainer analyzes the data sent to the cloud server.

[1574] "Example 2"

[1575] Step 1: The AI ​​personal trainer uses the collected data to provide custom coaching tailored to the user's goals and needs.

[1576] Step 2: For example, if the user is trying to lose weight, the app will suggest appropriate dietary and exercise regimes.

[1577] Step 3: If the user is aiming to maintain their health, the app will suggest appropriate lifestyle and exercise habits.

[1578] "Example 3"

[1579] Step 1: The AI ​​personal trainer understands the user's behavioral patterns and lifestyle.

[1580] Step 2: Use that understanding to provide the best advice and guidance.

[1581] Step 3: For example, if the user has a nocturnal lifestyle, the system will suggest exercise and meal times that fit that lifestyle. Also, if the user has specific dietary restrictions, the system will suggest meals that fit those restrictions.

[1582] Example 1

[1583] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1584] Conventional personal trainer systems have difficulty fully understanding an individual's behavioral patterns and lifestyle habits and providing appropriate advice. Furthermore, data collection and analysis are often done manually, making it difficult to provide efficient feedback.

[1585] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to a server on a cloud, means for storing the transmitted data in a database, means for analyzing the stored data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for transmitting generated feedback to a terminal, and means for displaying the feedback on the terminal. This makes it possible to automatically collect and analyze an individual's behavioral patterns and lifestyle habits and provide efficient and appropriate feedback.

[1586] "Individual personal data" refers to information such as an individual's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[1587] "Means of collection" refers to methods and devices for obtaining personal data of individuals using smartphones or wearable devices.

[1588] "Cloud servers" refer to remote servers accessible via the Internet that provide infrastructure for storing and processing data.

[1589] "Transmitting means" refers to a method or device for transferring collected data to a server on the cloud via the Internet.

[1590] "Means for storing data in a database" refers to a system or method for storing received data in a structured format.

[1591] "Means of analysis" refers to the algorithms and software used to analyze stored data and assess an individual's behavioral patterns and health status.

[1592] "Means for providing customized instruction" refers to a method or apparatus for generating advice or guidance based on the analysis results that is tailored to individual goals and needs.

[1593] "Means for sending feedback" refers to a method or device for transferring the generated feedback to the terminal.

[1594] "Means for displaying feedback" refers to a method or device for visually presenting feedback to a user at a terminal.

[1595] This invention relates to an AI personal trainer system that collects and analyzes personal data of individuals and provides customized instruction tailored to their individual goals and needs. Specific embodiments of this system will be described below.

[1596] Users use smartphones and wearable devices to collect personal data such as behavioral patterns, lifestyle habits, health status, exercise volume, and dietary details. Specifically, they use smartphone applications and wearable devices. These devices record data such as the number of steps taken, heart rate, sleep patterns, and dietary details.

[1597] The device sends the collected data to a cloud server. The data is encrypted and sent over an internet connection. For example, a smartphone application sends step count data to a cloud server.

[1598] The server stores the received data in a database. Specifically, the server uses a database system such as MySQL to store the user's data in a structured format.

[1599] The server then passes the stored data to an AI personal trainer for analysis. The AI ​​personal trainer uses machine learning libraries such as Python and TensorFlow to analyze the data. For example, the server inputs the user's step count data into an AI model to evaluate the user's exercise progress.

[1600] The server generates feedback based on the analysis results. For example, an AI personal trainer might generate feedback such as, "You haven't reached your goal of 10,000 steps today. I recommend walking another 2,000 steps."

[1601] The generated feedback is sent from the server to the device, which then displays the feedback to the user. For example, a smartphone application might display a notification to the user saying, "You haven't reached your goal of 10,000 steps today. We recommend that you walk another 2,000 steps."

[1602] Specific examples

[1603] Example 1: Exercise advice

[1604] Suppose a user uses a wearable device to record the number of steps they take each day. The wearable device collects the user's step count data and sends it to a smartphone application. The application sends this data to a server on the cloud, which stores the data in a database. The AI ​​personal trainer analyzes the stored data and generates feedback for the user, such as, "You haven't reached your goal of 10,000 steps today. I recommend walking another 2,000 steps." The server sends this feedback to the smartphone application, which then displays a notification to the user.

[1605] Example 2: Dietary advice

[1606] Suppose a user is using a smartphone application to record their diet. The user enters their dietary information into the application, which then sends this data to a server in the cloud. The server stores the data in a database, and an AI personal trainer analyzes the data. As a result of the analysis, feedback is generated for the user, such as "Today's meal lacks vegetables. We recommend adding a salad to your next meal." The server then sends this feedback to the smartphone application, which then displays a notification to the user.

[1607] Prompt Sentence Examples

[1608] "Please describe an AI personal trainer system that analyzes a user's behavioral patterns and health status and generates appropriate exercise and dietary advice. Please include the names of any specific devices or software."

[1609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1610] Step 1: Data collection

[1611] Users use smartphones or wearable devices to collect personal data such as behavioral patterns, lifestyle habits, health status, exercise amount, and dietary details. Specifically, they use smartphone applications or wearable devices. The input is the user's behavioral data, and the output is the collected personal data.

[1612] Step 2: Send data

[1613] The device sends the collected data to a server in the cloud. At this time, the data is encrypted and sent via an internet connection. Specifically, the smartphone application sends step count data to the cloud server. The input is the collected personal data, and the output is the data sent to the server.

[1614] Step 3: Save data

[1615] The server stores the received data in a database. Specifically, the server uses a database system such as MySQL to store the user's data in a structured format. The input is the data sent to the server, and the output is the data stored in the database.

[1616] Step 4: Data analysis

[1617] The server passes the stored data to the AI ​​personal trainer for analysis. The AI ​​personal trainer analyzes the data using machine learning libraries such as Python and TensorFlow. Specifically, the server inputs the user's step count data into an AI model to evaluate exercise progress. The input is the data stored in the database, and the output is the analysis results.

[1618] Step 5: Feedback generation

[1619] The server generates feedback based on the analysis results. Specifically, the AI ​​personal trainer generates feedback such as, "You haven't reached your goal of 10,000 steps today. I recommend you walk another 2,000 steps." The input is the analysis results, and the output is the generated feedback.

[1620] Step 6: Send your feedback

[1621] The server sends the generated feedback to the device. Specifically, the server sends the generated feedback to the smartphone application. The input is the generated feedback, and the output is the feedback sent to the device.

[1622] Step 7: Feedback display

[1623] The device displays feedback to the user. Specifically, a smartphone application displays a notification to the user saying, "You haven't reached your goal of 10,000 steps today. We recommend you walk another 2,000 steps." The input is the feedback sent to the device, and the output is the feedback displayed to the user.

[1624] (Application example 1)

[1625] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1626] While existing personal trainer systems can provide guidance based on an individual's health status and lifestyle habits, they lack the ability to provide meal plans or link with food delivery services, making it difficult for users to easily consume healthy meals. Furthermore, there is also the issue of not being able to fully meet the needs of users due to a lack of data analysis to generate individual meal plans.

[1627] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1628] In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for generating an optimal meal plan based on the analysis results, and means for providing meals based on the generated meal plan. This makes it possible to provide an optimal meal plan based on the user's health condition and lifestyle habits, and to link with food delivery services.

[1629] "Individual personal data" refers to information such as an individual's behavioral patterns, lifestyle habits, health status, amount of exercise, and dietary content.

[1630] "Means of collection" refers to methods of obtaining individuals' personal data using smartphones, wearable devices, etc.

[1631] "Means of analysis" refers to the method of sending collected personal data to a server on the cloud and having AI analyze the data.

[1632] "Means for providing customized instruction" refers to methods for providing advice and guidance tailored to individual goals and needs based on the analysis results.

[1633] "Method of generating meal plans" refers to the method by which the AI ​​creates optimal meal plans for users based on the analysis results.

[1634] "Means of providing meals" refers to the method of delivering meals to users through affiliated food delivery services based on the generated meal plan.

[1635] The system for implementing this invention includes a series of processes for collecting and analyzing personal data of an individual, generating an optimal meal plan, and providing meals through a food delivery service. A specific embodiment of this system is described below.

[1636] First, personal data is collected from the user's smartphone or wearable device. This data includes the user's behavioral patterns, lifestyle habits, health status, amount of exercise, dietary content, etc. This data is obtained, for example, using sensors in the smartphone or the data collection function of the wearable device.

[1637] The collected data is then sent to a server in the cloud. The server can be a cloud service such as AWS (Amazon Web Services). On the server, the collected data is analyzed using AI. The AI ​​model uses a machine learning framework such as TensorFlow.

[1638] Based on the analysis results, the server generates an optimal meal plan for the user. This meal plan is customized based on the user's health condition and lifestyle habits. The generated meal plan is sent to a partner food delivery service. The food delivery service can accept orders via API communication, for example.

[1639] For example, a user opens an app on their smartphone and enters their daily activity data and food preferences. The app sends this data to the cloud, where AI analyzes it. The AI ​​then generates a meal plan optimized for the user and sends an order to partner restaurants. The user can then receive healthy meals at home.

[1640] Examples of prompts for generative AI models include:

[1641] "Generate optimal meal plans by taking into account the user's activity level, food preferences, health status, and daily calorie intake."

[1642] In this way, it becomes possible to provide optimal meal plans based on the user's health condition and lifestyle habits, and to link with food delivery services.

[1643] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1644] Step 1:

[1645] Users collect personal data using smartphones and wearable devices.

[1646] Input: Data such as user behavior patterns, lifestyle habits, health status, exercise amount, and dietary details.

[1647] Data processing: Acquire data from sensors on smartphones and wearable devices and convert it into an appropriate format.

[1648] Output: Collected personal data.

[1649] Step 2:

[1650] The device sends the collected personal data to a server on the cloud.

[1651] Input: Collected personal data.

[1652] Data processing: The data is packaged using the appropriate protocol (e.g. HTTPS) to send it to the cloud server.

[1653] Output: Personal data sent to cloud server.

[1654] Step 3:

[1655] The server analyzes the received personal data.

[1656] Input: Personal data sent to the cloud server.

[1657] Data Computing: Using AI models (e.g., TensorFlow) to analyze data and assess the user's health and lifestyle.

[1658] Output: Analysis results (e.g., user's health status, lifestyle assessment).

[1659] Step 4:

[1660] The server generates an optimal meal plan based on the analysis results.

[1661] Input: Analysis results.

[1662] Data Computation: Generative AI models are used to generate optimal meal plans based on the user's health and lifestyle habits.

[1663] Output: The generated meal plan.

[1664] Step 5:

[1665] The server sends the generated meal plan to a partner food delivery service.

[1666] Input: The generated meal plan.

[1667] Data processing: Convert the meal plan into a format suitable for the food delivery service's API and send it.

[1668] Output: Meal plan sent to food delivery service.

[1669] Step 6:

[1670] The food delivery service prepares and delivers meals to users based on their meal plan.

[1671] Input: The meal plan sent to the food delivery service.

[1672] What happens: The food delivery service prepares the meal and delivers it to you at the specified time and location.

[1673] Output: The meal delivered to the user.

[1674] In this way, optimal meal plans based on the user's health condition and lifestyle habits can be provided, and collaboration with food delivery services can be achieved.

[1675] Example 2

[1676] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1677] Conventional personal trainer systems lack the data collection and analysis required to provide customized training tailored to individual goals and needs, resulting in insufficient individualized training. Additionally, it was difficult to understand the user's behavioral patterns, habits, and lifestyle, and provide optimal advice based on that.

[1678] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1679] In this invention, the server includes means for collecting personal data of an individual, means for transmitting the collected personal data to the server, means for the server to store the received data in a database, means for the server to analyze the data using a generative AI model, means for the server to generate customized instruction tailored to individual goals and needs based on the analysis results, means for transmitting the generated customized instruction to a terminal, and means for the terminal to display the customized instruction to the user. This makes it possible to provide highly accurate customized instruction tailored to the user's goals and needs.

[1680] "Individual personal data" refers to information about an individual, such as the user's goals, weight, height, diet, and exercise habits.

[1681] A "server" is a computer system that stores data, analyzes it, runs generative AI models, etc.

[1682] A "terminal" is a device (e.g., smartphone, tablet, PC) that a user uses to enter data and receive customized instruction.

[1683] A "database" is a system that allows a server to store and manage users' personal data.

[1684] A "generative AI model" is an artificial intelligence model that analyzes user data and generates custom instruction.

[1685] "Customized instruction" is personalized advice and guidance generated based on a user's goals and needs.

[1686] A "prompt" is a textual instruction to be input to a generative AI model.

[1687] An "HTTP POST request" is a type of communication protocol that allows a terminal to send data to a server.

[1688] An "INSERT query" is an SQL statement used to add new data to a database.

[1689] A "SELECT query" is an SQL statement used to retrieve data from a database.

[1690] This invention is a system that collects personal data of an individual, analyzes it using a generative AI model, and provides customized instruction tailored to the user's goals and needs. Specific embodiments of this system are described below.

[1691] Hardware and software used

[1692] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1693] Software: Generative AI models (e.g., GPT-4), database management systems (e.g., MySQL), and user interfaces

[1694] Data collection and transmission

[1695] The user uses the device to input personal data such as goals (e.g., diet, maintaining health), current weight, height, dietary habits, exercise habits, etc. The device then sends the input data to the server using an HTTP POST request.

[1696] Data storage and management

[1697] The server stores the received data in a database. The database management system (e.g. MySQL) organizes the data by user and executes INSERT queries to store the data.

[1698] Analyzing data and generating custom instruction

[1699] The server retrieves the user's data from the database using a SELECT query and generates prompts to input into a generative AI model (e.g., GPT-4). The generative AI model then generates custom instruction tailored to the user's goals and needs based on the prompts.

[1700] Sending and Viewing Custom Instructions

[1701] The server sends the generated custom instruction to the terminal as an HTTP response, and the terminal displays the received custom instruction to the user.

[1702] Specific examples

[1703] For users who want to lose weight

[1704] 1. User enters data:

[1705] The user enters the following information into the application form: "Goal weight: 60 kg, Current weight: 70 kg, Diet: Bread and coffee for breakfast, salad and chicken for lunch, fish and vegetables for dinner, Exercise habits: Jogging three times a week" and presses the "Submit" button.

[1706] 2. The device sends the data to the server:

[1707] The terminal transmits the input data to the server.

[1708] 3. The server saves the data to the database:

[1709] The server stores user data in a database.

[1710] 4. The server analyzes the data using the generative AI model:

[1711] The server retrieves user data from the database and generates prompt sentences for the generative AI model.

[1712] 5. The server generates the custom instructions:

[1713] The generative AI model suggests calorie restrictions and specific exercise plans based on the following: target weight: 60 kg, current weight: 70 kg, diet: bread and coffee for breakfast, salad and chicken for lunch, fish and vegetables for dinner, exercise habits: jogging three times a week.

[1714] 6. The server sends the generated instructions to the device:

[1715] The server transmits the generated instructions to the terminal.

[1716] 7. The device displays instructions to the user:

[1717] The device displays instructions to the user such as, "We recommend limiting your daily calorie intake to 1,500 kcal and continuing to jog three times a week. Add a protein shake to your breakfast and eat plenty of vegetables at lunch."

[1718] Prompt Sentence Examples

[1719] For users looking to lose weight:

[1720] "My current weight is 70 kg, and my goal weight is 60 kg. My daily diet consists of bread and coffee for breakfast, salad and chicken for lunch, and fish and vegetables for dinner. I go jogging three times a week. Please suggest an appropriate diet and exercise plan."

[1721] For users looking to stay healthy:

[1722] "I currently weigh 65 kg and don't have any specific weight loss goals. I try to eat a balanced diet each day, but I'd like some specific advice. I do yoga twice a week. Could you suggest a lifestyle and exercise plan to help me stay healthy?"

[1723] In this way, the system performs specific actions at each step and provides customized instruction to the user.

[1724] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1725] Step 1:

[1726] The user enters the data.

[1727] The user uses the device to input personal data such as goals (e.g., diet, maintaining health), current weight, height, dietary habits, and exercise habits. Specifically, the user enters the required information into a form in a dedicated application and presses the "Submit" button. The input data includes the target weight, current weight, dietary habits, and exercise habits.

[1728] Step 2:

[1729] The device sends the data to the server.

[1730] The terminal sends the data entered by the user to the server using an HTTP POST request. Specifically, the terminal converts the entered data into JSON format and sends it to the server's API endpoint. The input is the personal data entered by the user, and the output is the data sent to the server.

[1731] Step 3:

[1732] The server stores the data in a database.

[1733] The server stores the received data in a database. Specifically, the server uses a database management system (e.g., MySQL) to organize the data for each user and executes INSERT queries to store the data. The input is the data sent to the server, and the output is the data stored in the database.

[1734] Step 4:

[1735] The server analyzes the data using the generative AI model.

[1736] The server retrieves the user's data from the database using a SELECT query and generates a prompt to input to the generative AI model. Specifically, the server creates a prompt based on the user's goals and current situation and inputs it into the generative AI model (e.g., GPT-4). The input is the user's data retrieved from the database, and the output is the prompt to input to the generative AI model.

[1737] Step 5:

[1738] The server generates the custom instructions.

[1739] The generative AI model generates custom guidance tailored to the user's goals and needs based on the prompt. Specifically, the generative AI model analyzes the prompt and generates appropriate dietary and exercise plans and lifestyle advice. The input is the prompt, and the output is the generated custom guidance.

[1740] Step 6:

[1741] The server transmits the generated instruction to the terminal.

[1742] The server sends the generated custom instruction to the terminal as an HTTP response. Specifically, the server converts the generated instruction into JSON format and sends it to the terminal. The input is the generated custom instruction, and the output is the data sent to the terminal.

[1743] Step 7:

[1744] The terminal displays instructions to the user.

[1745] The device displays the customized instruction received from the server to the user. Specifically, the device updates the application UI and displays the generated instruction content in a user-friendly format. The input is the customized instruction received from the server, and the output is the instruction content displayed to the user.

[1746] (Application example 2)

[1747] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1748] While conventional personal trainer systems can provide customized instruction tailored to the user's goals and needs, they lack functions such as meal menu suggestions, ordering, and progress tracking, resulting in insufficient comprehensive support for users to achieve their goals. In particular, because there is no system that integrates meal suggestions and ordering, it takes time for users to actually consume the suggested meals, making continuous health management difficult.

[1749] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1750] In this invention, the server includes means for collecting personal data of an individual, means for analyzing the collected personal data, means for providing customized instruction tailored to individual goals and needs based on the analysis results, means for suggesting optimal meal menus based on the customized instruction, means for ordering the suggested meal menus in cooperation with an external meal provision service, and means for tracking the user's progress and providing feedback, thereby enabling the user to receive consistent health management, easily order the suggested meal menus, and continuously track their health status.

[1751] "Individual personal data" refers to individual information about a user, such as their weight, height, age, activity level, and goals.

[1752] "Means of collection" refers to the methods and devices used to obtain personal data from users.

[1753] "Means for analysis" refers to the methods and devices used to analyze collected personal data and extract information based on the user's goals and needs.

[1754] "Customized Instruction" refers to instruction or advice tailored to a user's individual goals and needs.

[1755] "AI personal trainer" refers to a system that uses artificial intelligence to analyze a user's personal data and provide customized instruction.

[1756] "Means for suggesting meal menus" refers to a method or device for generating and suggesting optimal meal menus based on the user's personal data and custom instructions.

[1757] "External Meal Delivery Service" refers to an external service provider that actually delivers the proposed meal menu.

[1758] "Means for ordering" refers to a method or device for placing an order for a proposed meal menu from an external meal service.

[1759] "Progress tracking means" means a method or device that continuously monitors and records a user's health status and progress toward goals.

[1760] "Means for providing feedback" refers to a method or device for providing appropriate advice or information to a user based on tracked progress data.

[1761] The system for implementing the present invention includes a series of means for collecting and analyzing personal data of an individual and providing customized guidance. Specific embodiments will be described below.

[1762] First, the user enters their personal data (weight, height, age, activity level, goals, etc.) through a smartphone application, which is then sent to a server and stored.

[1763] The server then analyzes the collected personal data using a generative AI model to generate custom guidance based on the user's goals and needs. For example, if the user is trying to lose weight, it will suggest low-calorie, nutritionally balanced meals.

[1764] The proposed meal menu can be ordered in cooperation with an external meal service. The user can easily order the proposed menu through the application. This ordering process is executed by the server using the API of the external meal service.

[1765] Furthermore, the server tracks the user's progress and provides feedback. It periodically collects data such as the user's weight and body fat percentage, and uses a generative AI model to evaluate the user's progress. Based on the evaluation results, the server provides appropriate advice and guidance to the user.

[1766] The following hardware and software are used to implement this system:

[1767] Smartphone (as a user interface)

[1768] Server (for storing and analyzing data)

[1769] Generative AI models (to generate custom instruction)

[1770] External meal service API (to order meals)

[1771] For example, if a user is trying to lose weight, they can input the following prompts into the generative AI model:

[1772] "Recommend optimal meals based on the user's weight, height, age, activity level, and goals. Make the suggested meals available for ordering through a food delivery service."

[1773] Based on this prompt, the generative AI model will suggest the optimal meal plan for the user, and the server will then order the plan from an external meal delivery service. Through this process, users can receive consistent health care.

[1774] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1775] Step 1:

[1776] The user inputs personal data through a smartphone application.

[1777] Input: Data such as the user's weight, height, age, activity level, and goals.

[1778] Output: The entered personal data is sent to the server.

[1779] Specific operation: The user enters their personal data into the application's input form and presses the "Submit" button. The application then sends the input data to the server.

[1780] Step 2:

[1781] The server stores the received personal data.

[1782] Input: Personal data submitted by the user.

[1783] Output: Saved personal data.

[1784] Specific operation: The server stores the received data in a database for later analysis.

[1785] Step 3:

[1786] The server analyzes the stored personal data.

[1787] Input: Stored personal data.

[1788] Output: Analysis results (customized instruction based on user goals and needs).

[1789] How it works: The server uses a generative AI model to analyze stored personal data and generate custom coaching based on the user's goals and needs.

[1790] Step 4:

[1791] The server will suggest the best meal menu based on your custom instructions.

[1792] Input: Analysis results (custom instruction).

[1793] Output: A suggested meal menu.

[1794] Specific operation: The server inputs a prompt into the generative AI model to generate the optimal meal menu for the user. Example prompt: "Please suggest the optimal meal menu based on the user's weight, height, age, activity level, and goals."

[1795] Step 5:

[1796] The server orders the proposed meal menu in cooperation with an external meal provision service.

[1797] Input: A suggested meal menu.

[1798] Output: Order confirmation information.

[1799] Specific operation: The server uses the API of the external meal service to order the proposed meal menu. If the order is successful, order confirmation information is returned to the server.

[1800] Step 6:

[1801] The server tracks the user's progress and provides feedback.

[1802] Input: Progress data such as the user's weight and body fat percentage.

[1803] Output: Feedback information.

[1804] Specific operation: The server periodically collects progress data from the user, evaluates the progress using a generative AI model, and provides appropriate advice and guidance to the user based on the evaluation results.

[1805] Example 3

[1806] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1807] Conventional personal trainer systems have difficulty fully understanding an individual's behavioral patterns and lifestyle and providing optimal advice and guidance based on that. Furthermore, insufficient pre-processing and analysis of collected data has led to problems with the accuracy of the advice provided.

[1808] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of an individual, means for preprocessing the collected personal data, means for analyzing the preprocessed data, means for providing customized guidance tailored to individual goals and needs based on the analysis results, means for generating optimal advice and guidance using a generative AI model, and means for providing the generated advice to the user's terminal. This makes it possible to understand an individual's behavioral patterns and living situation in detail and provide highly accurate advice and guidance based on that understanding.

[1809] "Individual personal data" refers to information about an individual's behavioral patterns, habits, living conditions, health status, etc.

[1810] "Means of collection" refers to the hardware and software functions used to obtain an individual's personal data.

[1811] "Preprocessing means" refers to data cleansing and filtering functions to convert collected data into a format suitable for analysis.

[1812] "Means of analysis" refers to algorithms and machine learning models that use preprocessed data to analyze individuals' behavioral patterns and living conditions.

[1813] "Means for providing customized coaching" refers to the ability to generate advice and guidance tailored to individual goals and needs based on the analysis results.

[1814] "Generative AI model" refers to an artificial intelligence model that generates optimal advice and guidance based on a user's behavioral patterns and living situation.

[1815] "Means for generating advice or guidance" refers to the ability to use a generative AI model to create optimal advice or guidance for a user.

[1816] "Means for providing to the user's device" refers to the function of sending the generated advice and guidance to the user's device, such as a smartphone or tablet.

[1817] This invention relates to an AI personal trainer system that understands an individual's behavioral patterns and lifestyle and provides optimal advice and guidance based on that understanding. Specific embodiments of this system are described below.

[1818] System configuration

[1819] This system includes a server, a user's device, and a generative AI model. The server collects, preprocesses, and analyzes the user's personal data to generate optimal advice and guidance. The user's device is a device such as a smartphone or tablet that receives the advice and guidance provided by the server.

[1820] Hardware and software used

[1821] Hardware: Smartwatches, smartphones, tablets

[1822] Software: Python, TensorFlow, Database Management System

[1823] Data collection and preprocessing

[1824] The server collects lifestyle data from users' devices. Specifically, it obtains data such as sleep time, exercise time, and meal times from sensors on smartwatches and smartphones. The collected data is stored in a database and preprocessed. This preprocessing includes filling in missing values ​​and removing outliers.

[1825] Analyzing the data

[1826] The pre-processed data is then analyzed using Python and TensorFlow. The server then applies machine learning models to analyze the user's behavioral patterns and lifestyle. For example, an algorithm is used to determine whether the user has a night-owl lifestyle.

[1827] Generating Advice

[1828] Based on the analysis results, the server uses a generative AI model to generate optimal advice and guidance tailored to the user's behavioral patterns and lifestyle. For example, for a user with a nocturnal lifestyle, the server suggests optimal times for exercise and meals at night.

[1829] Providing advice

[1830] The generated advice is sent from the server to the user's device, where the user can check the advice on their smartphone, tablet, or other device. Specific examples of advice provided include the following:

[1831] Suggested exercise time: "Since you have a night owl lifestyle, I recommend exercising between 8:00 and 9:00 PM."

[1832] Mealtime suggestion: "In keeping with your night-owl lifestyle, we recommend eating dinner between 7:00 and 8:00 p.m."

[1833] Prompt Sentence Examples

[1834] An example of a prompt sentence to input to the generative AI model is as follows:

[1835] User behavior: Night owl

[1836] User's life situation: Work continues late into the night

[1837] User dietary restrictions: Gluten-free

[1838] Generated advice: Suggested exercise and meal times to suit night-owl lifestyles, gluten-free meal suggestions

[1839] In this way, the AI ​​personal trainer can understand the user's behavioral patterns and lifestyle in detail and provide highly accurate advice and guidance based on that understanding. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[1840] Step 1: Collect user data

[1841] The server collects lifestyle data from the user's device. As input, it receives data such as sleep time, exercise time, and meal time obtained from sensors on the smartwatch or smartphone. Specifically, the server obtains this data through an API and stores it in a database. As output, the collected raw data is stored in the database.

[1842] Step 2: Preprocessing the data

[1843] The server preprocesses the collected data. It uses the raw data stored in the database as input. Specifically, the server performs functions such as filling in missing values ​​and removing outliers. For example, if a user forgets to record some data, it applies an algorithm to fill in the missing data. It also detects and removes outliers such as abnormally high heart rates or step counts. The output is clean, preprocessed data.

[1844] Step 3: Analyze behavioral patterns

[1845] The server uses the preprocessed data to analyze the user's behavioral patterns. The preprocessed, clean data is used as input. Specifically, the server applies a machine learning model using Python and TensorFlow to determine whether the user has a night-owl lifestyle. For example, if the user's sleep time is from late at night to late in the morning, the server determines that the user has a night-owl lifestyle. The output is an analysis result of the user's behavioral patterns.

[1846] Step 4: Generating Advice

[1847] The server uses a generative AI model to generate optimal advice and guidance based on the analysis results. The behavioral pattern analysis results are used as input. Specifically, the server inputs prompt statements into the generative AI model to generate optimal advice for the user. For example, for a user with a nocturnal lifestyle, it would suggest appropriate exercise and meal times at night. The generated advice and guidance is obtained as output.

[1848] Step 5: Providing advice

[1849] The server provides the generated advice to the user's device. The generated advice and guidance are used as input. Specifically, the server sends the advice to the user's smartphone or tablet. The user can check the advice on their device. For example, if the user has a nocturnal lifestyle, the following notification will be displayed on the device: "You have a nocturnal lifestyle, so we recommend that you exercise between 8:00 and 9:00 p.m." The output is the advice displayed on the user's device.

[1850] (Application example 3)

[1851] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1852] Traditional personal trainer systems struggled to fully understand users' behavioral patterns and lifestyles and provide customized instruction tailored to their individual needs. They also lacked the functionality to provide real-time feedback and advice, preventing them from effectively supporting users in managing their health and achieving their fitness goals. Furthermore, they were inadequate in proposing optimal exercise and meal plans based on users' lifestyle habits and dietary restrictions.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for collecting personal data of individuals, means for analyzing the collected personal data, means including an artificial intelligence personal trainer that provides customized instruction tailored to individual goals and needs based on the analysis results, means for learning the user's behavioral patterns and lifestyle and proposing optimal exercise and meal plans, and means for providing real-time feedback and advice. This makes it possible to propose optimal exercise and meal plans based on the user's behavioral patterns and lifestyle, and to effectively support the user in managing their health and achieving their fitness goals through real-time feedback and advice.

[1854] "Individual personal data" refers to individual information such as a user's behavioral patterns, lifestyle habits, health status, and dietary restrictions.

[1855] "Means of collection" refers to methods of obtaining individuals' personal data using sensors, smartphone applications, wearable devices, etc.

[1856] "Means of analysis" refers to the algorithms and software used to process collected personal data and understand users' behavioral patterns and living situations.

[1857] An "artificial intelligence personal trainer" is a system that uses machine learning and artificial intelligence technology to provide custom instruction tailored to a user's individual goals and needs.

[1858] "Means of learning user behavior patterns and lifestyle conditions" refers to algorithms and software that understand and learn from users' lifestyle habits and behavior patterns based on collected data.

[1859] "Means for suggesting optimal exercise and meal plans" refers to algorithms and software that generate and suggest individually customized exercise and meal plans based on the user's behavioral patterns and lifestyle.

[1860] "Means of providing real-time feedback and advice" refers to algorithms and software that provide immediate and appropriate feedback and advice based on the user's current state and behavior.

[1861] A system for implementing this invention includes an AI personal trainer that collects and analyzes personal data of an individual and proposes optimal exercise and meal plans to the user. Specific embodiments of this system are described below.

[1862] 1. System Configuration

[1863] The system consists of the following main components:

[1864] Data collection methods: Smartphones, wearable devices, sensors, etc. are used to collect data on users' behavioral patterns and living conditions.

[1865] Data analysis methods: Using algorithms and software (e.g., Python, machine learning libraries) to process collected data and understand users' behavioral patterns and life situations.

[1866] AI Personal Trainer: A system that uses machine learning and artificial intelligence technology to provide custom coaching tailored to a user's individual goals and needs.

[1867] Recommendation method: Algorithms and software that generate and suggest optimal exercise and meal plans based on the user's behavioral patterns and lifestyle.

[1868] Feedback tools: Algorithms and software that provide real-time feedback and advice.

[1869] 2. Data Collection and Analysis

[1870] The server collects data on the user's behavioral patterns and lifestyle from smartphones and wearable devices, including sleep patterns, exercise history, dietary habits, etc. The collected data is processed using data analysis tools to generate a model for understanding the user's behavioral patterns and lifestyle.

[1871] 3. Providing custom instruction

[1872] Based on the generated model, the AI ​​personal trainer will provide customized guidance tailored to the user's individual goals and needs. For example, if the user has a nocturnal lifestyle, it will suggest evening jogging or stretching. If the user is vegan, it will suggest a menu centered on tofu and vegetables.

[1873] 4. Real-time feedback

[1874] The server provides instant and appropriate feedback and advice based on the user's current condition and behavior, allowing users to receive real-time support and effective support in managing their health and achieving their fitness goals.

[1875] Specific examples

[1876] For example, if a user has a night owl lifestyle, the system will suggest evening jogging or stretching, or if the user is vegan, it will suggest tofu and vegetable-based meals.

[1877] Prompt Sentence Examples

[1878] "Recommend optimal exercise and meal plans based on the user's behavioral patterns and dietary restrictions. For example, if the user is a night owl, suggest evening jogging or stretching. If the user is vegan, suggest a menu centered around tofu and vegetables."

[1879] In this way, the system can provide users with advice tailored to their individual needs and effectively support them in managing their health and fitness goals.

[1880] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1881] Step 1:

[1882] The server collects data on the user's behavioral patterns and lifestyle from smartphones and wearable devices. Specifically, it acquires data on the user's sleep patterns, exercise history, dietary details, etc. The input is the data sent from the smartphone or wearable device, and the output is the collected personal data.

[1883] Step 2:

[1884] The server stores the collected personal data in a database. Specifically, it uses a database management system (e.g., MySQL) to organize and store data for each user. The input is the collected personal data, and the output is the data stored in the database.

[1885] Step 3:

[1886] The server analyzes the stored personal data. Specifically, it uses machine learning algorithms to generate a model for understanding the user's behavioral patterns and living situations. The input is the personal data stored in the database, and the output is a model for understanding the user's behavioral patterns and living situations.

[1887] Step 4:

[1888] The server then uses the generated model to provide customized guidance tailored to the user's individual goals and needs. Specifically, it proposes optimal exercise and meal plans based on the user's behavioral patterns and lifestyle. The input is the generated model, and the output is the content of the customized guidance.

[1889] Step 5:

[1890] The server provides real-time feedback and advice to users. Specifically, it instantly generates appropriate feedback and advice based on the user's current state and behavior, and notifies the user through a smartphone application. The input is the user's current state and behavior data, and the output is real-time feedback and advice.

[1891] Step 6:

[1892] The user receives and implements the feedback and advice provided by the server. Specifically, the user implements the exercise and meal plans notified to them through the smartphone application and sends the results back to the server. The input is the feedback and advice from the server, and the output is the data of the implementation results.

[1893] In this way, the system can provide users with advice tailored to their individual needs and effectively support them in managing their health and fitness goals.

[1894] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1895] "Example 1"

[1896] In one embodiment of the present invention, the AI ​​personal trainer includes an emotion engine that recognizes a user's emotions and tailors custom instruction based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it can suggest relaxation and exercises that contribute to relaxation. Alternatively, if the emotion engine recognizes that the user is feeling joy, it can suggest activities to maintain that joy.

[1897] "Example 2"

[1898] The emotion engine also provides optimal advice and guidance based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling sad, it will provide advice to alleviate that sadness. This advice could include appropriate exercise, diet, or relaxation techniques.

[1899] "Example 3"

[1900] Additionally, the emotion engine tracks a user's emotional state over time and analyzes changes. Based on this analysis, the emotion engine provides customized guidance that responds to changes in the user's emotional state. For example, if the emotion engine recognizes that a user has been feeling stressed for a period of time, it will suggest a long-term plan for stress management.

[1901] The processing flow of each embodiment will be described below.

[1902] "Example 1"

[1903] Step 1: The AI ​​personal trainer recognizes the user's emotions using an emotion engine.

[1904] Step 2: The emotion engine tailors custom instruction based on the perceived emotional state.

[1905] Step 3: For example, if the emotion engine recognizes that the user is feeling stressed, it will suggest relaxation and exercises that will help with relaxation.

[1906] "Example 2"

[1907] Step 1: The emotion engine provides optimal advice and guidance based on the user's emotional state.

[1908] Step 2: For example, if the emotion engine recognizes that the user is feeling sad, it will provide advice to alleviate that sadnes...

Claims

1. A first means for collecting personal data, which refers to information regarding a user's behavioral patterns, habits, living conditions, and health conditions; A second means for analyzing the collected personal data; a third means for generating a first prompt sentence for outputting advice tailored to the behavioral pattern and living situation of the user based on the analysis result of the personal data; a fourth means for inputting the generated first prompt sentence into a generative AI model to generate the advice; A fifth means for transmitting the generated advice to a terminal of the user; A system including:

2. a sixth means for recognizing an emotional state of the user from the personal data of the user using an emotion engine; the third means generates a second prompt sentence for suggesting an optimal meal menu for the emotional state of the user based on the emotional state of the user; The fourth means inputs the generated second prompt sentence into a generative AI model to generate the meal menu. The system of claim 1 .

3. and a seventh means for ordering the generated meal menu from a food delivery service. The system of claim 2.

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