System

Abstract

A system is provided.SOLUTION: A system comprising: means for conversing with an individual thinking of dieting; means for generating a diet menu and an exercise plan based on information collected by the conversation; means for acquiring and analyzing information of a weight scale and a photograph of an object eaten; and means for correcting the diet menu and the exercise plan based on a result of the analysis.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] For individuals considering dieting, it is difficult to create an appropriate meal menu and exercise plan on their own, making it difficult to effectively manage weight. Furthermore, selecting a menu that suits an individual's preferences and lifestyle requires a lot of time and knowledge, making it difficult to maintain motivation. Furthermore, recording and analyzing daily weight fluctuations and dietary information is cumbersome, making it difficult to maintain consistent management. There is a need to improve this situation and support individuals in achieving sustainable diets without straining themselves. [Means for solving the problem]

[0005] This invention provides a system that has a means for communicating with individuals considering dieting and generates a meal menu and exercise plan based on information collected from the conversation. It also has a means for acquiring and analyzing scale information and photos of food eaten, and a means for modifying the meal menu and exercise plan based on the analysis results. It also includes a means for sending encouraging messages based on the analysis results to maintain the individual's motivation. This system enables planning tailored to an individual's preferences and lifestyle habits, and also efficiently manages daily weight, resulting in a more sustainable diet.

[0006] "Individuals considering dieting" refers to people who are considering dietary restrictions and exercise in order to lose weight and manage their health.

[0007] "Means of conversation" refers to technologies and mechanisms for interacting with users in natural language and collecting information from users.

[0008] "Collected information" refers to data obtained through conversations with users, such as food preferences, allergies, and past dieting experiences.

[0009] "Meal menu" refers to the specific meal contents that an individual will consume during the diet period.

[0010] An "exercise plan" refers to the type, frequency, and intensity of exercise recommended to achieve an individual's weight loss goals.

[0011] "Means for generating" refers to technologies and tools for automatically creating meal menus and exercise plans based on collected information.

[0012] "Weight scale information" refers to weight data measured by an individual on a weight scale.

[0013] "Photos of what you ate" refers to photos of the food the user has eaten, and are the subject of analysis.

[0014] "Means of acquisition and analysis" refers to the technology and mechanisms that collect information from the scale and photos of what has been eaten, and analyze the contents.

[0015] "Modification measures" refers to techniques or mechanisms for changing or improving existing meal menus or exercise plans based on the analysis results.

[0016] "Encouraging Messages" refers to positive notifications and comments sent to maintain and improve an individual's motivation.

[0017] "Means to maintain motivation" refers to techniques and systems that increase an individual's motivation to continue dieting. [Brief explanation of the drawings]

[0018] [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. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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, a 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), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0039] The system of the present invention provides a customized meal menu and exercise plan to an individual considering dieting, and monitors and adjusts the progress. The program process will be explained below with specific examples.

[0040] Overview of program processing

[0041] Collection of User Information

[0042] The user accesses the Personal Diet AI Manager through a terminal and enters basic information such as name, age, gender, height, current weight, target weight, desired diet period, etc. The server receives this information and stores it in a database.

[0043] Gathering more information with conversational AI

[0044] The server then uses conversational AI to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. Based on the user's answers, the server updates the user profile.

[0045] Generate meal menus and exercise plans

[0046] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences and physical condition. The generated plan is then returned to the server and sent to the user's device for display.

[0047] Progress data collection and analysis

[0048] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[0049] Modifying plans and managing motivation

[0050] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to keep the user motivated.

[0051] Specific examples

[0052] 1. Collection of User Information

[0053] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The server receives this information and stores it in a database.

[0054] 2. Gathering detailed information

[0055] The server sends additional questions to the user through the terminal. Examples of questions include "What is your favorite food?" and "Do you have any allergies?" The user answers "I like Japanese food" and "I'm allergic to dairy products." The server receives these answers and updates the user profile.

[0056] 3. Generate meal menus and exercise plans

[0057] The server passes the updated user profile to the generation AI, which then generates an individual meal menu and exercise plan. The generation AI creates a "Japanese food-centered menu" and a "30-minute jogging plan three times a week" and sends them back to the server. The server then sends the generated plan to the user's device, where it is displayed to the user.

[0058] 4. Collecting and analyzing progress data

[0059] The user weighs themselves one week later and enters "65kg." They also take and send a photo of the food they ate that day. The server receives this and sends the data to the AI ​​system for classification. The AI ​​system analyzes the data, evaluates weight fluctuations and dietary details, and sends the results back to the server.

[0060] 5. Revising your plan and managing your motivation

[0061] The server then modifies the meal menu and exercise plan based on the analysis results. For example, it might suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also sends encouraging messages to the user, such as "You're on a great pace! Keep it up!"

[0062] In this way, the system of the present invention provides a customized meal menu and exercise plan based on the user's specific needs, supporting sustainable weight loss.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[0066] Step 2:

[0067] The terminal transmits the input information to the server.

[0068] Step 3:

[0069] The server stores the received information in a database.

[0070] Step 4:

[0071] The server then asks the user additional questions via the device using conversational AI, such as "What is your favorite food?" or "Do you have any allergies?"

[0072] Step 5:

[0073] The user answers additional questions using the terminal.

[0074] Step 6:

[0075] The terminal sends the user's answer to the server.

[0076] Step 7:

[0077] The server receives the user's response and updates the user profile.

[0078] Step 8:

[0079] The server passes the updated user profile to the generation AI.

[0080] Step 9:

[0081] The generation AI generates a customized meal menu and exercise plan based on the user's preferences and information, and sends the results to the server.

[0082] Step 10:

[0083] The server transmits the generated meal menu and exercise plan to the user's terminal.

[0084] Step 11:

[0085] The device displays a meal menu and exercise plan to the user.

[0086] Step 12:

[0087] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[0088] Step 13:

[0089] The device sends the weight data and a photo of what you ate to the server.

[0090] Step 14:

[0091] The server sends the received data to the identification AI and requests it to analyze it.

[0092] Step 15:

[0093] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[0094] Step 16:

[0095] The server then modifies the meal menu and exercise plan based on the analysis results.

[0096] Step 17:

[0097] The server sends the modified plan to the user's terminal.

[0098] Step 18:

[0099] The terminal displays the modified plan to the user.

[0100] Step 19:

[0101] The conversational AI generates encouraging messages for the user and sends them to the device via the server. The device displays the messages to the user and sends messages such as "Great pace, keep it up!" depending on the registered content.

[0102] The above is the specific processing flow of the system.

[0103] Example 1

[0104] Next, a description will be given of 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."

[0105] In modern society, it is difficult to provide a customized diet plan that suits an individual's lifestyle and food preferences, and it is also difficult to properly monitor progress and maintain motivation.

[0106] 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.

[0107] In this invention, the server includes a means for communicating with the individual via the communication terminal, a means for saving the individual's basic information in a database, a means for sending prompts to the generation AI and generating a meal menu and exercise plan customized for the individual, a means for acquiring weight information and meal photos periodically entered by the individual, a means for analyzing the acquired information and modifying the meal menu and exercise plan, and a means for sending encouraging messages to the individual. This makes it possible to provide an appropriate diet plan based on the individual's lifestyle and preferences and to continuously manage motivation based on the plan.

[0108] "Individual" refers to a user who uses this system to diet.

[0109] A "communications terminal" is a device used by an individual to exchange information with a server.

[0110] A "means of conversation" is a tool or algorithm that allows the server to engage in two-way communication with an individual.

[0111] "Basic information" refers to information such as name, age, sex, height, and weight that an individual provides to the server.

[0112] A "database" is a system in which a server stores basic information about individuals and other data.

[0113] "Generative AI" is artificial intelligence that creates meal menus and exercise plans based on personalized prompts.

[0114] A "prompt" is a document containing personalized requests and information for each individual that the server passes to the generating AI.

[0115] A "meal menu" is a plan created by the generative AI that includes food choices and meal schedules appropriate for an individual.

[0116] An "exercise plan" is a plan created by the generating AI that includes exercise content and schedules that are suitable for each individual.

[0117] "Weight information" refers to weight data that an individual measures periodically and inputs into the server.

[0118] A "meal photo" is an image that an individual takes and sends to a server to record the food they have eaten.

[0119] "Means of analysis" refers to algorithms or tools that analyze the data received by the server and extract the necessary information.

[0120] "Encouraging messages" are words of encouragement or consolation sent by the server to keep individuals motivated.

[0121] This invention is a system that provides a customized meal menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. This system is realized by integrating multiple hardware and software components, including a server, terminals, generation AI, classification AI, and database.

[0122] Collection of User Information

[0123] The user accesses the Personal Diet AI Manager through a device (smartphone or computer). The device screen displays an input screen for information such as name, age, gender, height, current weight, target weight, and desired diet period. The user enters this basic information, which is then received by the server and stored in a database. For example, the user might enter "Yamada Hanako, age 30, female, 160cm, 68kg, target weight 55kg, duration 3 months."

[0124] Gathering more information with conversational AI

[0125] The server then launches a conversational AI that prompts the user with detailed questions on their device, gathering additional information such as their food preferences, allergies, and past dieting experiences. When the user enters answers such as "I like Japanese food" or "I'm allergic to dairy products," the user profile is updated accordingly.

[0126] Generate meal menus and exercise plans

[0127] The updated user profile is sent to the generation AI as a prompt. For example, a prompt like "I'm a 30-year-old woman, my current weight is 68 kg, and my diet goal is 55 kg. I particularly like Japanese food, but I'm allergic to dairy products. Please suggest a customized meal menu and exercise plan that suits this user." Based on this, the generation AI generates a personalized meal menu and exercise plan, which are then displayed on the user's device via the server.

[0128] Progress data collection and analysis

[0129] Users periodically measure their weight and enter that data into their device. They also take photos of the food they eat and send them to the server from their device. For example, if a user enters their weight as "65 kg" one week later and sends photos of the food they have eaten, the server will send this data to the AI ​​recognition system and analyze the results.

[0130] Modifying plans and managing motivation

[0131] Based on the data analysis results returned by the classification AI, the server modifies the user's meal menu and exercise plan as needed. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to the user, such as, "You're on a great pace! Keep it up!"

[0132] In this way, the system of the present invention provides a continuously customized meal menu and exercise plan based on an individual's specific needs, helping them achieve a sustainable diet.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1: Collect user information

[0135] The user accesses the Personal Diet AI Manager through a terminal. The terminal screen displays an input form for information such as name, age, gender, height, current weight, target weight, and desired diet period. The basic information entered is sent to the server and stored in a database.

[0136] Input: Name, age, sex, height, weight, target weight, diet period

[0137] Output: Saved user basic information data

[0138] Specific operation: For example, the user enters "Yamada Hanako, 30 years old, female, 160cm, 68kg, goal weight 55kg, period 3 months." The server receives this information and records it in the database.

[0139] Step 2: Gathering more information with conversational AI

[0140] The server launches the conversational AI and displays detailed questions to the user. The user then inputs answers to the displayed questions via their device. Specific questions include food preferences, allergies, and past dieting experiences.

[0141] Input: User's answer regarding detailed information (e.g., I like Japanese food, I'm allergic to dairy products)

[0142] Output: Updated user profile

[0143] Specific operation: The server displays the question "What is your favorite food?" on the terminal, and the user enters "I like Japanese food." Similarly, the user answers "I'm allergic to dairy products" to the question "Do you have any allergies?" This information is sent to the server, and the user profile is updated.

[0144] Step 3: Create a meal plan and exercise plan

[0145] The server sends the updated user profile as a prompt to the AI ​​generator, which then generates a customized meal and exercise plan for the user based on the prompt. The plan is then sent to the server and displayed on the user's device.

[0146] Input: Updated user profile (prompt text)

[0147] Output: Customized meal and exercise plan

[0148] Specific operation: The server sends the generation AI a prompt message: "A 30-year-old woman, currently weighing 68 kg, with a diet goal of 55 kg. She particularly likes Japanese food and is allergic to dairy products. Please suggest a customized meal menu and exercise plan for this user." The generation AI generates a plan such as "Breakfast: Japanese salad, Lunch: Japanese-style rice balls, Dinner: Boiled fish, Exercise: 30 minutes of jogging three times a week" and sends it back to the server. The server then sends the results to the user's device and displays them.

[0149] Step 4: Collect and analyze progress data

[0150] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them from the device to the server. A classification AI analyzes this data and evaluates weight fluctuations and dietary content. The analysis results are sent back to the server, and the user profile is updated.

[0151] Input: Regularly entered weight data and photos of meals eaten

[0152] Output: Analyzed weight fluctuation data, dietary assessment data

[0153] Specific operation: The user weighs themselves one week later, enters "65kg," takes a photo of the food they ate that day, and sends it. The server receives this and sends it to the classification AI. The classification AI analyzes it and sends the evaluation results back to the server.

[0154] Step 5: Modify your plan and manage your motivation

[0155] The server then modifies the user's meal menu and exercise plan as needed based on the analysis results. The modified plan is then sent back to the user's device. The server also uses conversational AI to periodically send encouraging messages to keep the user motivated.

[0156] Input: Analyzed weight fluctuation data, dietary assessment data

[0157] Output: Modified meal and exercise plan, encouraging messages

[0158] Specific operation: The server makes suggestions for corrections, such as "Reduce the amount of salad you have for breakfast and increase the amount of boiled fish you have for dinner," and sends these to the user's device. The conversational AI also sends encouraging messages, such as "You're doing great! Keep it up!"

[0159] (Application example 1)

[0160] Next, a description will be given of Application 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."

[0161] Conventional health management systems for factory workers lack sufficient customization based on individual health conditions and preferences, and lack real-time analysis of progress data or features to encourage workers to maintain motivation. As a result, workers often stop taking care of their health. There is a need to solve this problem and provide a system that allows workers to take continuous health management.

[0162] 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.

[0163] In this invention, the server includes a means for collecting basic information about the person under health management, a means for using conversational AI to collect detailed information about the person under health management, and a means for generating a customized health management menu and exercise plan based on the collected information using generation AI. This allows for customization according to individual health conditions and preferences, making it possible to provide a system that allows workers to continuously manage their health.

[0164] A "health management subject" is an individual whose health condition is managed by the system.

[0165] "Basic information" refers to initial data about an individual, such as name, age, sex, weight, target weight, and health status.

[0166] "Conversational AI" is an artificial intelligence system that uses natural language processing to converse with users and collect and provide information.

[0167] "Detailed information" refers to specific personal data collected in addition to basic information such as dietary preferences, allergies, and past exercise experience.

[0168] "Generative AI" is artificial intelligence that has the ability to generate customized health management menus and exercise plans based on individual user information.

[0169] A "health management menu" is a dietary and lifestyle suggestion designed to improve a subject's health.

[0170] An "exercise plan" is an exercise regime designed to improve a subject's fitness or health.

[0171] "Weight scale information" refers to data obtained by a person who is subject to health management periodically measuring their weight and entering the results into the system.

[0172] "Photos of meals consumed" are image data taken to record the meals eaten by the subject.

[0173] "Analysis" refers to the process in which the system evaluates and analyzes the subject's health condition and progress of the plan based on the collected data.

[0174] "Encouraging messages" are positive messages that the system periodically sends to keep the subject motivated.

[0175] The system of this invention was developed to support the health management of factory workers. The system consists of the following main components:

[0176] 1. Hardware:

[0177] Smartphones: A user interface used daily by factory workers, they are responsible for inputting and outputting data.

[0178] Server: Provides a central database and data processing.

[0179] 2. Software:

[0180] Conversational AI: An artificial intelligence system that uses natural language processing (e.g., ChatGPT) to collect detailed information necessary for health management through conversations with workers.

[0181] Generative AI: Artificial intelligence (e.g., GPT-4) that can generate customized health and exercise plans based on collected data.

[0182] Data analysis AI: Analyzes collected data and evaluates progress of health management menus and exercise plans (e.g., TensorFlow).

[0183] Database: A system (e.g. MySQL) that stores and manages basic and detailed worker information.

[0184] Program processing overview

[0185] The server stores basic information entered by the worker on their smartphone in a database, and uses conversational AI to collect detailed information, which is then passed to a generation AI to generate a customized health management menu and exercise plan. The plan is then returned to the server and sent to the worker's smartphone for display.

[0186] Examples:

[0187] Factory workers access a smartphone application and enter basic information such as name, age, gender, current weight, and target weight. The server stores this information in a database. Next, a conversational AI asks for more detailed information such as food preferences and whether or not the worker has any allergies, and the AI ​​then generates a customized health management menu and exercise plan based on this information.

[0188] Example prompt sentences to use:

[0189] User Profile:

[0190] Name: Factory Worker A

[0191] Age: 35

[0192] Gender: Male

[0193] Weight: 75kg

[0194] Target weight: 70kg

[0195] Favorite food: Japanese food

[0196] Allergies: None

[0197] Previous diet experience: First time

[0198] Based on the above information, generate a one-week meal menu and exercise plan for the user, focusing on Japanese cuisine.

[0199] Specific operation of the system

[0200] The server uses conversational AI to collect detailed information based on the worker's input data. This information is then supplemented to generate an individually customized health management menu and exercise plan. In addition, daily progress data (weight scale information and photos of meals eaten) is collected, and the data analysis AI analyzes this data to automatically adjust the health management menu and exercise plan. This enables workers to continuously manage their health.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] A user accesses the system using a terminal and enters basic information (such as name, age, sex, current weight, target weight, and health condition). The server receives this basic information and stores it in a database.

[0204] Input: Basic information entered by the user into the device.

[0205] Specific operation: The user opens the smartphone application, enters their name, age, gender, current weight, target weight, etc. into the input form, and presses the submit button. The server stores this data in a database.

[0206] Output: Basic information data stored on the server.

[0207] Step 2:

[0208] The server uses conversational AI to collect detailed information from the user (such as food preferences, allergies, and past exercise history), which is then used to update the user profile.

[0209] Input: Basic information stored on the server.

[0210] How it works: The conversational AI asks the user a series of questions and receives their answers, such as "What's your favorite food?" or "Do you have any allergies?" The user answers through their device, and the answers are sent to the server.

[0211] Output: A user profile with updated details.

[0212] Step 3:

[0213] The server passes the updated user profile to the generation AI, which generates a customized health management menu and exercise plan. The generated plan is then returned to the server and sent to the user's device for display.

[0214] Input: The updated user profile.

[0215] How it works: The server passes the user profile to the generative AI model and generates a prompt. The generative AI model generates a customized menu and plan and returns the results to the server. The server sends the results to the user's device and displays them in the application.

[0216] Output: A customized health and exercise plan.

[0217] Step 4:

[0218] Users periodically enter their health progress data (for example, their weight measured on a scale or photos of the food they have eaten), and the server receives this data and stores it in a database.

[0219] Input: Health progress data entered by the user into the device.

[0220] How it works: Users use a smartphone application to input their weight and upload photos of their meals. The server receives this data and stores it in a database.

[0221] Output: Health progress data stored in a database.

[0222] Step 5:

[0223] The server uses data analysis AI to analyze the health progress data, which then evaluates the user's health status and progress towards their plan.

[0224] Input: Health progress data stored in a database.

[0225] Specific operation: The server passes health progress data to the data analysis AI, which evaluates weight fluctuations and dietary content. The analysis results are returned to the server.

[0226] Output: Analysis results on health status and plan progress.

[0227] Step 6:

[0228] Based on the analysis results, the server will adjust the health management menu and exercise plan as needed, and will also send encouraging messages to the user through conversational AI to keep them motivated.

[0229] Input: Analysis results on health status and plan progress.

[0230] Specific operation: Based on the analysis results, the server automatically modifies the menu and plan contents. For example, reduce the amount of salad at breakfast and increase the amount of fish at dinner. The modified plan is sent to the user's device. The conversational AI also sends encouraging messages to the user, such as "Great pace! Keep it up!"

[0231] Output: A modified health and exercise plan, along with encouraging messages.

[0232] 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.

[0233] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[0234] Overview of program processing

[0235] Collection of User Information

[0236] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[0237] Gathering more information with conversational AI and emotion engines

[0238] Next, the server uses conversational AI and an emotion engine to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. The emotion engine analyzes the user's emotions from their facial expressions and voice, taking their state into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[0239] Generate meal menus and exercise plans

[0240] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The plan is then returned to the server and sent to the user's device for display.

[0241] Progress data collection and analysis

[0242] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[0243] Modifying plans and managing motivation

[0244] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. In addition, the emotion engine recognizes the user's emotions and generates and sends encouraging messages according to their state through the conversational AI.

[0245] Specific examples

[0246] 1. Collection of User Information

[0247] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The device sends this information to the server, which then stores it in a database.

[0248] 2. Gathering detailed information

[0249] The server asks the user additional questions via the device using the conversational AI and emotion engine. These questions include "What is your favorite food?" and "Do you have any allergies?" When the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state. The server receives this information and updates the user profile.

[0250] 3. Generate meal menus and exercise plans

[0251] The server then passes the updated user profile to the AI ​​generator, which then generates a personalized meal plan and exercise plan, taking into account the emotional information obtained by the emotion engine. The plan is then sent back to the server and displayed on the user's device.

[0252] 4. Collecting and analyzing progress data

[0253] The user periodically weighs themselves and enters "65kg." They also take photos of the meals they ate that day and send them from their device to the server. The server then sends these to a classification AI, which analyzes their weight fluctuations and dietary details and sends the results back to the server.

[0254] 5. Revising your plan and managing your motivation

[0255] Based on the analysis results, the server will adjust the meal menu and exercise plan as appropriate. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The adjusted plan will then be sent back to the user's device. Based on the emotion engine, the conversational AI will also generate encouraging messages for the user, such as "You're doing a great job! Keep it up!"

[0256] In this way, the system of the present invention supports sustainable weight loss by providing a customized meal menu and exercise plan based on the user's specific needs and emotional state, and also motivates the user by recognizing their emotions and sending them encouraging messages accordingly.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[0260] Step 2:

[0261] The terminal transmits the input information to the server.

[0262] Step 3:

[0263] The server stores the received information in a database.

[0264] Step 4:

[0265] The server uses conversational AI and emotion engines to display additional questions to the user through the device, such as "What is your favorite food?" and "Do you have any allergies?"

[0266] Step 5:

[0267] The emotion engine analyzes the user's facial expressions and voice to determine their emotions at that time.

[0268] Step 6:

[0269] The user answers additional questions using the terminal.

[0270] Step 7:

[0271] The terminal sends the user's answer to the server.

[0272] Step 8:

[0273] The server receives the user's response and the emotion data from the emotion engine and updates the user profile.

[0274] Step 9:

[0275] The server passes the updated user profile to the generation AI.

[0276] Step 10:

[0277] The generative AI generates a customized meal menu and exercise plan based on the user's preferences, information, and emotional data, and sends the results to the server.

[0278] Step 11:

[0279] The server transmits the generated meal menu and exercise plan to the user's terminal.

[0280] Step 12:

[0281] The device displays a meal menu and exercise plan to the user.

[0282] Step 13:

[0283] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[0284] Step 14:

[0285] The device sends the weight data and a photo of what you ate to the server.

[0286] Step 15:

[0287] The server sends the received data to the identification AI and requests it to analyze it.

[0288] Step 16:

[0289] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[0290] Step 17:

[0291] The server then modifies the meal menu and exercise plan based on the analysis results.

[0292] Step 18:

[0293] The server sends the modified plan to the user's terminal.

[0294] Step 19:

[0295] The terminal displays the modified plan to the user.

[0296] Step 20:

[0297] The emotion engine reanalyzes the user's emotional state and sends that information to the conversational AI.

[0298] Step 21:

[0299] The conversational AI generates encouraging messages for the user and sends them to the device via the server, which then displays the messages to the user, such as "Great pace, keep it up!"

[0300] The above is the specific processing flow of the system.

[0301] Example 2

[0302] Next, a description will be given of 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."

[0303] Conventional diet support systems tend to fall into a one-size-fits-all approach because they are unable to fully consider individual preferences and emotional states. As a result, users' motivation tends to decline and the success rate of long-term dieting is low. In addition, the collected data cannot be effectively utilized, making it difficult to provide individually customized plans.

[0304] 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.

[0305] In this invention, the server includes a means for a user to input basic information, transmit it, and store it in a database, a means for collecting detailed information using a conversational AI and an emotion engine and updating the user profile, a means for a generation AI to generate a meal menu and exercise plan based on the updated user profile and display it, a means for a user to transmit weight data and photos of meals, which a recognition AI analyzes, and a means for modifying the meal menu and exercise plan based on the analyzed data and generating messages to maintain motivation. This makes it possible to provide a customized diet plan that takes into account the user's preferences and emotional state, and maintain long-term motivation.

[0306] "Entering user information" refers to the act of a user accessing the Personal Diet AI Manager using a device and entering basic information such as name, age, gender, height, current weight, target weight, and desired diet period.

[0307] A "database" is an information system for storing and managing collected user information and progress data.

[0308] "Conversational AI" is an artificial intelligence that collects information through dialogue with users in natural language and provides appropriate answers and information.

[0309] The "emotion engine" is a system that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[0310] "Generative AI" is artificial intelligence that generates customized meal menus and exercise plans based on user profiles and emotional information.

[0311] "Discrimination AI" is an artificial intelligence that analyzes weight data and photos of meals sent by users to determine calories and nutritional value.

[0312] A "prompt sentence" is an input sentence that gives specific instructions to the generation AI.

[0313] A "user profile" is data that integrates a user's basic information, detailed information, and emotional information.

[0314] The "meal menu" refers to the meal contents and specific menu items that are recommended for the user to consume.

[0315] An "exercise plan" is a recommended exercise or workout plan for a user.

[0316] The "message to maintain motivation" is a message to support and encourage the user to continue their diet.

[0317] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[0318] Hardware and software used

[0319] The system uses the following major hardware and software:

[0320] Device: The device a user uses to enter information or view results (e.g., smartphone, tablet, computer).

[0321] Server: A central system for managing data and running various AI models.

[0322] Generative AI: Artificial intelligence that generates customized meal menus and exercise plans based on user profiles.

[0323] Conversational AI: Artificial intelligence that gathers information through natural dialogue with users and provides appropriate answers and information.

[0324] Emotion engine: A system that analyzes emotions from the user's facial expressions and voice and recognizes their state.

[0325] Recognition AI: Artificial intelligence that analyzes weight data and food photos submitted by users to determine calories and nutritional value.

[0326] System Operation Overview

[0327] Collection of User Information

[0328] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[0329] Gathering more information with conversational AI and emotion engines

[0330] The server uses conversational AI to ask detailed questions to the user, such as about their food preferences, allergies, and past dieting experiences. It also uses an emotion engine to analyze the user's emotional state from their facial expressions and voice. The server updates the user's profile based on their answers and emotional information.

[0331] Generate meal menus and exercise plans

[0332] The server then passes the updated user profile information to the AI ​​generator, which then generates a customized meal and exercise plan. The plan is then sent back to the server and displayed on the user's device.

[0333] Progress data collection and analysis

[0334] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to a classification AI that analyzes weight fluctuations and dietary content.

[0335] Modifying plans and managing motivation

[0336] The server updates the meal menu and exercise plan as needed based on the analysis results of the classification AI. The revised plan is then sent back to the user's device. Additionally, the conversational AI generates encouraging messages based on the emotion engine and sends them to the device.

[0337] Examples of concrete examples and prompts

[0338] Specific examples

[0339] The user enters basic information such as "Name, age 30, female, 160cm, 68kg, 55kg, 3 months old" into the terminal.

[0340] The server uses conversational AI to ask questions such as "What's your favorite food?" and "Do you have any allergies?", and the user answers "I like Japanese food."

[0341] The emotion engine analyzes positive emotions from the user's facial expressions and stores them in a database.

[0342] A profile such as "User information: Female, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" is sent to the generation AI, and a customized plan is generated.

[0343] Prompt Sentence Examples

[0344] "User information: Female, 30 years old, 160cm, 68kg, goal weight 55kg, 3 months. User preferences: Likes Japanese food, allergy to dairy products. Emotional state: Positive. Generate a customized meal menu and exercise plan based on this information."

[0345] In this way, the system of the present invention provides customized support based on the user's specific needs and emotional state, enabling them to achieve sustainable weight loss.

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1: Enter your user information

[0348] Specific behavior:

[0349] The user accesses the personal diet AI manager using a device.

[0350] The terminal prompts the user to enter basic information such as name, age, sex, height, current weight, target weight, and desired diet period.

[0351] Input: Name, age, gender, height, current weight, goal weight, desired diet duration.

[0352] Output: The user's basic information is sent from the device to the server and stored in a database.

[0353] Step 2: Gathering more information with conversational AI and emotion engines

[0354] Specific behavior:

[0355] The server uses conversational AI to generate detailed questions for the user, such as their food preferences, allergies, and past dieting experiences.

[0356] The terminal displays these questions to the user, who then enters the answers.

[0357] The emotion engine analyzes the user's facial expressions and voice in real time to extract emotional information.

[0358] Input: Question sent by the server, user's answer, emotion data from the emotion engine.

[0359] Output: The user's detailed answers and sentiment information are sent to the server and the user profile is updated.

[0360] Step 3: Create a meal plan and exercise plan

[0361] Specific behavior:

[0362] The server sends the updated user profile to the generation AI, providing data such as "User information: female, 30 years old, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" as a prompt.

[0363] Generative AI generates customized meal menus and exercise plans based on the data.

[0364] The generated plan is returned to the server, which then transmits it to the user's terminal.

[0365] Input: Updated user profile, prompt statement.

[0366] Output: Generate a customized meal menu and exercise plan and display it on the user's device.

[0367] Step 4: Collect and analyze progress data

[0368] Specific behavior:

[0369] The user periodically measures their weight and inputs the data into the device. They also take photos of the food they eat and send them to the server via the device.

[0370] The server sends the received data to a recognition AI, which analyzes the meal contents from the photo and determines the calories and nutritional value.

[0371] Input: User weight data, meal photos.

[0372] Output: The analysis results from the classification AI are sent to the server and stored in a database.

[0373] Step 5: Modify your plan and manage your motivation

[0374] Specific behavior:

[0375] The server will revise the meal menu and exercise plan as needed based on the analysis results of the identification AI.

[0376] The updated plan is passed from the server to the generation AI, which generates a new, revised plan.

[0377] Based on the emotion engine, the conversational AI generates encouraging messages for the user and sends them to the device.

[0378] Input: Analysis results of the discrimination AI, user progress data, and emotional information from the emotion engine.

[0379] Output: Generates and sends updated meal and exercise plans and encouraging messages to the device.

[0380] The above is the specific flow of the program processing of this system, which enables users to achieve a sustainable diet and receive individually customized support.

[0381] (Application example 2)

[0382] Next, a description will be given of Application 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."

[0383] Conventional diet systems lacked sufficient support for providing customized meal menus and exercise plans suited to individual users, particularly lacking advice and encouragement tailored to each individual's emotional state. Furthermore, there was no established method for providing personalized support using a virtual environment. As a result, it was difficult for users to maintain their motivation, making it difficult to achieve diet results.

[0384] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for conversing with an individual considering dieting; means for generating a meal menu and an exercise plan based on information collected through the conversation; means for acquiring and analyzing scale information and photos of food eaten; means for modifying the meal menu and exercise plan based on the analysis results; means for recognizing the individual's emotional state using an emotion engine and generating messages based on this; and means for providing customized support to the individual through a virtual environment. This allows for the provision of a meal menu and exercise plan tailored to the emotional state and preferences of each individual user in real time, enabling sustained diet support while maintaining high user motivation.

[0385] An "individual considering dieting" is an individual who is trying to improve their diet and exercise in order to lose weight and maintain good health.

[0386] "Means for conversation" refers to means for two-way communication with users, and uses technologies such as voice recognition and text chat.

[0387] "Collected Information" refers to individual information entered by the user, such as dietary preferences, allergy information, and past dieting experiences.

[0388] The "means for generating a meal menu and exercise plan" is a means for creating an appropriate meal and exercise plan based on the user's individual information.

[0389] "Weight scale information" refers to weight data measured periodically by the user.

[0390] "Photos of what you ate" refers to images taken by a user to record what they ate.

[0391] "Means of analysis" refers to the means for processing collected data and analyzing the results, and uses artificial intelligence and image recognition technology, etc.

[0392] The "means for modifying a meal menu or exercise plan" refers to a means for updating or changing an existing meal menu or exercise plan based on the analysis results.

[0393] An "emotion engine" refers to software or hardware for analyzing emotions from a user's facial expressions and voice.

[0394] The "means for generating a message" is a means for creating a message of encouragement or advice based on the analyzed emotional data.

[0395] A "virtual environment" is a technology that provides a virtual space that is different from the real world, and is realized using smartphones, head-mounted displays, etc.

[0396] "Customized support" refers to personalized advice and assistance tailored to each individual user.

[0397] As an embodiment of the present invention, a system is configured in which a server, a terminal, and a user each have their own role. The specific configuration and processing content of this system will be described below.

[0398] 1. Collection of User Information

[0399] Users access the diet support application using a device (smartphone, smart glasses, head-mounted display, etc.). First, the user enters basic information such as name, age, gender, height, current weight, target weight, and desired diet period. This information is sent from the device to the server and stored in the server's database.

[0400] 2. Gathering more information with conversational AI and emotion engines

[0401] The server uses conversational AI to ask the user detailed questions via their device, such as about their food preferences, allergies, and past dieting experiences. At this time, an emotion engine (such as Google Cloud Video Intelligence API) analyzes the user's emotions from their facial expressions and voice, and these emotions are also taken into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[0402] 3. Generate meal menus and exercise plans

[0403] The server then passes the updated user profile to the generative AI model, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The generated plan is then sent from the server to the device and displayed to the user.

[0404] 4. Collecting and analyzing progress data

[0405] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to the classification AI for analysis.

[0406] 5. Revising your plan and managing your motivation

[0407] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the device. The server also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which are then sent to the device.

[0408] As a concrete example, suppose a user enters basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." This information is stored on the server, and the conversational AI asks questions such as "What is your favorite food?" and "Do you have any allergies?" If the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to determine their emotional state. The server uses this information to update the user profile and generate an individually customized meal menu and exercise plan.

[0409] An example of an input prompt for a generative AI model is as follows:

[0410] "Generate a diet menu suitable for the user if the user is 30 years old and the emotion indicated by the user's facial expression is happy."

[0411] In this way, it is possible to provide a user with a customized meal menu and exercise plan that addresses their specific needs and emotional state.

[0412] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0413] Step 1:

[0414] A user accesses a diet support application using a device and inputs basic information (name, age, sex, height, current weight, target weight, desired diet period). This information is sent from the device to a server and stored in the server's database. The input data is the information provided by the user, and the output is the user's basic profile information stored on the server.

[0415] Step 2:

[0416] The server uses conversational AI to ask the user additional questions via the device (such as food preferences, allergies, and past dieting experiences). The user enters their answers, and the emotion engine analyzes their emotions from their facial expressions and voice. This additional information and emotion data is sent to the server, which updates the user profile. The input data is the user's additional information and emotion data, and the output is an updated user profile.

[0417] Step 3:

[0418] The server passes the updated user profile to the generative AI model, which generates a meal menu and exercise plan. Specifically, a prompt is input into the generative AI model, which generates a plan that takes into account the user's preferences, physical condition, and emotional state. The generated plan is returned to the server and sent to the device to be displayed to the user. The input data is the updated user profile, and the output is the generated meal menu and exercise plan.

[0419] Step 4:

[0420] Users measure their weight periodically and enter the data into the device. They also take photos of the food they eat and send them to a server via the device. The server then sends this data to a classification AI for analysis. The input data are the weight measurement results and photos of the food, and the output is the data analyzed by the classification AI.

[0421] Step 5:

[0422] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the discrimination AI. The updated plan is then sent back to the device. It also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which then sends them to the device. The input data are the analysis results and the user's emotional data, and the output is the revised meal menu, exercise plan, and encouraging messages.

[0423] 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.

[0424] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0425] 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.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0439] The system of the present invention provides a customized meal menu and exercise plan to an individual considering dieting, and monitors and adjusts the progress. The program process will be explained below with specific examples.

[0440] Overview of program processing

[0441] Collection of User Information

[0442] The user accesses the Personal Diet AI Manager through a terminal and enters basic information such as name, age, gender, height, current weight, target weight, desired diet period, etc. The server receives this information and stores it in a database.

[0443] Gathering more information with conversational AI

[0444] The server then uses conversational AI to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. Based on the user's answers, the server updates the user profile.

[0445] Generate meal menus and exercise plans

[0446] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences and physical condition. The generated plan is then returned to the server and sent to the user's device for display.

[0447] Progress data collection and analysis

[0448] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[0449] Modifying plans and managing motivation

[0450] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to keep the user motivated.

[0451] Specific examples

[0452] 1. Collection of User Information

[0453] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The server receives this information and stores it in a database.

[0454] 2. Gathering detailed information

[0455] The server sends additional questions to the user through the terminal. Examples of questions include "What is your favorite food?" and "Do you have any allergies?" The user answers "I like Japanese food" and "I'm allergic to dairy products." The server receives these answers and updates the user profile.

[0456] 3. Generate meal menus and exercise plans

[0457] The server passes the updated user profile to the generation AI, which then generates an individual meal menu and exercise plan. The generation AI creates a "Japanese food-centered menu" and a "30-minute jogging plan three times a week" and sends them back to the server. The server then sends the generated plan to the user's device, where it is displayed to the user.

[0458] 4. Collecting and analyzing progress data

[0459] The user weighs themselves one week later and enters "65kg." They also take and send a photo of the food they ate that day. The server receives this and sends the data to the AI ​​system for classification. The AI ​​system analyzes the data, evaluates weight fluctuations and dietary details, and sends the results back to the server.

[0460] 5. Revising your plan and managing your motivation

[0461] The server then modifies the meal menu and exercise plan based on the analysis results. For example, it might suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also sends encouraging messages to the user, such as "You're on a great pace! Keep it up!"

[0462] In this way, the system of the present invention provides a customized meal menu and exercise plan based on the user's specific needs, supporting sustainable weight loss.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[0466] Step 2:

[0467] The terminal transmits the input information to the server.

[0468] Step 3:

[0469] The server stores the received information in a database.

[0470] Step 4:

[0471] The server then asks the user additional questions via the device using conversational AI, such as "What is your favorite food?" or "Do you have any allergies?"

[0472] Step 5:

[0473] The user answers additional questions using the terminal.

[0474] Step 6:

[0475] The terminal sends the user's answer to the server.

[0476] Step 7:

[0477] The server receives the user's response and updates the user profile.

[0478] Step 8:

[0479] The server passes the updated user profile to the generation AI.

[0480] Step 9:

[0481] The generation AI generates a customized meal menu and exercise plan based on the user's preferences and information, and sends the results to the server.

[0482] Step 10:

[0483] The server transmits the generated meal menu and exercise plan to the user's terminal.

[0484] Step 11:

[0485] The device displays a meal menu and exercise plan to the user.

[0486] Step 12:

[0487] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[0488] Step 13:

[0489] The device sends the weight data and a photo of what you ate to the server.

[0490] Step 14:

[0491] The server sends the received data to the identification AI and requests it to analyze it.

[0492] Step 15:

[0493] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[0494] Step 16:

[0495] The server then modifies the meal menu and exercise plan based on the analysis results.

[0496] Step 17:

[0497] The server sends the modified plan to the user's terminal.

[0498] Step 18:

[0499] The terminal displays the modified plan to the user.

[0500] Step 19:

[0501] The conversational AI generates encouraging messages for the user and sends them to the device via the server. The device displays the messages to the user and sends messages such as "Great pace, keep it up!" depending on the registered content.

[0502] The above is the specific processing flow of the system.

[0503] Example 1

[0504] Next, a description will be given of 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."

[0505] In modern society, it is difficult to provide a customized diet plan that suits an individual's lifestyle and food preferences, and it is also difficult to properly monitor progress and maintain motivation.

[0506] 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.

[0507] In this invention, the server includes a means for communicating with the individual via the communication terminal, a means for saving the individual's basic information in a database, a means for sending prompts to the generation AI and generating a meal menu and exercise plan customized for the individual, a means for acquiring weight information and meal photos periodically entered by the individual, a means for analyzing the acquired information and modifying the meal menu and exercise plan, and a means for sending encouraging messages to the individual. This makes it possible to provide an appropriate diet plan based on the individual's lifestyle and preferences and to continuously manage motivation based on the plan.

[0508] "Individual" refers to a user who uses this system to diet.

[0509] A "communications terminal" is a device used by an individual to exchange information with a server.

[0510] A "means of conversation" is a tool or algorithm that allows the server to engage in two-way communication with an individual.

[0511] "Basic information" refers to information such as name, age, sex, height, and weight that an individual provides to the server.

[0512] A "database" is a system in which a server stores basic information about individuals and other data.

[0513] "Generative AI" is artificial intelligence that creates meal menus and exercise plans based on personalized prompts.

[0514] A "prompt" is a document containing personalized requests and information for each individual that the server passes to the generating AI.

[0515] A "meal menu" is a plan created by the generative AI that includes food choices and meal schedules appropriate for an individual.

[0516] An "exercise plan" is a plan created by the generating AI that includes exercise content and schedules that are suitable for each individual.

[0517] "Weight information" refers to weight data that an individual measures periodically and inputs into the server.

[0518] A "meal photo" is an image that an individual takes and sends to a server to record the food they have eaten.

[0519] "Means of analysis" refers to algorithms or tools that analyze the data received by the server and extract the necessary information.

[0520] "Encouraging messages" are words of encouragement or consolation sent by the server to keep individuals motivated.

[0521] This invention is a system that provides a customized meal menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. This system is realized by integrating multiple hardware and software components, including a server, terminals, generation AI, classification AI, and database.

[0522] Collection of User Information

[0523] The user accesses the Personal Diet AI Manager through a device (smartphone or computer). The device screen displays an input screen for information such as name, age, gender, height, current weight, target weight, and desired diet period. The user enters this basic information, which is then received by the server and stored in a database. For example, the user might enter "Yamada Hanako, age 30, female, 160cm, 68kg, target weight 55kg, duration 3 months."

[0524] Gathering more information with conversational AI

[0525] The server then launches a conversational AI that prompts the user with detailed questions on their device, gathering additional information such as their food preferences, allergies, and past dieting experiences. When the user enters answers such as "I like Japanese food" or "I'm allergic to dairy products," the user profile is updated accordingly.

[0526] Generate meal menus and exercise plans

[0527] The updated user profile is sent to the generation AI as a prompt. For example, a prompt like "I'm a 30-year-old woman, my current weight is 68 kg, and my diet goal is 55 kg. I particularly like Japanese food, but I'm allergic to dairy products. Please suggest a customized meal menu and exercise plan that suits this user." Based on this, the generation AI generates a personalized meal menu and exercise plan, which are then displayed on the user's device via the server.

[0528] Progress data collection and analysis

[0529] Users periodically measure their weight and enter that data into their device. They also take photos of the food they eat and send them to the server from their device. For example, if a user enters their weight as "65 kg" one week later and sends photos of the food they have eaten, the server will send this data to the AI ​​recognition system and analyze the results.

[0530] Modifying plans and managing motivation

[0531] Based on the data analysis results returned by the classification AI, the server modifies the user's meal menu and exercise plan as needed. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to the user, such as, "You're on a great pace! Keep it up!"

[0532] In this way, the system of the present invention provides a continuously customized meal menu and exercise plan based on an individual's specific needs, helping them achieve a sustainable diet.

[0533] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0534] Step 1: Collect user information

[0535] The user accesses the Personal Diet AI Manager through a terminal. The terminal screen displays an input form for information such as name, age, gender, height, current weight, target weight, and desired diet period. The basic information entered is sent to the server and stored in a database.

[0536] Input: Name, age, sex, height, weight, target weight, diet period

[0537] Output: Saved user basic information data

[0538] Specific operation: For example, the user enters "Yamada Hanako, 30 years old, female, 160cm, 68kg, goal weight 55kg, period 3 months." The server receives this information and records it in the database.

[0539] Step 2: Gathering more information with conversational AI

[0540] The server launches the conversational AI and displays detailed questions to the user. The user then inputs answers to the displayed questions via their device. Specific questions include food preferences, allergies, and past dieting experiences.

[0541] Input: User's answer regarding detailed information (e.g., I like Japanese food, I'm allergic to dairy products)

[0542] Output: Updated user profile

[0543] Specific operation: The server displays the question "What is your favorite food?" on the terminal, and the user enters "I like Japanese food." Similarly, the user answers "I'm allergic to dairy products" to the question "Do you have any allergies?" This information is sent to the server, and the user profile is updated.

[0544] Step 3: Create a meal plan and exercise plan

[0545] The server sends the updated user profile as a prompt to the AI ​​generator, which then generates a customized meal and exercise plan for the user based on the prompt. The plan is then sent to the server and displayed on the user's device.

[0546] Input: Updated user profile (prompt text)

[0547] Output: Customized meal and exercise plan

[0548] Specific operation: The server sends the generation AI a prompt message: "A 30-year-old woman, currently weighing 68 kg, with a diet goal of 55 kg. She particularly likes Japanese food and is allergic to dairy products. Please suggest a customized meal menu and exercise plan for this user." The generation AI generates a plan such as "Breakfast: Japanese salad, Lunch: Japanese-style rice balls, Dinner: Boiled fish, Exercise: 30 minutes of jogging three times a week" and sends it back to the server. The server then sends the results to the user's device and displays them.

[0549] Step 4: Collect and analyze progress data

[0550] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them from the device to the server. A classification AI analyzes this data and evaluates weight fluctuations and dietary content. The analysis results are sent back to the server, and the user profile is updated.

[0551] Input: Regularly entered weight data and photos of meals eaten

[0552] Output: Analyzed weight fluctuation data, dietary assessment data

[0553] Specific operation: The user weighs themselves one week later, enters "65kg," takes a photo of the food they ate that day, and sends it. The server receives this and sends it to the classification AI. The classification AI analyzes it and sends the evaluation results back to the server.

[0554] Step 5: Modify your plan and manage your motivation

[0555] The server then modifies the user's meal menu and exercise plan as needed based on the analysis results. The modified plan is then sent back to the user's device. The server also uses conversational AI to periodically send encouraging messages to keep the user motivated.

[0556] Input: Analyzed weight fluctuation data, dietary assessment data

[0557] Output: Modified meal and exercise plan, encouraging messages

[0558] Specific operation: The server makes suggestions for corrections, such as "Reduce the amount of salad you have for breakfast and increase the amount of boiled fish you have for dinner," and sends these to the user's device. The conversational AI also sends encouraging messages, such as "You're doing great! Keep it up!"

[0559] (Application example 1)

[0560] Next, a description will be given of Application 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."

[0561] Conventional health management systems for factory workers lack sufficient customization based on individual health conditions and preferences, and lack real-time analysis of progress data or features to encourage workers to maintain motivation. As a result, workers often stop taking care of their health. There is a need to solve this problem and provide a system that allows workers to take continuous health management.

[0562] 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.

[0563] In this invention, the server includes a means for collecting basic information about the person under health management, a means for using conversational AI to collect detailed information about the person under health management, and a means for generating a customized health management menu and exercise plan based on the collected information using generation AI. This allows for customization according to individual health conditions and preferences, making it possible to provide a system that allows workers to continuously manage their health.

[0564] A "health management subject" is an individual whose health condition is managed by the system.

[0565] "Basic information" refers to initial data about an individual, such as name, age, sex, weight, target weight, and health status.

[0566] "Conversational AI" is an artificial intelligence system that uses natural language processing to converse with users and collect and provide information.

[0567] "Detailed information" refers to specific personal data collected in addition to basic information such as dietary preferences, allergies, and past exercise experience.

[0568] "Generative AI" is artificial intelligence that has the ability to generate customized health management menus and exercise plans based on individual user information.

[0569] A "health management menu" is a dietary and lifestyle suggestion designed to improve a subject's health.

[0570] An "exercise plan" is an exercise regime designed to improve a subject's fitness or health.

[0571] "Weight scale information" refers to data obtained by a person who is subject to health management periodically measuring their weight and entering the results into the system.

[0572] "Photos of meals consumed" are image data taken to record the meals eaten by the subject.

[0573] "Analysis" refers to the process in which the system evaluates and analyzes the subject's health condition and progress of the plan based on the collected data.

[0574] "Encouraging messages" are positive messages that the system periodically sends to keep the subject motivated.

[0575] The system of this invention was developed to support the health management of factory workers. The system consists of the following main components:

[0576] 1. Hardware:

[0577] Smartphones: A user interface used daily by factory workers, they are responsible for inputting and outputting data.

[0578] Server: Provides a central database and data processing.

[0579] 2. Software:

[0580] Conversational AI: An artificial intelligence system that uses natural language processing (e.g., ChatGPT) to collect detailed information necessary for health management through conversations with workers.

[0581] Generative AI: Artificial intelligence (e.g., GPT-4) that can generate customized health and exercise plans based on collected data.

[0582] Data analysis AI: Analyzes collected data and evaluates progress of health management menus and exercise plans (e.g., TensorFlow).

[0583] Database: A system (e.g. MySQL) that stores and manages basic and detailed worker information.

[0584] Program processing overview

[0585] The server stores basic information entered by the worker on their smartphone in a database, and uses conversational AI to collect detailed information, which is then passed to a generation AI to generate a customized health management menu and exercise plan. The plan is then returned to the server and sent to the worker's smartphone for display.

[0586] Examples:

[0587] Factory workers access a smartphone application and enter basic information such as name, age, gender, current weight, and target weight. The server stores this information in a database. Next, a conversational AI asks for more detailed information such as food preferences and whether or not the worker has any allergies, and the AI ​​then generates a customized health management menu and exercise plan based on this information.

[0588] Example prompt sentences to use:

[0589] User Profile:

[0590] Name: Factory Worker A

[0591] Age: 35

[0592] Gender: Male

[0593] Weight: 75kg

[0594] Target weight: 70kg

[0595] Favorite food: Japanese food

[0596] Allergies: None

[0597] Previous diet experience: First time

[0598] Based on the above information, generate a one-week meal menu and exercise plan for the user, focusing on Japanese cuisine.

[0599] Specific operation of the system

[0600] The server uses conversational AI to collect detailed information based on the worker's input data. This information is then supplemented to generate an individually customized health management menu and exercise plan. In addition, daily progress data (weight scale information and photos of meals eaten) is collected, and the data analysis AI analyzes this data to automatically adjust the health management menu and exercise plan. This enables workers to continuously manage their health.

[0601] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0602] Step 1:

[0603] A user accesses the system using a terminal and enters basic information (such as name, age, sex, current weight, target weight, and health condition). The server receives this basic information and stores it in a database.

[0604] Input: Basic information entered by the user into the device.

[0605] Specific operation: The user opens the smartphone application, enters their name, age, gender, current weight, target weight, etc. into the input form, and presses the submit button. The server stores this data in a database.

[0606] Output: Basic information data stored on the server.

[0607] Step 2:

[0608] The server uses conversational AI to collect detailed information from the user (such as food preferences, allergies, and past exercise history), which is then used to update the user profile.

[0609] Input: Basic information stored on the server.

[0610] How it works: The conversational AI asks the user a series of questions and receives their answers, such as "What's your favorite food?" or "Do you have any allergies?" The user answers through their device, and the answers are sent to the server.

[0611] Output: A user profile with updated details.

[0612] Step 3:

[0613] The server passes the updated user profile to the generation AI, which generates a customized health management menu and exercise plan. The generated plan is then returned to the server and sent to the user's device for display.

[0614] Input: The updated user profile.

[0615] How it works: The server passes the user profile to the generative AI model and generates a prompt. The generative AI model generates a customized menu and plan and returns the results to the server. The server sends the results to the user's device and displays them in the application.

[0616] Output: A customized health and exercise plan.

[0617] Step 4:

[0618] Users periodically enter their health progress data (for example, their weight measured on a scale or photos of the food they have eaten), and the server receives this data and stores it in a database.

[0619] Input: Health progress data entered by the user into the device.

[0620] How it works: Users use a smartphone application to input their weight and upload photos of their meals. The server receives this data and stores it in a database.

[0621] Output: Health progress data stored in a database.

[0622] Step 5:

[0623] The server uses data analysis AI to analyze the health progress data, which then evaluates the user's health status and progress towards their plan.

[0624] Input: Health progress data stored in a database.

[0625] Specific operation: The server passes health progress data to the data analysis AI, which evaluates weight fluctuations and dietary content. The analysis results are returned to the server.

[0626] Output: Analysis results on health status and plan progress.

[0627] Step 6:

[0628] Based on the analysis results, the server will adjust the health management menu and exercise plan as needed, and will also send encouraging messages to the user through conversational AI to keep them motivated.

[0629] Input: Analysis results on health status and plan progress.

[0630] Specific operation: Based on the analysis results, the server automatically modifies the menu and plan contents. For example, reduce the amount of salad at breakfast and increase the amount of fish at dinner. The modified plan is sent to the user's device. The conversational AI also sends encouraging messages to the user, such as "Great pace! Keep it up!"

[0631] Output: A modified health and exercise plan, along with encouraging messages.

[0632] 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.

[0633] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[0634] Overview of program processing

[0635] Collection of User Information

[0636] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[0637] Gathering more information with conversational AI and emotion engines

[0638] Next, the server uses conversational AI and an emotion engine to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. The emotion engine analyzes the user's emotions from their facial expressions and voice, taking their state into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[0639] Generate meal menus and exercise plans

[0640] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The plan is then returned to the server and sent to the user's device for display.

[0641] Progress data collection and analysis

[0642] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[0643] Modifying plans and managing motivation

[0644] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. In addition, the emotion engine recognizes the user's emotions and generates and sends encouraging messages according to their state through the conversational AI.

[0645] Specific examples

[0646] 1. Collection of User Information

[0647] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The device sends this information to the server, which then stores it in a database.

[0648] 2. Gathering detailed information

[0649] The server asks the user additional questions via the device using the conversational AI and emotion engine. These questions include "What is your favorite food?" and "Do you have any allergies?" When the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state. The server receives this information and updates the user profile.

[0650] 3. Generate meal menus and exercise plans

[0651] The server then passes the updated user profile to the AI ​​generator, which then generates a personalized meal plan and exercise plan, taking into account the emotional information obtained by the emotion engine. The plan is then sent back to the server and displayed on the user's device.

[0652] 4. Collecting and analyzing progress data

[0653] The user periodically weighs themselves and enters "65kg." They also take photos of the meals they ate that day and send them from their device to the server. The server then sends these to a classification AI, which analyzes their weight fluctuations and dietary details and sends the results back to the server.

[0654] 5. Revising your plan and managing your motivation

[0655] Based on the analysis results, the server will adjust the meal menu and exercise plan as appropriate. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The adjusted plan will then be sent back to the user's device. Based on the emotion engine, the conversational AI will also generate encouraging messages for the user, such as "You're doing a great job! Keep it up!"

[0656] In this way, the system of the present invention supports sustainable weight loss by providing a customized meal menu and exercise plan based on the user's specific needs and emotional state, and also motivates the user by recognizing their emotions and sending them encouraging messages accordingly.

[0657] The processing flow will be explained below.

[0658] Step 1:

[0659] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[0660] Step 2:

[0661] The terminal transmits the input information to the server.

[0662] Step 3:

[0663] The server stores the received information in a database.

[0664] Step 4:

[0665] The server uses conversational AI and emotion engines to display additional questions to the user through the device, such as "What is your favorite food?" and "Do you have any allergies?"

[0666] Step 5:

[0667] The emotion engine analyzes the user's facial expressions and voice to determine their emotions at that time.

[0668] Step 6:

[0669] The user answers additional questions using the terminal.

[0670] Step 7:

[0671] The terminal sends the user's answer to the server.

[0672] Step 8:

[0673] The server receives the user's response and the emotion data from the emotion engine and updates the user profile.

[0674] Step 9:

[0675] The server passes the updated user profile to the generation AI.

[0676] Step 10:

[0677] The generative AI generates a customized meal menu and exercise plan based on the user's preferences, information, and emotional data, and sends the results to the server.

[0678] Step 11:

[0679] The server transmits the generated meal menu and exercise plan to the user's terminal.

[0680] Step 12:

[0681] The device displays a meal menu and exercise plan to the user.

[0682] Step 13:

[0683] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[0684] Step 14:

[0685] The device sends the weight data and a photo of what you ate to the server.

[0686] Step 15:

[0687] The server sends the received data to the identification AI and requests it to analyze it.

[0688] Step 16:

[0689] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[0690] Step 17:

[0691] The server then modifies the meal menu and exercise plan based on the analysis results.

[0692] Step 18:

[0693] The server sends the modified plan to the user's terminal.

[0694] Step 19:

[0695] The terminal displays the modified plan to the user.

[0696] Step 20:

[0697] The emotion engine reanalyzes the user's emotional state and sends that information to the conversational AI.

[0698] Step 21:

[0699] The conversational AI generates encouraging messages for the user and sends them to the device via the server, which then displays the messages to the user, such as "Great pace, keep it up!"

[0700] The above is the specific processing flow of the system.

[0701] Example 2

[0702] Next, a description will be given of 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."

[0703] Conventional diet support systems tend to fall into a one-size-fits-all approach because they are unable to fully consider individual preferences and emotional states. As a result, users' motivation tends to decline and the success rate of long-term dieting is low. In addition, the collected data cannot be effectively utilized, making it difficult to provide individually customized plans.

[0704] 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.

[0705] In this invention, the server includes a means for a user to input basic information, transmit it, and store it in a database, a means for collecting detailed information using a conversational AI and an emotion engine and updating the user profile, a means for a generation AI to generate a meal menu and exercise plan based on the updated user profile and display it, a means for a user to transmit weight data and photos of meals, which a recognition AI analyzes, and a means for modifying the meal menu and exercise plan based on the analyzed data and generating messages to maintain motivation. This makes it possible to provide a customized diet plan that takes into account the user's preferences and emotional state, and maintain long-term motivation.

[0706] "Entering user information" refers to the act of a user accessing the Personal Diet AI Manager using a device and entering basic information such as name, age, gender, height, current weight, target weight, and desired diet period.

[0707] A "database" is an information system for storing and managing collected user information and progress data.

[0708] "Conversational AI" is an artificial intelligence that collects information through dialogue with users in natural language and provides appropriate answers and information.

[0709] The "emotion engine" is a system that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[0710] "Generative AI" is artificial intelligence that generates customized meal menus and exercise plans based on user profiles and emotional information.

[0711] "Discrimination AI" is an artificial intelligence that analyzes weight data and photos of meals sent by users to determine calories and nutritional value.

[0712] A "prompt sentence" is an input sentence that gives specific instructions to the generation AI.

[0713] A "user profile" is data that integrates a user's basic information, detailed information, and emotional information.

[0714] The "meal menu" refers to the meal contents and specific menu items that are recommended for the user to consume.

[0715] An "exercise plan" is a recommended exercise or workout plan for a user.

[0716] The "message to maintain motivation" is a message to support and encourage the user to continue their diet.

[0717] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[0718] Hardware and software used

[0719] The system uses the following major hardware and software:

[0720] Device: The device a user uses to enter information or view results (e.g., smartphone, tablet, computer).

[0721] Server: A central system for managing data and running various AI models.

[0722] Generative AI: Artificial intelligence that generates customized meal menus and exercise plans based on user profiles.

[0723] Conversational AI: Artificial intelligence that gathers information through natural dialogue with users and provides appropriate answers and information.

[0724] Emotion engine: A system that analyzes emotions from the user's facial expressions and voice and recognizes their state.

[0725] Recognition AI: Artificial intelligence that analyzes weight data and food photos submitted by users to determine calories and nutritional value.

[0726] System Operation Overview

[0727] Collection of User Information

[0728] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[0729] Gathering more information with conversational AI and emotion engines

[0730] The server uses conversational AI to ask detailed questions to the user, such as about their food preferences, allergies, and past dieting experiences. It also uses an emotion engine to analyze the user's emotional state from their facial expressions and voice. The server updates the user's profile based on their answers and emotional information.

[0731] Generate meal menus and exercise plans

[0732] The server then passes the updated user profile information to the AI ​​generator, which then generates a customized meal and exercise plan. The plan is then sent back to the server and displayed on the user's device.

[0733] Progress data collection and analysis

[0734] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to a classification AI that analyzes weight fluctuations and dietary content.

[0735] Modifying plans and managing motivation

[0736] The server updates the meal menu and exercise plan as needed based on the analysis results of the classification AI. The revised plan is then sent back to the user's device. Additionally, the conversational AI generates encouraging messages based on the emotion engine and sends them to the device.

[0737] Examples of concrete examples and prompts

[0738] Specific examples

[0739] The user enters basic information such as "Name, age 30, female, 160cm, 68kg, 55kg, 3 months old" into the terminal.

[0740] The server uses conversational AI to ask questions such as "What's your favorite food?" and "Do you have any allergies?", and the user answers "I like Japanese food."

[0741] The emotion engine analyzes positive emotions from the user's facial expressions and stores them in a database.

[0742] A profile such as "User information: Female, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" is sent to the generation AI, and a customized plan is generated.

[0743] Prompt Sentence Examples

[0744] "User information: Female, 30 years old, 160cm, 68kg, goal weight 55kg, 3 months. User preferences: Likes Japanese food, allergy to dairy products. Emotional state: Positive. Generate a customized meal menu and exercise plan based on this information."

[0745] In this way, the system of the present invention provides customized support based on the user's specific needs and emotional state, enabling them to achieve sustainable weight loss.

[0746] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0747] Step 1: Enter your user information

[0748] Specific behavior:

[0749] The user accesses the personal diet AI manager using a device.

[0750] The terminal prompts the user to enter basic information such as name, age, sex, height, current weight, target weight, and desired diet period.

[0751] Input: Name, age, gender, height, current weight, goal weight, desired diet duration.

[0752] Output: The user's basic information is sent from the device to the server and stored in a database.

[0753] Step 2: Gathering more information with conversational AI and emotion engines

[0754] Specific behavior:

[0755] The server uses conversational AI to generate detailed questions for the user, such as their food preferences, allergies, and past dieting experiences.

[0756] The terminal displays these questions to the user, who then enters the answers.

[0757] The emotion engine analyzes the user's facial expressions and voice in real time to extract emotional information.

[0758] Input: Question sent by the server, user's answer, emotion data from the emotion engine.

[0759] Output: The user's detailed answers and sentiment information are sent to the server and the user profile is updated.

[0760] Step 3: Create a meal plan and exercise plan

[0761] Specific behavior:

[0762] The server sends the updated user profile to the generation AI, providing data such as "User information: female, 30 years old, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" as a prompt.

[0763] Generative AI generates customized meal menus and exercise plans based on the data.

[0764] The generated plan is returned to the server, which then transmits it to the user's terminal.

[0765] Input: Updated user profile, prompt statement.

[0766] Output: Generate a customized meal menu and exercise plan and display it on the user's device.

[0767] Step 4: Collect and analyze progress data

[0768] Specific behavior:

[0769] The user periodically measures their weight and inputs the data into the device. They also take photos of the food they eat and send them to the server via the device.

[0770] The server sends the received data to a recognition AI, which analyzes the meal contents from the photo and determines the calories and nutritional value.

[0771] Input: User weight data, meal photos.

[0772] Output: The analysis results from the classification AI are sent to the server and stored in a database.

[0773] Step 5: Modify your plan and manage your motivation

[0774] Specific behavior:

[0775] The server will revise the meal menu and exercise plan as needed based on the analysis results of the identification AI.

[0776] The updated plan is passed from the server to the generation AI, which generates a new, revised plan.

[0777] Based on the emotion engine, the conversational AI generates encouraging messages for the user and sends them to the device.

[0778] Input: Analysis results of the discrimination AI, user progress data, and emotional information from the emotion engine.

[0779] Output: Generates and sends updated meal and exercise plans and encouraging messages to the device.

[0780] The above is the specific flow of the program processing of this system, which enables users to achieve a sustainable diet and receive individually customized support.

[0781] (Application example 2)

[0782] Next, a description will be given of Application 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."

[0783] Conventional diet systems lacked sufficient support for providing customized meal menus and exercise plans suited to individual users, particularly lacking advice and encouragement tailored to each individual's emotional state. Furthermore, there was no established method for providing personalized support using a virtual environment. As a result, it was difficult for users to maintain their motivation, making it difficult to achieve diet results.

[0784] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for conversing with an individual considering dieting; means for generating a meal menu and an exercise plan based on information collected through the conversation; means for acquiring and analyzing scale information and photos of food eaten; means for modifying the meal menu and exercise plan based on the analysis results; means for recognizing the individual's emotional state using an emotion engine and generating messages based on this; and means for providing customized support to the individual through a virtual environment. This allows for the provision of a meal menu and exercise plan tailored to the emotional state and preferences of each individual user in real time, enabling sustained diet support while maintaining high user motivation.

[0785] An "individual considering dieting" is an individual who is trying to improve their diet and exercise in order to lose weight and maintain good health.

[0786] "Means for conversation" refers to means for two-way communication with users, and uses technologies such as voice recognition and text chat.

[0787] "Collected Information" refers to individual information entered by the user, such as dietary preferences, allergy information, and past dieting experiences.

[0788] The "means for generating a meal menu and exercise plan" is a means for creating an appropriate meal and exercise plan based on the user's individual information.

[0789] "Weight scale information" refers to weight data measured periodically by the user.

[0790] "Photos of what you ate" refers to images taken by a user to record what they ate.

[0791] "Means of analysis" refers to the means for processing collected data and analyzing the results, and uses artificial intelligence and image recognition technology, etc.

[0792] The "means for modifying a meal menu or exercise plan" refers to a means for updating or changing an existing meal menu or exercise plan based on the analysis results.

[0793] An "emotion engine" refers to software or hardware for analyzing emotions from a user's facial expressions and voice.

[0794] The "means for generating a message" is a means for creating a message of encouragement or advice based on the analyzed emotional data.

[0795] A "virtual environment" is a technology that provides a virtual space that is different from the real world, and is realized using smartphones, head-mounted displays, etc.

[0796] "Customized support" refers to personalized advice and assistance tailored to each individual user.

[0797] As an embodiment of the present invention, a system is configured in which a server, a terminal, and a user each have their own role. The specific configuration and processing content of this system will be described below.

[0798] 1. Collection of User Information

[0799] Users access the diet support application using a device (smartphone, smart glasses, head-mounted display, etc.). First, the user enters basic information such as name, age, gender, height, current weight, target weight, and desired diet period. This information is sent from the device to the server and stored in the server's database.

[0800] 2. Gathering more information with conversational AI and emotion engines

[0801] The server uses conversational AI to ask the user detailed questions via their device, such as about their food preferences, allergies, and past dieting experiences. At this time, an emotion engine (such as Google Cloud Video Intelligence API) analyzes the user's emotions from their facial expressions and voice, and these emotions are also taken into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[0802] 3. Generate meal menus and exercise plans

[0803] The server then passes the updated user profile to the generative AI model, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The generated plan is then sent from the server to the device and displayed to the user.

[0804] 4. Collecting and analyzing progress data

[0805] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to the classification AI for analysis.

[0806] 5. Revising your plan and managing your motivation

[0807] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the device. The server also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which are then sent to the device.

[0808] As a concrete example, suppose a user enters basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." This information is stored on the server, and the conversational AI asks questions such as "What is your favorite food?" and "Do you have any allergies?" If the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to determine their emotional state. The server uses this information to update the user profile and generate an individually customized meal menu and exercise plan.

[0809] An example of an input prompt for a generative AI model is as follows:

[0810] "Generate a diet menu suitable for the user if the user is 30 years old and the emotion indicated by the user's facial expression is happy."

[0811] In this way, it is possible to provide a user with a customized meal menu and exercise plan that addresses their specific needs and emotional state.

[0812] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0813] Step 1:

[0814] A user accesses a diet support application using a device and inputs basic information (name, age, sex, height, current weight, target weight, desired diet period). This information is sent from the device to a server and stored in the server's database. The input data is the information provided by the user, and the output is the user's basic profile information stored on the server.

[0815] Step 2:

[0816] The server uses conversational AI to ask the user additional questions via the device (such as food preferences, allergies, and past dieting experiences). The user enters their answers, and the emotion engine analyzes their emotions from their facial expressions and voice. This additional information and emotion data is sent to the server, which updates the user profile. The input data is the user's additional information and emotion data, and the output is an updated user profile.

[0817] Step 3:

[0818] The server passes the updated user profile to the generative AI model, which generates a meal menu and exercise plan. Specifically, a prompt is input into the generative AI model, which generates a plan that takes into account the user's preferences, physical condition, and emotional state. The generated plan is returned to the server and sent to the device to be displayed to the user. The input data is the updated user profile, and the output is the generated meal menu and exercise plan.

[0819] Step 4:

[0820] Users measure their weight periodically and enter the data into the device. They also take photos of the food they eat and send them to a server via the device. The server then sends this data to a classification AI for analysis. The input data are the weight measurement results and photos of the food, and the output is the data analyzed by the classification AI.

[0821] Step 5:

[0822] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the discrimination AI. The updated plan is then sent back to the device. It also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which then sends them to the device. The input data are the analysis results and the user's emotional data, and the output is the revised meal menu, exercise plan, and encouraging messages.

[0823] 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.

[0824] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0825] 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.

[0826] [Third embodiment]

[0827] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0828] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0839] The system of the present invention provides a customized meal menu and exercise plan to an individual considering dieting, and monitors and adjusts the progress. The program process will be explained below with specific examples.

[0840] Overview of program processing

[0841] Collection of User Information

[0842] The user accesses the Personal Diet AI Manager through a terminal and enters basic information such as name, age, gender, height, current weight, target weight, desired diet period, etc. The server receives this information and stores it in a database.

[0843] Gathering more information with conversational AI

[0844] The server then uses conversational AI to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. Based on the user's answers, the server updates the user profile.

[0845] Generate meal menus and exercise plans

[0846] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences and physical condition. The generated plan is then returned to the server and sent to the user's device for display.

[0847] Progress data collection and analysis

[0848] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[0849] Modifying plans and managing motivation

[0850] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to keep the user motivated.

[0851] Specific examples

[0852] 1. Collection of User Information

[0853] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The server receives this information and stores it in a database.

[0854] 2. Gathering detailed information

[0855] The server sends additional questions to the user through the terminal. Examples of questions include "What is your favorite food?" and "Do you have any allergies?" The user answers "I like Japanese food" and "I'm allergic to dairy products." The server receives these answers and updates the user profile.

[0856] 3. Generate meal menus and exercise plans

[0857] The server passes the updated user profile to the generation AI, which then generates an individual meal menu and exercise plan. The generation AI creates a "Japanese food-centered menu" and a "30-minute jogging plan three times a week" and sends them back to the server. The server then sends the generated plan to the user's device, where it is displayed to the user.

[0858] 4. Collecting and analyzing progress data

[0859] The user weighs themselves one week later and enters "65kg." They also take and send a photo of the food they ate that day. The server receives this and sends the data to the AI ​​system for classification. The AI ​​system analyzes the data, evaluates weight fluctuations and dietary details, and sends the results back to the server.

[0860] 5. Revising your plan and managing your motivation

[0861] The server then modifies the meal menu and exercise plan based on the analysis results. For example, it might suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also sends encouraging messages to the user, such as "You're on a great pace! Keep it up!"

[0862] In this way, the system of the present invention provides a customized meal menu and exercise plan based on the user's specific needs, supporting sustainable weight loss.

[0863] The processing flow will be explained below.

[0864] Step 1:

[0865] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[0866] Step 2:

[0867] The terminal transmits the input information to the server.

[0868] Step 3:

[0869] The server stores the received information in a database.

[0870] Step 4:

[0871] The server then asks the user additional questions via the device using conversational AI, such as "What is your favorite food?" or "Do you have any allergies?"

[0872] Step 5:

[0873] The user answers additional questions using the terminal.

[0874] Step 6:

[0875] The terminal sends the user's answer to the server.

[0876] Step 7:

[0877] The server receives the user's response and updates the user profile.

[0878] Step 8:

[0879] The server passes the updated user profile to the generation AI.

[0880] Step 9:

[0881] The generation AI generates a customized meal menu and exercise plan based on the user's preferences and information, and sends the results to the server.

[0882] Step 10:

[0883] The server transmits the generated meal menu and exercise plan to the user's terminal.

[0884] Step 11:

[0885] The device displays a meal menu and exercise plan to the user.

[0886] Step 12:

[0887] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[0888] Step 13:

[0889] The device sends the weight data and a photo of what you ate to the server.

[0890] Step 14:

[0891] The server sends the received data to the identification AI and requests it to analyze it.

[0892] Step 15:

[0893] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[0894] Step 16:

[0895] The server then modifies the meal menu and exercise plan based on the analysis results.

[0896] Step 17:

[0897] The server sends the modified plan to the user's terminal.

[0898] Step 18:

[0899] The terminal displays the modified plan to the user.

[0900] Step 19:

[0901] The conversational AI generates encouraging messages for the user and sends them to the device via the server. The device displays the messages to the user and sends messages such as "Great pace, keep it up!" depending on the registered content.

[0902] The above is the specific processing flow of the system.

[0903] Example 1

[0904] Next, a description will be given of 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."

[0905] In modern society, it is difficult to provide a customized diet plan that suits an individual's lifestyle and food preferences, and it is also difficult to properly monitor progress and maintain motivation.

[0906] 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.

[0907] In this invention, the server includes a means for communicating with the individual via the communication terminal, a means for saving the individual's basic information in a database, a means for sending prompts to the generation AI and generating a meal menu and exercise plan customized for the individual, a means for acquiring weight information and meal photos periodically entered by the individual, a means for analyzing the acquired information and modifying the meal menu and exercise plan, and a means for sending encouraging messages to the individual. This makes it possible to provide an appropriate diet plan based on the individual's lifestyle and preferences and to continuously manage motivation based on the plan.

[0908] "Individual" refers to a user who uses this system to diet.

[0909] A "communications terminal" is a device used by an individual to exchange information with a server.

[0910] A "means of conversation" is a tool or algorithm that allows the server to engage in two-way communication with an individual.

[0911] "Basic information" refers to information such as name, age, sex, height, and weight that an individual provides to the server.

[0912] A "database" is a system in which a server stores basic information about individuals and other data.

[0913] "Generative AI" is artificial intelligence that creates meal menus and exercise plans based on personalized prompts.

[0914] A "prompt" is a document containing personalized requests and information for each individual that the server passes to the generating AI.

[0915] A "meal menu" is a plan created by the generative AI that includes food choices and meal schedules appropriate for an individual.

[0916] An "exercise plan" is a plan created by the generating AI that includes exercise content and schedules that are suitable for each individual.

[0917] "Weight information" refers to weight data that an individual measures periodically and inputs into the server.

[0918] A "meal photo" is an image that an individual takes and sends to a server to record the food they have eaten.

[0919] "Means of analysis" refers to algorithms or tools that analyze the data received by the server and extract the necessary information.

[0920] "Encouraging messages" are words of encouragement or consolation sent by the server to keep individuals motivated.

[0921] This invention is a system that provides a customized meal menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. This system is realized by integrating multiple hardware and software components, including a server, terminals, generation AI, classification AI, and database.

[0922] Collection of User Information

[0923] The user accesses the Personal Diet AI Manager through a device (smartphone or computer). The device screen displays an input screen for information such as name, age, gender, height, current weight, target weight, and desired diet period. The user enters this basic information, which is then received by the server and stored in a database. For example, the user might enter "Yamada Hanako, age 30, female, 160cm, 68kg, target weight 55kg, duration 3 months."

[0924] Gathering more information with conversational AI

[0925] The server then launches a conversational AI that prompts the user with detailed questions on their device, gathering additional information such as their food preferences, allergies, and past dieting experiences. When the user enters answers such as "I like Japanese food" or "I'm allergic to dairy products," the user profile is updated accordingly.

[0926] Generate meal menus and exercise plans

[0927] The updated user profile is sent to the generation AI as a prompt. For example, a prompt like "I'm a 30-year-old woman, my current weight is 68 kg, and my diet goal is 55 kg. I particularly like Japanese food, but I'm allergic to dairy products. Please suggest a customized meal menu and exercise plan that suits this user." Based on this, the generation AI generates a personalized meal menu and exercise plan, which are then displayed on the user's device via the server.

[0928] Progress data collection and analysis

[0929] Users periodically measure their weight and enter that data into their device. They also take photos of the food they eat and send them to the server from their device. For example, if a user enters their weight as "65 kg" one week later and sends photos of the food they have eaten, the server will send this data to the AI ​​recognition system and analyze the results.

[0930] Modifying plans and managing motivation

[0931] Based on the data analysis results returned by the classification AI, the server modifies the user's meal menu and exercise plan as needed. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to the user, such as, "You're on a great pace! Keep it up!"

[0932] In this way, the system of the present invention provides a continuously customized meal menu and exercise plan based on an individual's specific needs, helping them achieve a sustainable diet.

[0933] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0934] Step 1: Collect user information

[0935] The user accesses the Personal Diet AI Manager through a terminal. The terminal screen displays an input form for information such as name, age, gender, height, current weight, target weight, and desired diet period. The basic information entered is sent to the server and stored in a database.

[0936] Input: Name, age, sex, height, weight, target weight, diet period

[0937] Output: Saved user basic information data

[0938] Specific operation: For example, the user enters "Yamada Hanako, 30 years old, female, 160cm, 68kg, goal weight 55kg, period 3 months." The server receives this information and records it in the database.

[0939] Step 2: Gathering more information with conversational AI

[0940] The server launches the conversational AI and displays detailed questions to the user. The user then inputs answers to the displayed questions via their device. Specific questions include food preferences, allergies, and past dieting experiences.

[0941] Input: User's answer regarding detailed information (e.g., I like Japanese food, I'm allergic to dairy products)

[0942] Output: Updated user profile

[0943] Specific operation: The server displays the question "What is your favorite food?" on the terminal, and the user enters "I like Japanese food." Similarly, the user answers "I'm allergic to dairy products" to the question "Do you have any allergies?" This information is sent to the server, and the user profile is updated.

[0944] Step 3: Create a meal plan and exercise plan

[0945] The server sends the updated user profile as a prompt to the AI ​​generator, which then generates a customized meal and exercise plan for the user based on the prompt. The plan is then sent to the server and displayed on the user's device.

[0946] Input: Updated user profile (prompt text)

[0947] Output: Customized meal and exercise plan

[0948] Specific operation: The server sends the generation AI a prompt message: "A 30-year-old woman, currently weighing 68 kg, with a diet goal of 55 kg. She particularly likes Japanese food and is allergic to dairy products. Please suggest a customized meal menu and exercise plan for this user." The generation AI generates a plan such as "Breakfast: Japanese salad, Lunch: Japanese-style rice balls, Dinner: Boiled fish, Exercise: 30 minutes of jogging three times a week" and sends it back to the server. The server then sends the results to the user's device and displays them.

[0949] Step 4: Collect and analyze progress data

[0950] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them from the device to the server. A classification AI analyzes this data and evaluates weight fluctuations and dietary content. The analysis results are sent back to the server, and the user profile is updated.

[0951] Input: Regularly entered weight data and photos of meals eaten

[0952] Output: Analyzed weight fluctuation data, dietary assessment data

[0953] Specific operation: The user weighs themselves one week later, enters "65kg," takes a photo of the food they ate that day, and sends it. The server receives this and sends it to the classification AI. The classification AI analyzes it and sends the evaluation results back to the server.

[0954] Step 5: Modify your plan and manage your motivation

[0955] The server then modifies the user's meal menu and exercise plan as needed based on the analysis results. The modified plan is then sent back to the user's device. The server also uses conversational AI to periodically send encouraging messages to keep the user motivated.

[0956] Input: Analyzed weight fluctuation data, dietary assessment data

[0957] Output: Modified meal and exercise plan, encouraging messages

[0958] Specific operation: The server makes suggestions for corrections, such as "Reduce the amount of salad you have for breakfast and increase the amount of boiled fish you have for dinner," and sends these to the user's device. The conversational AI also sends encouraging messages, such as "You're doing great! Keep it up!"

[0959] (Application example 1)

[0960] Next, a description will be given of Application 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."

[0961] Conventional health management systems for factory workers lack sufficient customization based on individual health conditions and preferences, and lack real-time analysis of progress data or features to encourage workers to maintain motivation. As a result, workers often stop taking care of their health. There is a need to solve this problem and provide a system that allows workers to take continuous health management.

[0962] 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.

[0963] In this invention, the server includes a means for collecting basic information about the person under health management, a means for using conversational AI to collect detailed information about the person under health management, and a means for generating a customized health management menu and exercise plan based on the collected information using generation AI. This allows for customization according to individual health conditions and preferences, making it possible to provide a system that allows workers to continuously manage their health.

[0964] A "health management subject" is an individual whose health condition is managed by the system.

[0965] "Basic information" refers to initial data about an individual, such as name, age, sex, weight, target weight, and health status.

[0966] "Conversational AI" is an artificial intelligence system that uses natural language processing to converse with users and collect and provide information.

[0967] "Detailed information" refers to specific personal data collected in addition to basic information such as dietary preferences, allergies, and past exercise experience.

[0968] "Generative AI" is artificial intelligence that has the ability to generate customized health management menus and exercise plans based on individual user information.

[0969] A "health management menu" is a dietary and lifestyle suggestion designed to improve a subject's health.

[0970] An "exercise plan" is an exercise regime designed to improve a subject's fitness or health.

[0971] "Weight scale information" refers to data obtained by a person who is subject to health management periodically measuring their weight and entering the results into the system.

[0972] "Photos of meals consumed" are image data taken to record the meals eaten by the subject.

[0973] "Analysis" refers to the process in which the system evaluates and analyzes the subject's health condition and progress of the plan based on the collected data.

[0974] "Encouraging messages" are positive messages that the system periodically sends to keep the subject motivated.

[0975] The system of this invention was developed to support the health management of factory workers. The system consists of the following main components:

[0976] 1. Hardware:

[0977] Smartphones: A user interface used daily by factory workers, they are responsible for inputting and outputting data.

[0978] Server: Provides a central database and data processing.

[0979] 2. Software:

[0980] Conversational AI: An artificial intelligence system that uses natural language processing (e.g., ChatGPT) to collect detailed information necessary for health management through conversations with workers.

[0981] Generative AI: Artificial intelligence (e.g., GPT-4) that can generate customized health and exercise plans based on collected data.

[0982] Data analysis AI: Analyzes collected data and evaluates progress of health management menus and exercise plans (e.g., TensorFlow).

[0983] Database: A system (e.g. MySQL) that stores and manages basic and detailed worker information.

[0984] Program processing overview

[0985] The server stores basic information entered by the worker on their smartphone in a database, and uses conversational AI to collect detailed information, which is then passed to a generation AI to generate a customized health management menu and exercise plan. The plan is then returned to the server and sent to the worker's smartphone for display.

[0986] Examples:

[0987] Factory workers access a smartphone application and enter basic information such as name, age, gender, current weight, and target weight. The server stores this information in a database. Next, a conversational AI asks for more detailed information such as food preferences and whether or not the worker has any allergies, and the AI ​​then generates a customized health management menu and exercise plan based on this information.

[0988] Example prompt sentences to use:

[0989] User Profile:

[0990] Name: Factory Worker A

[0991] Age: 35

[0992] Gender: Male

[0993] Weight: 75kg

[0994] Target weight: 70kg

[0995] Favorite food: Japanese food

[0996] Allergies: None

[0997] Previous diet experience: First time

[0998] Based on the above information, generate a one-week meal menu and exercise plan for the user, focusing on Japanese cuisine.

[0999] Specific operation of the system

[1000] The server uses conversational AI to collect detailed information based on the worker's input data. This information is then supplemented to generate an individually customized health management menu and exercise plan. In addition, daily progress data (weight scale information and photos of meals eaten) is collected, and the data analysis AI analyzes this data to automatically adjust the health management menu and exercise plan. This enables workers to continuously manage their health.

[1001] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1002] Step 1:

[1003] A user accesses the system using a terminal and enters basic information (such as name, age, sex, current weight, target weight, and health condition). The server receives this basic information and stores it in a database.

[1004] Input: Basic information entered by the user into the device.

[1005] Specific operation: The user opens the smartphone application, enters their name, age, gender, current weight, target weight, etc. into the input form, and presses the submit button. The server stores this data in a database.

[1006] Output: Basic information data stored on the server.

[1007] Step 2:

[1008] The server uses conversational AI to collect detailed information from the user (such as food preferences, allergies, and past exercise history), which is then used to update the user profile.

[1009] Input: Basic information stored on the server.

[1010] How it works: The conversational AI asks the user a series of questions and receives their answers, such as "What's your favorite food?" or "Do you have any allergies?" The user answers through their device, and the answers are sent to the server.

[1011] Output: A user profile with updated details.

[1012] Step 3:

[1013] The server passes the updated user profile to the generation AI, which generates a customized health management menu and exercise plan. The generated plan is then returned to the server and sent to the user's device for display.

[1014] Input: The updated user profile.

[1015] How it works: The server passes the user profile to the generative AI model and generates a prompt. The generative AI model generates a customized menu and plan and returns the results to the server. The server sends the results to the user's device and displays them in the application.

[1016] Output: A customized health and exercise plan.

[1017] Step 4:

[1018] Users periodically enter their health progress data (for example, their weight measured on a scale or photos of the food they have eaten), and the server receives this data and stores it in a database.

[1019] Input: Health progress data entered by the user into the device.

[1020] How it works: Users use a smartphone application to input their weight and upload photos of their meals. The server receives this data and stores it in a database.

[1021] Output: Health progress data stored in a database.

[1022] Step 5:

[1023] The server uses data analysis AI to analyze the health progress data, which then evaluates the user's health status and progress towards their plan.

[1024] Input: Health progress data stored in a database.

[1025] Specific operation: The server passes health progress data to the data analysis AI, which evaluates weight fluctuations and dietary content. The analysis results are returned to the server.

[1026] Output: Analysis results on health status and plan progress.

[1027] Step 6:

[1028] Based on the analysis results, the server will adjust the health management menu and exercise plan as needed, and will also send encouraging messages to the user through conversational AI to keep them motivated.

[1029] Input: Analysis results on health status and plan progress.

[1030] Specific operation: Based on the analysis results, the server automatically modifies the menu and plan contents. For example, reduce the amount of salad at breakfast and increase the amount of fish at dinner. The modified plan is sent to the user's device. The conversational AI also sends encouraging messages to the user, such as "Great pace! Keep it up!"

[1031] Output: A modified health and exercise plan, along with encouraging messages.

[1032] 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.

[1033] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[1034] Overview of program processing

[1035] Collection of User Information

[1036] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[1037] Gathering more information with conversational AI and emotion engines

[1038] Next, the server uses conversational AI and an emotion engine to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. The emotion engine analyzes the user's emotions from their facial expressions and voice, taking their state into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[1039] Generate meal menus and exercise plans

[1040] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The plan is then returned to the server and sent to the user's device for display.

[1041] Progress data collection and analysis

[1042] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[1043] Modifying plans and managing motivation

[1044] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. In addition, the emotion engine recognizes the user's emotions and generates and sends encouraging messages according to their state through the conversational AI.

[1045] Specific examples

[1046] 1. Collection of User Information

[1047] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The device sends this information to the server, which then stores it in a database.

[1048] 2. Gathering detailed information

[1049] The server asks the user additional questions via the device using the conversational AI and emotion engine. These questions include "What is your favorite food?" and "Do you have any allergies?" When the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state. The server receives this information and updates the user profile.

[1050] 3. Generate meal menus and exercise plans

[1051] The server then passes the updated user profile to the AI ​​generator, which then generates a personalized meal plan and exercise plan, taking into account the emotional information obtained by the emotion engine. The plan is then sent back to the server and displayed on the user's device.

[1052] 4. Collecting and analyzing progress data

[1053] The user periodically weighs themselves and enters "65kg." They also take photos of the meals they ate that day and send them from their device to the server. The server then sends these to a classification AI, which analyzes their weight fluctuations and dietary details and sends the results back to the server.

[1054] 5. Revising your plan and managing your motivation

[1055] Based on the analysis results, the server will adjust the meal menu and exercise plan as appropriate. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The adjusted plan will then be sent back to the user's device. Based on the emotion engine, the conversational AI will also generate encouraging messages for the user, such as "You're doing a great job! Keep it up!"

[1056] In this way, the system of the present invention supports sustainable weight loss by providing a customized meal menu and exercise plan based on the user's specific needs and emotional state, and also motivates the user by recognizing their emotions and sending them encouraging messages accordingly.

[1057] The processing flow will be explained below.

[1058] Step 1:

[1059] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[1060] Step 2:

[1061] The terminal transmits the input information to the server.

[1062] Step 3:

[1063] The server stores the received information in a database.

[1064] Step 4:

[1065] The server uses conversational AI and emotion engines to display additional questions to the user through the device, such as "What is your favorite food?" and "Do you have any allergies?"

[1066] Step 5:

[1067] The emotion engine analyzes the user's facial expressions and voice to determine their emotions at that time.

[1068] Step 6:

[1069] The user answers additional questions using the terminal.

[1070] Step 7:

[1071] The terminal sends the user's answer to the server.

[1072] Step 8:

[1073] The server receives the user's response and the emotion data from the emotion engine and updates the user profile.

[1074] Step 9:

[1075] The server passes the updated user profile to the generation AI.

[1076] Step 10:

[1077] The generative AI generates a customized meal menu and exercise plan based on the user's preferences, information, and emotional data, and sends the results to the server.

[1078] Step 11:

[1079] The server transmits the generated meal menu and exercise plan to the user's terminal.

[1080] Step 12:

[1081] The device displays a meal menu and exercise plan to the user.

[1082] Step 13:

[1083] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[1084] Step 14:

[1085] The device sends the weight data and a photo of what you ate to the server.

[1086] Step 15:

[1087] The server sends the received data to the identification AI and requests it to analyze it.

[1088] Step 16:

[1089] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[1090] Step 17:

[1091] The server then modifies the meal menu and exercise plan based on the analysis results.

[1092] Step 18:

[1093] The server sends the modified plan to the user's terminal.

[1094] Step 19:

[1095] The terminal displays the modified plan to the user.

[1096] Step 20:

[1097] The emotion engine reanalyzes the user's emotional state and sends that information to the conversational AI.

[1098] Step 21:

[1099] The conversational AI generates encouraging messages for the user and sends them to the device via the server, which then displays the messages to the user, such as "Great pace, keep it up!"

[1100] The above is the specific processing flow of the system.

[1101] Example 2

[1102] Next, a description will be given of 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."

[1103] Conventional diet support systems tend to fall into a one-size-fits-all approach because they are unable to fully consider individual preferences and emotional states. As a result, users' motivation tends to decline and the success rate of long-term dieting is low. In addition, the collected data cannot be effectively utilized, making it difficult to provide individually customized plans.

[1104] 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.

[1105] In this invention, the server includes a means for a user to input basic information, transmit it, and store it in a database, a means for collecting detailed information using a conversational AI and an emotion engine and updating the user profile, a means for a generation AI to generate a meal menu and exercise plan based on the updated user profile and display it, a means for a user to transmit weight data and photos of meals, which a recognition AI analyzes, and a means for modifying the meal menu and exercise plan based on the analyzed data and generating messages to maintain motivation. This makes it possible to provide a customized diet plan that takes into account the user's preferences and emotional state, and maintain long-term motivation.

[1106] "Entering user information" refers to the act of a user accessing the Personal Diet AI Manager using a device and entering basic information such as name, age, gender, height, current weight, target weight, and desired diet period.

[1107] A "database" is an information system for storing and managing collected user information and progress data.

[1108] "Conversational AI" is an artificial intelligence that collects information through dialogue with users in natural language and provides appropriate answers and information.

[1109] The "emotion engine" is a system that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[1110] "Generative AI" is artificial intelligence that generates customized meal menus and exercise plans based on user profiles and emotional information.

[1111] "Discrimination AI" is an artificial intelligence that analyzes weight data and photos of meals sent by users to determine calories and nutritional value.

[1112] A "prompt sentence" is an input sentence that gives specific instructions to the generation AI.

[1113] A "user profile" is data that integrates a user's basic information, detailed information, and emotional information.

[1114] The "meal menu" refers to the meal contents and specific menu items that are recommended for the user to consume.

[1115] An "exercise plan" is a recommended exercise or workout plan for a user.

[1116] The "message to maintain motivation" is a message to support and encourage the user to continue their diet.

[1117] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[1118] Hardware and software used

[1119] The system uses the following major hardware and software:

[1120] Device: The device a user uses to enter information or view results (e.g., smartphone, tablet, computer).

[1121] Server: A central system for managing data and running various AI models.

[1122] Generative AI: Artificial intelligence that generates customized meal menus and exercise plans based on user profiles.

[1123] Conversational AI: Artificial intelligence that gathers information through natural dialogue with users and provides appropriate answers and information.

[1124] Emotion engine: A system that analyzes emotions from the user's facial expressions and voice and recognizes their state.

[1125] Recognition AI: Artificial intelligence that analyzes weight data and food photos submitted by users to determine calories and nutritional value.

[1126] System Operation Overview

[1127] Collection of User Information

[1128] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[1129] Gathering more information with conversational AI and emotion engines

[1130] The server uses conversational AI to ask detailed questions to the user, such as about their food preferences, allergies, and past dieting experiences. It also uses an emotion engine to analyze the user's emotional state from their facial expressions and voice. The server updates the user's profile based on their answers and emotional information.

[1131] Generate meal menus and exercise plans

[1132] The server then passes the updated user profile information to the AI ​​generator, which then generates a customized meal and exercise plan. The plan is then sent back to the server and displayed on the user's device.

[1133] Progress data collection and analysis

[1134] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to a classification AI that analyzes weight fluctuations and dietary content.

[1135] Modifying plans and managing motivation

[1136] The server updates the meal menu and exercise plan as needed based on the analysis results of the classification AI. The revised plan is then sent back to the user's device. Additionally, the conversational AI generates encouraging messages based on the emotion engine and sends them to the device.

[1137] Examples of concrete examples and prompts

[1138] Specific examples

[1139] The user enters basic information such as "Name, age 30, female, 160cm, 68kg, 55kg, 3 months old" into the terminal.

[1140] The server uses conversational AI to ask questions such as "What's your favorite food?" and "Do you have any allergies?", and the user answers "I like Japanese food."

[1141] The emotion engine analyzes positive emotions from the user's facial expressions and stores them in a database.

[1142] A profile such as "User information: Female, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" is sent to the generation AI, and a customized plan is generated.

[1143] Prompt Sentence Examples

[1144] "User information: Female, 30 years old, 160cm, 68kg, goal weight 55kg, 3 months. User preferences: Likes Japanese food, allergy to dairy products. Emotional state: Positive. Generate a customized meal menu and exercise plan based on this information."

[1145] In this way, the system of the present invention provides customized support based on the user's specific needs and emotional state, enabling them to achieve sustainable weight loss.

[1146] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1147] Step 1: Enter your user information

[1148] Specific behavior:

[1149] The user accesses the personal diet AI manager using a device.

[1150] The terminal prompts the user to enter basic information such as name, age, sex, height, current weight, target weight, and desired diet period.

[1151] Input: Name, age, gender, height, current weight, goal weight, desired diet duration.

[1152] Output: The user's basic information is sent from the device to the server and stored in a database.

[1153] Step 2: Gathering more information with conversational AI and emotion engines

[1154] Specific behavior:

[1155] The server uses conversational AI to generate detailed questions for the user, such as their food preferences, allergies, and past dieting experiences.

[1156] The terminal displays these questions to the user, who then enters the answers.

[1157] The emotion engine analyzes the user's facial expressions and voice in real time to extract emotional information.

[1158] Input: Question sent by the server, user's answer, emotion data from the emotion engine.

[1159] Output: The user's detailed answers and sentiment information are sent to the server and the user profile is updated.

[1160] Step 3: Create a meal plan and exercise plan

[1161] Specific behavior:

[1162] The server sends the updated user profile to the generation AI, providing data such as "User information: female, 30 years old, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" as a prompt.

[1163] Generative AI generates customized meal menus and exercise plans based on the data.

[1164] The generated plan is returned to the server, which then transmits it to the user's terminal.

[1165] Input: Updated user profile, prompt statement.

[1166] Output: Generate a customized meal menu and exercise plan and display it on the user's device.

[1167] Step 4: Collect and analyze progress data

[1168] Specific behavior:

[1169] The user periodically measures their weight and inputs the data into the device. They also take photos of the food they eat and send them to the server via the device.

[1170] The server sends the received data to a recognition AI, which analyzes the meal contents from the photo and determines the calories and nutritional value.

[1171] Input: User weight data, meal photos.

[1172] Output: The analysis results from the classification AI are sent to the server and stored in a database.

[1173] Step 5: Modify your plan and manage your motivation

[1174] Specific behavior:

[1175] The server will revise the meal menu and exercise plan as needed based on the analysis results of the identification AI.

[1176] The updated plan is passed from the server to the generation AI, which generates a new, revised plan.

[1177] Based on the emotion engine, the conversational AI generates encouraging messages for the user and sends them to the device.

[1178] Input: Analysis results of the discrimination AI, user progress data, and emotional information from the emotion engine.

[1179] Output: Generates and sends updated meal and exercise plans and encouraging messages to the device.

[1180] The above is the specific flow of the program processing of this system, which enables users to achieve a sustainable diet and receive individually customized support.

[1181] (Application example 2)

[1182] Next, a description will be given of Application 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."

[1183] Conventional diet systems lacked sufficient support for providing customized meal menus and exercise plans suited to individual users, particularly lacking advice and encouragement tailored to each individual's emotional state. Furthermore, there was no established method for providing personalized support using a virtual environment. As a result, it was difficult for users to maintain their motivation, making it difficult to achieve diet results.

[1184] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for conversing with an individual considering dieting; means for generating a meal menu and an exercise plan based on information collected through the conversation; means for acquiring and analyzing scale information and photos of food eaten; means for modifying the meal menu and exercise plan based on the analysis results; means for recognizing the individual's emotional state using an emotion engine and generating messages based on this; and means for providing customized support to the individual through a virtual environment. This allows for the provision of a meal menu and exercise plan tailored to the emotional state and preferences of each individual user in real time, enabling sustained diet support while maintaining high user motivation.

[1185] An "individual considering dieting" is an individual who is trying to improve their diet and exercise in order to lose weight and maintain good health.

[1186] "Means for conversation" refers to means for two-way communication with users, and uses technologies such as voice recognition and text chat.

[1187] "Collected Information" refers to individual information entered by the user, such as dietary preferences, allergy information, and past dieting experiences.

[1188] The "means for generating a meal menu and exercise plan" is a means for creating an appropriate meal and exercise plan based on the user's individual information.

[1189] "Weight scale information" refers to weight data measured periodically by the user.

[1190] "Photos of what you ate" refers to images taken by a user to record what they ate.

[1191] "Means of analysis" refers to the means for processing collected data and analyzing the results, and uses artificial intelligence and image recognition technology, etc.

[1192] The "means for modifying a meal menu or exercise plan" refers to a means for updating or changing an existing meal menu or exercise plan based on the analysis results.

[1193] An "emotion engine" refers to software or hardware for analyzing emotions from a user's facial expressions and voice.

[1194] The "means for generating a message" is a means for creating a message of encouragement or advice based on the analyzed emotional data.

[1195] A "virtual environment" is a technology that provides a virtual space that is different from the real world, and is realized using smartphones, head-mounted displays, etc.

[1196] "Customized support" refers to personalized advice and assistance tailored to each individual user.

[1197] As an embodiment of the present invention, a system is configured in which a server, a terminal, and a user each have their own role. The specific configuration and processing content of this system will be described below.

[1198] 1. Collection of User Information

[1199] Users access the diet support application using a device (smartphone, smart glasses, head-mounted display, etc.). First, the user enters basic information such as name, age, gender, height, current weight, target weight, and desired diet period. This information is sent from the device to the server and stored in the server's database.

[1200] 2. Gathering more information with conversational AI and emotion engines

[1201] The server uses conversational AI to ask the user detailed questions via their device, such as about their food preferences, allergies, and past dieting experiences. At this time, an emotion engine (such as Google Cloud Video Intelligence API) analyzes the user's emotions from their facial expressions and voice, and these emotions are also taken into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[1202] 3. Generate meal menus and exercise plans

[1203] The server then passes the updated user profile to the generative AI model, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The generated plan is then sent from the server to the device and displayed to the user.

[1204] 4. Collecting and analyzing progress data

[1205] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to the classification AI for analysis.

[1206] 5. Revising your plan and managing your motivation

[1207] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the device. The server also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which are then sent to the device.

[1208] As a concrete example, suppose a user enters basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." This information is stored on the server, and the conversational AI asks questions such as "What is your favorite food?" and "Do you have any allergies?" If the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to determine their emotional state. The server uses this information to update the user profile and generate an individually customized meal menu and exercise plan.

[1209] An example of an input prompt for a generative AI model is as follows:

[1210] "Generate a diet menu suitable for the user if the user is 30 years old and the emotion indicated by the user's facial expression is happy."

[1211] In this way, it is possible to provide a user with a customized meal menu and exercise plan that addresses their specific needs and emotional state.

[1212] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1213] Step 1:

[1214] A user accesses a diet support application using a device and inputs basic information (name, age, sex, height, current weight, target weight, desired diet period). This information is sent from the device to a server and stored in the server's database. The input data is the information provided by the user, and the output is the user's basic profile information stored on the server.

[1215] Step 2:

[1216] The server uses conversational AI to ask the user additional questions via the device (such as food preferences, allergies, and past dieting experiences). The user enters their answers, and the emotion engine analyzes their emotions from their facial expressions and voice. This additional information and emotion data is sent to the server, which updates the user profile. The input data is the user's additional information and emotion data, and the output is an updated user profile.

[1217] Step 3:

[1218] The server passes the updated user profile to the generative AI model, which generates a meal menu and exercise plan. Specifically, a prompt is input into the generative AI model, which generates a plan that takes into account the user's preferences, physical condition, and emotional state. The generated plan is returned to the server and sent to the device to be displayed to the user. The input data is the updated user profile, and the output is the generated meal menu and exercise plan.

[1219] Step 4:

[1220] Users measure their weight periodically and enter the data into the device. They also take photos of the food they eat and send them to a server via the device. The server then sends this data to a classification AI for analysis. The input data are the weight measurement results and photos of the food, and the output is the data analyzed by the classification AI.

[1221] Step 5:

[1222] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the discrimination AI. The updated plan is then sent back to the device. It also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which then sends them to the device. The input data are the analysis results and the user's emotional data, and the output is the revised meal menu, exercise plan, and encouraging messages.

[1223] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 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.

[1224] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1225] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1226] [Fourth embodiment]

[1227] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1228] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1229] 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).

[1230] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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 control target 443 are also connected to the bus 52.

[1231] 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.

[1232] 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).

[1233] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1234] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1235] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, 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.

[1236] 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.

[1237] 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.

[1238] In the robot 414, the processor 46 performs the reception output process. 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.

[1239] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1240] The system of the present invention provides a customized meal menu and exercise plan to an individual considering dieting, and monitors and adjusts the progress. The program process will be explained below with specific examples.

[1241] Overview of program processing

[1242] Collection of User Information

[1243] The user accesses the Personal Diet AI Manager through a terminal and enters basic information such as name, age, gender, height, current weight, target weight, desired diet period, etc. The server receives this information and stores it in a database.

[1244] Gathering more information with conversational AI

[1245] The server then uses conversational AI to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. Based on the user's answers, the server updates the user profile.

[1246] Generate meal menus and exercise plans

[1247] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences and physical condition. The generated plan is then returned to the server and sent to the user's device for display.

[1248] Progress data collection and analysis

[1249] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[1250] Modifying plans and managing motivation

[1251] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to keep the user motivated.

[1252] Specific examples

[1253] 1. Collection of User Information

[1254] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The server receives this information and stores it in a database.

[1255] 2. Gathering detailed information

[1256] The server sends additional questions to the user through the terminal. Examples of questions include "What is your favorite food?" and "Do you have any allergies?" The user answers "I like Japanese food" and "I'm allergic to dairy products." The server receives these answers and updates the user profile.

[1257] 3. Generate meal menus and exercise plans

[1258] The server passes the updated user profile to the generation AI, which then generates an individual meal menu and exercise plan. The generation AI creates a "Japanese food-centered menu" and a "30-minute jogging plan three times a week" and sends them back to the server. The server then sends the generated plan to the user's device, where it is displayed to the user.

[1259] 4. Collecting and analyzing progress data

[1260] The user weighs themselves one week later and enters "65kg." They also take and send a photo of the food they ate that day. The server receives this and sends the data to the AI ​​system for classification. The AI ​​system analyzes the data, evaluates weight fluctuations and dietary details, and sends the results back to the server.

[1261] 5. Revising your plan and managing your motivation

[1262] The server then modifies the meal menu and exercise plan based on the analysis results. For example, it might suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also sends encouraging messages to the user, such as "You're on a great pace! Keep it up!"

[1263] In this way, the system of the present invention provides a customized meal menu and exercise plan based on the user's specific needs, supporting sustainable weight loss.

[1264] The processing flow will be explained below.

[1265] Step 1:

[1266] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[1267] Step 2:

[1268] The terminal transmits the input information to the server.

[1269] Step 3:

[1270] The server stores the received information in a database.

[1271] Step 4:

[1272] The server then asks the user additional questions via the device using conversational AI, such as "What is your favorite food?" or "Do you have any allergies?"

[1273] Step 5:

[1274] The user answers additional questions using the terminal.

[1275] Step 6:

[1276] The terminal sends the user's answer to the server.

[1277] Step 7:

[1278] The server receives the user's response and updates the user profile.

[1279] Step 8:

[1280] The server passes the updated user profile to the generation AI.

[1281] Step 9:

[1282] The generation AI generates a customized meal menu and exercise plan based on the user's preferences and information, and sends the results to the server.

[1283] Step 10:

[1284] The server transmits the generated meal menu and exercise plan to the user's terminal.

[1285] Step 11:

[1286] The device displays a meal menu and exercise plan to the user.

[1287] Step 12:

[1288] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[1289] Step 13:

[1290] The device sends the weight data and a photo of what you ate to the server.

[1291] Step 14:

[1292] The server sends the received data to the identification AI and requests it to analyze it.

[1293] Step 15:

[1294] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[1295] Step 16:

[1296] The server then modifies the meal menu and exercise plan based on the analysis results.

[1297] Step 17:

[1298] The server sends the modified plan to the user's terminal.

[1299] Step 18:

[1300] The terminal displays the modified plan to the user.

[1301] Step 19:

[1302] The conversational AI generates encouraging messages for the user and sends them to the device via the server. The device displays the messages to the user and sends messages such as "Great pace, keep it up!" depending on the registered content.

[1303] The above is the specific processing flow of the system.

[1304] Example 1

[1305] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1306] In modern society, it is difficult to provide a customized diet plan that suits an individual's lifestyle and food preferences, and it is also difficult to properly monitor progress and maintain motivation.

[1307] 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.

[1308] In this invention, the server includes a means for communicating with the individual via the communication terminal, a means for saving the individual's basic information in a database, a means for sending prompts to the generation AI and generating a meal menu and exercise plan customized for the individual, a means for acquiring weight information and meal photos periodically entered by the individual, a means for analyzing the acquired information and modifying the meal menu and exercise plan, and a means for sending encouraging messages to the individual. This makes it possible to provide an appropriate diet plan based on the individual's lifestyle and preferences and to continuously manage motivation based on the plan.

[1309] "Individual" refers to a user who uses this system to diet.

[1310] A "communications terminal" is a device used by an individual to exchange information with a server.

[1311] A "means of conversation" is a tool or algorithm that allows the server to engage in two-way communication with an individual.

[1312] "Basic information" refers to information such as name, age, sex, height, and weight that an individual provides to the server.

[1313] A "database" is a system in which a server stores basic information about individuals and other data.

[1314] "Generative AI" is artificial intelligence that creates meal menus and exercise plans based on personalized prompts.

[1315] A "prompt" is a document containing personalized requests and information for each individual that the server passes to the generating AI.

[1316] A "meal menu" is a plan created by the generative AI that includes food choices and meal schedules appropriate for an individual.

[1317] An "exercise plan" is a plan created by the generating AI that includes exercise content and schedules that are suitable for each individual.

[1318] "Weight information" refers to weight data that an individual measures periodically and inputs into the server.

[1319] A "meal photo" is an image that an individual takes and sends to a server to record the food they have eaten.

[1320] "Means of analysis" refers to algorithms or tools that analyze the data received by the server and extract the necessary information.

[1321] "Encouraging messages" are words of encouragement or consolation sent by the server to keep individuals motivated.

[1322] This invention is a system that provides a customized meal menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. This system is realized by integrating multiple hardware and software components, including a server, terminals, generation AI, classification AI, and database.

[1323] Collection of User Information

[1324] The user accesses the Personal Diet AI Manager through a device (smartphone or computer). The device screen displays an input screen for information such as name, age, gender, height, current weight, target weight, and desired diet period. The user enters this basic information, which is then received by the server and stored in a database. For example, the user might enter "Yamada Hanako, age 30, female, 160cm, 68kg, target weight 55kg, duration 3 months."

[1325] Gathering more information with conversational AI

[1326] The server then launches a conversational AI that prompts the user with detailed questions on their device, gathering additional information such as their food preferences, allergies, and past dieting experiences. When the user enters answers such as "I like Japanese food" or "I'm allergic to dairy products," the user profile is updated accordingly.

[1327] Generate meal menus and exercise plans

[1328] The updated user profile is sent to the generation AI as a prompt. For example, a prompt like "I'm a 30-year-old woman, my current weight is 68 kg, and my diet goal is 55 kg. I particularly like Japanese food, but I'm allergic to dairy products. Please suggest a customized meal menu and exercise plan that suits this user." Based on this, the generation AI generates a personalized meal menu and exercise plan, which are then displayed on the user's device via the server.

[1329] Progress data collection and analysis

[1330] Users periodically measure their weight and enter that data into their device. They also take photos of the food they eat and send them to the server from their device. For example, if a user enters their weight as "65 kg" one week later and sends photos of the food they have eaten, the server will send this data to the AI ​​recognition system and analyze the results.

[1331] Modifying plans and managing motivation

[1332] Based on the data analysis results returned by the classification AI, the server modifies the user's meal menu and exercise plan as needed. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The modified plan is then sent back to the user's device. The conversational AI also periodically sends encouraging messages to the user, such as, "You're on a great pace! Keep it up!"

[1333] In this way, the system of the present invention provides a continuously customized meal menu and exercise plan based on an individual's specific needs, helping them achieve a sustainable diet.

[1334] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1335] Step 1: Collect user information

[1336] The user accesses the Personal Diet AI Manager through a terminal. The terminal screen displays an input form for information such as name, age, gender, height, current weight, target weight, and desired diet period. The basic information entered is sent to the server and stored in a database.

[1337] Input: Name, age, sex, height, weight, target weight, diet period

[1338] Output: Saved user basic information data

[1339] Specific operation: For example, the user enters "Yamada Hanako, 30 years old, female, 160cm, 68kg, goal weight 55kg, period 3 months." The server receives this information and records it in the database.

[1340] Step 2: Gathering more information with conversational AI

[1341] The server launches the conversational AI and displays detailed questions to the user. The user then inputs answers to the displayed questions via their device. Specific questions include food preferences, allergies, and past dieting experiences.

[1342] Input: User's answer regarding detailed information (e.g., I like Japanese food, I'm allergic to dairy products)

[1343] Output: Updated user profile

[1344] Specific operation: The server displays the question "What is your favorite food?" on the terminal, and the user enters "I like Japanese food." Similarly, the user answers "I'm allergic to dairy products" to the question "Do you have any allergies?" This information is sent to the server, and the user profile is updated.

[1345] Step 3: Create a meal plan and exercise plan

[1346] The server sends the updated user profile as a prompt to the AI ​​generator, which then generates a customized meal and exercise plan for the user based on the prompt. The plan is then sent to the server and displayed on the user's device.

[1347] Input: Updated user profile (prompt text)

[1348] Output: Customized meal and exercise plan

[1349] Specific operation: The server sends the generation AI a prompt message: "A 30-year-old woman, currently weighing 68 kg, with a diet goal of 55 kg. She particularly likes Japanese food and is allergic to dairy products. Please suggest a customized meal menu and exercise plan for this user." The generation AI generates a plan such as "Breakfast: Japanese salad, Lunch: Japanese-style rice balls, Dinner: Boiled fish, Exercise: 30 minutes of jogging three times a week" and sends it back to the server. The server then sends the results to the user's device and displays them.

[1350] Step 4: Collect and analyze progress data

[1351] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them from the device to the server. A classification AI analyzes this data and evaluates weight fluctuations and dietary content. The analysis results are sent back to the server, and the user profile is updated.

[1352] Input: Regularly entered weight data and photos of meals eaten

[1353] Output: Analyzed weight fluctuation data, dietary assessment data

[1354] Specific operation: The user weighs themselves one week later, enters "65kg," takes a photo of the food they ate that day, and sends it. The server receives this and sends it to the classification AI. The classification AI analyzes it and sends the evaluation results back to the server.

[1355] Step 5: Modify your plan and manage your motivation

[1356] The server then modifies the user's meal menu and exercise plan as needed based on the analysis results. The modified plan is then sent back to the user's device. The server also uses conversational AI to periodically send encouraging messages to keep the user motivated.

[1357] Input: Analyzed weight fluctuation data, dietary assessment data

[1358] Output: Modified meal and exercise plan, encouraging messages

[1359] Specific operation: The server makes suggestions for corrections, such as "Reduce the amount of salad you have for breakfast and increase the amount of boiled fish you have for dinner," and sends these to the user's device. The conversational AI also sends encouraging messages, such as "You're doing great! Keep it up!"

[1360] (Application example 1)

[1361] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1362] Conventional health management systems for factory workers lack sufficient customization based on individual health conditions and preferences, and lack real-time analysis of progress data or features to encourage workers to maintain motivation. As a result, workers often stop taking care of their health. There is a need to solve this problem and provide a system that allows workers to take continuous health management.

[1363] 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.

[1364] In this invention, the server includes a means for collecting basic information about the person under health management, a means for using conversational AI to collect detailed information about the person under health management, and a means for generating a customized health management menu and exercise plan based on the collected information using generation AI. This allows for customization according to individual health conditions and preferences, making it possible to provide a system that allows workers to continuously manage their health.

[1365] A "health management subject" is an individual whose health condition is managed by the system.

[1366] "Basic information" refers to initial data about an individual, such as name, age, sex, weight, target weight, and health status.

[1367] "Conversational AI" is an artificial intelligence system that uses natural language processing to converse with users and collect and provide information.

[1368] "Detailed information" refers to specific personal data collected in addition to basic information such as dietary preferences, allergies, and past exercise experience.

[1369] "Generative AI" is artificial intelligence that has the ability to generate customized health management menus and exercise plans based on individual user information.

[1370] A "health management menu" is a dietary and lifestyle suggestion designed to improve a subject's health.

[1371] An "exercise plan" is an exercise regime designed to improve a subject's fitness or health.

[1372] "Weight scale information" refers to data obtained by a person who is subject to health management periodically measuring their weight and entering the results into the system.

[1373] "Photos of meals consumed" are image data taken to record the meals eaten by the subject.

[1374] "Analysis" refers to the process in which the system evaluates and analyzes the subject's health condition and progress of the plan based on the collected data.

[1375] "Encouraging messages" are positive messages that the system periodically sends to keep the subject motivated.

[1376] The system of this invention was developed to support the health management of factory workers. The system consists of the following main components:

[1377] 1. Hardware:

[1378] Smartphones: A user interface used daily by factory workers, they are responsible for inputting and outputting data.

[1379] Server: Provides a central database and data processing.

[1380] 2. Software:

[1381] Conversational AI: An artificial intelligence system that uses natural language processing (e.g., ChatGPT) to collect detailed information necessary for health management through conversations with workers.

[1382] Generative AI: Artificial intelligence (e.g., GPT-4) that can generate customized health and exercise plans based on collected data.

[1383] Data analysis AI: Analyzes collected data and evaluates progress of health management menus and exercise plans (e.g., TensorFlow).

[1384] Database: A system (e.g. MySQL) that stores and manages basic and detailed worker information.

[1385] Program processing overview

[1386] The server stores basic information entered by the worker on their smartphone in a database, and uses conversational AI to collect detailed information, which is then passed to a generation AI to generate a customized health management menu and exercise plan. The plan is then returned to the server and sent to the worker's smartphone for display.

[1387] Examples:

[1388] Factory workers access a smartphone application and enter basic information such as name, age, gender, current weight, and target weight. The server stores this information in a database. Next, a conversational AI asks for more detailed information such as food preferences and whether or not the worker has any allergies, and the AI ​​then generates a customized health management menu and exercise plan based on this information.

[1389] Example prompt sentences to use:

[1390] User Profile:

[1391] Name: Factory Worker A

[1392] Age: 35

[1393] Gender: Male

[1394] Weight: 75kg

[1395] Target weight: 70kg

[1396] Favorite food: Japanese food

[1397] Allergies: None

[1398] Previous diet experience: First time

[1399] Based on the above information, generate a one-week meal menu and exercise plan for the user, focusing on Japanese cuisine.

[1400] Specific operation of the system

[1401] The server uses conversational AI to collect detailed information based on the worker's input data. This information is then supplemented to generate an individually customized health management menu and exercise plan. In addition, daily progress data (weight scale information and photos of meals eaten) is collected, and the data analysis AI analyzes this data to automatically adjust the health management menu and exercise plan. This enables workers to continuously manage their health.

[1402] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1403] Step 1:

[1404] A user accesses the system using a terminal and enters basic information (such as name, age, sex, current weight, target weight, and health condition). The server receives this basic information and stores it in a database.

[1405] Input: Basic information entered by the user into the device.

[1406] Specific operation: The user opens the smartphone application, enters their name, age, gender, current weight, target weight, etc. into the input form, and presses the submit button. The server stores this data in a database.

[1407] Output: Basic information data stored on the server.

[1408] Step 2:

[1409] The server uses conversational AI to collect detailed information from the user (such as food preferences, allergies, and past exercise history), which is then used to update the user profile.

[1410] Input: Basic information stored on the server.

[1411] How it works: The conversational AI asks the user a series of questions and receives their answers, such as "What's your favorite food?" or "Do you have any allergies?" The user answers through their device, and the answers are sent to the server.

[1412] Output: A user profile with updated details.

[1413] Step 3:

[1414] The server then passes the updated user profile to the AI ​​generator, which generates a customized health management menu and exercise plan. The plan is then returned to the server and sent to the user's device for display.

[1415] Input: The updated user profile.

[1416] How it works: The server passes the user profile to the generative AI model and generates a prompt. The generative AI model generates a customized menu and plan and returns the results to the server. The server sends the results to the user's device and displays them in the application.

[1417] Output: A customized health and exercise plan.

[1418] Step 4:

[1419] Users periodically enter their health progress data (for example, their weight measured on a scale or photos of the food they have eaten), and the server receives this data and stores it in a database.

[1420] Input: Health progress data entered by the user into the device.

[1421] How it works: Users use a smartphone application to input their weight and upload photos of their meals. The server receives this data and stores it in a database.

[1422] Output: Health progress data stored in a database.

[1423] Step 5:

[1424] The server uses data analysis AI to analyze the health progress data, which then evaluates the user's health status and progress towards their plan.

[1425] Input: Health progress data stored in a database.

[1426] Specific operation: The server passes health progress data to the data analysis AI, which evaluates weight fluctuations and dietary content. The analysis results are returned to the server.

[1427] Output: Analysis results on health status and plan progress.

[1428] Step 6:

[1429] Based on the analysis results, the server will adjust the health management menu and exercise plan as needed, and will also send encouraging messages to the user through conversational AI to keep them motivated.

[1430] Input: Analysis results on health status and plan progress.

[1431] Specific operation: Based on the analysis results, the server automatically modifies the menu and plan contents. For example, reduce the amount of salad at breakfast and increase the amount of fish at dinner. The modified plan is sent to the user's device. The conversational AI also sends encouraging messages to the user, such as "Great pace! Keep it up!"

[1432] Output: A modified health and exercise plan, along with encouraging messages.

[1433] 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.

[1434] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[1435] Overview of program processing

[1436] Collection of User Information

[1437] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[1438] Gathering more information with conversational AI and emotion engines

[1439] Next, the server uses conversational AI and an emotion engine to ask the user detailed questions about their food preferences, allergies, past dieting experiences, etc. The emotion engine analyzes the user's emotions from their facial expressions and voice, taking their state into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[1440] Generate meal menus and exercise plans

[1441] The server then passes the updated user profile to the AI ​​generator, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The plan is then returned to the server and sent to the user's device for display.

[1442] Progress data collection and analysis

[1443] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them via the device. This data is sent to the server and analyzed by the AI ​​recognition system.

[1444] Modifying plans and managing motivation

[1445] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the user's device. In addition, the emotion engine recognizes the user's emotions and generates and sends encouraging messages according to their state through the conversational AI.

[1446] Specific examples

[1447] 1. Collection of User Information

[1448] The user accesses the Personal Diet AI Manager using a device. On the screen that appears, they enter basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." The device sends this information to the server, which then stores it in a database.

[1449] 2. Gathering detailed information

[1450] The server asks the user additional questions via the device using the conversational AI and emotion engine. These questions include "What is your favorite food?" and "Do you have any allergies?" When the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state. The server receives this information and updates the user profile.

[1451] 3. Generate meal menus and exercise plans

[1452] The server then passes the updated user profile to the AI ​​generator, which then generates a personalized meal plan and exercise plan, taking into account the emotional information obtained by the emotion engine. The plan is then sent back to the server and displayed on the user's device.

[1453] 4. Collecting and analyzing progress data

[1454] The user periodically weighs themselves and enters "65kg." They also take photos of the meals they ate that day and send them from their device to the server. The server then sends these to a classification AI, which analyzes their weight fluctuations and dietary details and sends the results back to the server.

[1455] 5. Revising your plan and managing your motivation

[1456] Based on the analysis results, the server will adjust the meal menu and exercise plan as appropriate. For example, it may suggest reducing the amount of salad at breakfast and increasing the amount of boiled fish at dinner. The adjusted plan will then be sent back to the user's device. Based on the emotion engine, the conversational AI will also generate encouraging messages for the user, such as "You're doing a great job! Keep it up!"

[1457] In this way, the system of the present invention supports sustainable weight loss by providing a customized meal menu and exercise plan based on the user's specific needs and emotional state, and also motivates the user by recognizing their emotions and sending them encouraging messages accordingly.

[1458] The processing flow will be explained below.

[1459] Step 1:

[1460] The user accesses the Personal Diet AI Manager using a terminal and enters basic information (name, age, gender, height, current weight, target weight, and desired diet period).

[1461] Step 2:

[1462] The terminal transmits the input information to the server.

[1463] Step 3:

[1464] The server stores the received information in a database.

[1465] Step 4:

[1466] The server uses conversational AI and emotion engines to display additional questions to the user through the device, such as "What is your favorite food?" and "Do you have any allergies?"

[1467] Step 5:

[1468] The emotion engine analyzes the user's facial expressions and voice to determine their emotions at that time.

[1469] Step 6:

[1470] The user answers additional questions using the terminal.

[1471] Step 7:

[1472] The terminal sends the user's answer to the server.

[1473] Step 8:

[1474] The server receives the user's response and the emotion data from the emotion engine and updates the user profile.

[1475] Step 9:

[1476] The server passes the updated user profile to the generation AI.

[1477] Step 10:

[1478] The generative AI generates a customized meal menu and exercise plan based on the user's preferences, information, and emotional data, and sends the results to the server.

[1479] Step 11:

[1480] The server transmits the generated meal menu and exercise plan to the user's terminal.

[1481] Step 12:

[1482] The device displays a meal menu and exercise plan to the user.

[1483] Step 13:

[1484] The user measures their weight (e.g., "65 kg") and enters it into the device. They also take a photo of the food they have eaten and send it through the device.

[1485] Step 14:

[1486] The device sends the weight data and a photo of what you ate to the server.

[1487] Step 15:

[1488] The server sends the received data to the identification AI and requests it to analyze it.

[1489] Step 16:

[1490] The identification AI analyzes weight data and photos and sends calorie and nutrient information back to the server.

[1491] Step 17:

[1492] The server then modifies the meal menu and exercise plan based on the analysis results.

[1493] Step 18:

[1494] The server sends the modified plan to the user's terminal.

[1495] Step 19:

[1496] The terminal displays the modified plan to the user.

[1497] Step 20:

[1498] The emotion engine reanalyzes the user's emotional state and sends that information to the conversational AI.

[1499] Step 21:

[1500] The conversational AI generates encouraging messages for the user and sends them to the device via the server, which then displays the messages to the user, such as "Great pace, keep it up!"

[1501] The above is the specific processing flow of the system.

[1502] Example 2

[1503] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1504] Conventional diet support systems tend to fall into a one-size-fits-all approach because they are unable to fully consider individual preferences and emotional states. As a result, users' motivation tends to decline and the success rate of long-term dieting is low. In addition, the collected data cannot be effectively utilized, making it difficult to provide individually customized plans.

[1505] 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.

[1506] In this invention, the server includes a means for a user to input basic information, transmit it, and store it in a database, a means for collecting detailed information using a conversational AI and an emotion engine and updating the user profile, a means for a generation AI to generate a meal menu and exercise plan based on the updated user profile and display it, a means for a user to transmit weight data and photos of meals, which a recognition AI analyzes, and a means for modifying the meal menu and exercise plan based on the analyzed data and generating messages to maintain motivation. This makes it possible to provide a customized diet plan that takes into account the user's preferences and emotional state, and maintain long-term motivation.

[1507] "Entering user information" refers to the act of a user accessing the Personal Diet AI Manager using a device and entering basic information such as name, age, gender, height, current weight, target weight, and desired diet period.

[1508] A "database" is an information system for storing and managing collected user information and progress data.

[1509] "Conversational AI" is an artificial intelligence that collects information through dialogue with users in natural language and provides appropriate answers and information.

[1510] The "emotion engine" is a system that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[1511] "Generative AI" is artificial intelligence that generates customized meal menus and exercise plans based on user profiles and emotional information.

[1512] "Discrimination AI" is an artificial intelligence that analyzes weight data and photos of meals sent by users to determine calories and nutritional value.

[1513] A "prompt sentence" is an input sentence that gives specific instructions to the generation AI.

[1514] A "user profile" is data that integrates a user's basic information, detailed information, and emotional information.

[1515] The "meal menu" refers to the meal contents and specific menu items that are recommended for the user to consume.

[1516] An "exercise plan" is a recommended exercise or workout plan for a user.

[1517] The "message to maintain motivation" is a message to support and encourage the user to continue their diet.

[1518] The system of the present invention provides a customized diet menu and exercise plan to individuals considering dieting, and monitors and adjusts their progress. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to provide more personalized support.

[1519] Hardware and software used

[1520] The system uses the following major hardware and software:

[1521] Device: The device a user uses to enter information or view results (e.g., smartphone, tablet, computer).

[1522] Server: A central system for managing data and running various AI models.

[1523] Generative AI: Artificial intelligence that generates customized meal menus and exercise plans based on user profiles.

[1524] Conversational AI: Artificial intelligence that gathers information through natural dialogue with users and provides appropriate answers and information.

[1525] Emotion engine: A system that analyzes emotions from the user's facial expressions and voice and recognizes their state.

[1526] Recognition AI: Artificial intelligence that analyzes weight data and food photos submitted by users to determine calories and nutritional value.

[1527] System Operation Overview

[1528] Collection of User Information

[1529] The user accesses the Personal Diet AI Manager using a device and enters basic information (name, age, gender, height, current weight, target weight, desired diet period). The device sends this information to the server, which then stores the received information in a database.

[1530] Gathering more information with conversational AI and emotion engines

[1531] The server uses conversational AI to ask detailed questions to the user, such as about their food preferences, allergies, and past dieting experiences. It also uses an emotion engine to analyze the user's emotional state from their facial expressions and voice. The server updates the user's profile based on their answers and emotional information.

[1532] Generate meal menus and exercise plans

[1533] The server then passes the updated user profile information to the AI ​​generator, which then generates a customized meal and exercise plan. The plan is then sent back to the server and displayed on the user's device.

[1534] Progress data collection and analysis

[1535] Users periodically measure their weight and enter the data into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to a classification AI that analyzes weight fluctuations and dietary content.

[1536] Modifying plans and managing motivation

[1537] The server updates the meal menu and exercise plan as needed based on the analysis results of the classification AI. The revised plan is then sent back to the user's device. Additionally, the conversational AI generates encouraging messages based on the emotion engine and sends them to the device.

[1538] Examples of concrete examples and prompts

[1539] Specific examples

[1540] The user enters basic information such as "Name, age 30, female, 160cm, 68kg, 55kg, 3 months old" into the terminal.

[1541] The server uses conversational AI to ask questions such as "What's your favorite food?" and "Do you have any allergies?", and the user answers "I like Japanese food."

[1542] The emotion engine analyzes positive emotions from the user's facial expressions and stores them in a database.

[1543] A profile such as "User information: Female, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" is sent to the generation AI, and a customized plan is generated.

[1544] Prompt Sentence Examples

[1545] "User information: Female, 30 years old, 160cm, 68kg, goal weight 55kg, 3 months. User preferences: Likes Japanese food, allergy to dairy products. Emotional state: Positive. Generate a customized meal menu and exercise plan based on this information."

[1546] In this way, the system of the present invention provides customized support based on the user's specific needs and emotional state, enabling them to achieve sustainable weight loss.

[1547] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1548] Step 1: Enter your user information

[1549] Specific behavior:

[1550] The user accesses the personal diet AI manager using a device.

[1551] The terminal prompts the user to enter basic information such as name, age, sex, height, current weight, target weight, and desired diet period.

[1552] Input: Name, age, gender, height, current weight, goal weight, desired diet duration.

[1553] Output: The user's basic information is sent from the device to the server and stored in a database.

[1554] Step 2: Gathering more information with conversational AI and emotion engines

[1555] Specific behavior:

[1556] The server uses conversational AI to generate detailed questions for the user, such as their food preferences, allergies, and past dieting experiences.

[1557] The terminal displays these questions to the user, who then enters the answers.

[1558] The emotion engine analyzes the user's facial expressions and voice in real time to extract emotional information.

[1559] Input: Question sent by the server, user's answer, emotion data from the emotion engine.

[1560] Output: The user's detailed answers and sentiment information are sent to the server and the user profile is updated.

[1561] Step 3: Create a meal plan and exercise plan

[1562] Specific behavior:

[1563] The server sends the updated user profile to the generation AI, providing data such as "User information: female, 30 years old, 160cm, 68kg, goal weight 55kg, likes Japanese food, allergy to dairy products" as a prompt.

[1564] Generative AI generates customized meal menus and exercise plans based on the data.

[1565] The generated plan is returned to the server, which then transmits it to the user's terminal.

[1566] Input: Updated user profile, prompt statement.

[1567] Output: Generate a customized meal menu and exercise plan and display it on the user's device.

[1568] Step 4: Collect and analyze progress data

[1569] Specific behavior:

[1570] The user periodically measures their weight and inputs the data into the device. They also take photos of the food they eat and send them to the server via the device.

[1571] The server sends the received data to a recognition AI, which analyzes the meal contents from the photo and determines the calories and nutritional value.

[1572] Input: User weight data, meal photos.

[1573] Output: The analysis results from the classification AI are sent to the server and stored in a database.

[1574] Step 5: Modify your plan and manage your motivation

[1575] Specific behavior:

[1576] The server will revise the meal menu and exercise plan as needed based on the analysis results of the identification AI.

[1577] The updated plan is passed from the server to the generation AI, which generates a new, revised plan.

[1578] Based on the emotion engine, the conversational AI generates encouraging messages for the user and sends them to the device.

[1579] Input: Analysis results of the discrimination AI, user progress data, and emotional information from the emotion engine.

[1580] Output: Generates and sends updated meal and exercise plans and encouraging messages to the device.

[1581] The above is the specific flow of the program processing of this system, which enables users to achieve a sustainable diet and receive individually customized support.

[1582] (Application example 2)

[1583] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1584] Conventional diet systems lacked sufficient support for providing customized meal menus and exercise plans suited to individual users, particularly lacking advice and encouragement tailored to each individual's emotional state. Furthermore, there was no established method for providing personalized support using a virtual environment. As a result, it was difficult for users to maintain their motivation, making it difficult to achieve diet results.

[1585] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for conversing with an individual considering dieting; means for generating a meal menu and an exercise plan based on information collected through the conversation; means for acquiring and analyzing scale information and photos of food eaten; means for modifying the meal menu and exercise plan based on the analysis results; means for recognizing the individual's emotional state using an emotion engine and generating messages based on this; and means for providing customized support to the individual through a virtual environment. This allows for the provision of a meal menu and exercise plan tailored to the emotional state and preferences of each individual user in real time, enabling sustained diet support while maintaining high user motivation.

[1586] An "individual considering dieting" is an individual who is trying to improve their diet and exercise in order to lose weight and maintain good health.

[1587] "Means for conversation" refers to means for two-way communication with users, and uses technologies such as voice recognition and text chat.

[1588] "Collected Information" refers to individual information entered by the user, such as dietary preferences, allergy information, and past dieting experiences.

[1589] The "means for generating a meal menu and exercise plan" is a means for creating an appropriate meal and exercise plan based on the user's individual information.

[1590] "Weight scale information" refers to weight data measured periodically by the user.

[1591] "Photos of what you ate" refers to images taken by a user to record what they ate.

[1592] "Means of analysis" refers to the means for processing collected data and analyzing the results, and uses artificial intelligence and image recognition technology, etc.

[1593] The "means for modifying a meal menu or exercise plan" refers to a means for updating or changing an existing meal menu or exercise plan based on the analysis results.

[1594] An "emotion engine" refers to software or hardware for analyzing emotions from a user's facial expressions and voice.

[1595] The "means for generating a message" is a means for creating a message of encouragement or advice based on the analyzed emotional data.

[1596] A "virtual environment" is a technology that provides a virtual space that is different from the real world, and is realized using smartphones, head-mounted displays, etc.

[1597] "Customized support" refers to personalized advice and assistance tailored to each individual user.

[1598] As an embodiment of the present invention, a system is configured in which a server, a terminal, and a user each have their own role. The specific configuration and processing content of this system will be described below.

[1599] 1. Collection of User Information

[1600] Users access the diet support application using a device (smartphone, smart glasses, head-mounted display, etc.). First, the user enters basic information such as name, age, gender, height, current weight, target weight, and desired diet period. This information is sent from the device to the server and stored in the server's database.

[1601] 2. Gathering more information with conversational AI and emotion engines

[1602] The server uses conversational AI to ask the user detailed questions via their device, such as about their food preferences, allergies, and past dieting experiences. At this time, an emotion engine (such as Google Cloud Video Intelligence API) analyzes the user's emotions from their facial expressions and voice, and these emotions are also taken into consideration. Based on the user's answers and emotional information, the server updates the user's profile.

[1603] 3. Generate meal menus and exercise plans

[1604] The server then passes the updated user profile to the generative AI model, which then generates a meal menu and exercise plan tailored to the user's preferences, physical condition, and emotional state. The generated plan is then sent from the server to the device and displayed to the user.

[1605] 4. Collecting and analyzing progress data

[1606] Users periodically measure their weight and input the data from the scale into the device. They also take photos of the food they eat and send them to the server via the device. The server then sends this data to the classification AI for analysis.

[1607] 5. Revising your plan and managing your motivation

[1608] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the classification AI. The updated plan is then sent back to the device. The server also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which are then sent to the device.

[1609] As a concrete example, suppose a user enters basic information such as "Yamada Hanako, 30 years old, female, 160cm, 68kg, 55kg, 3 months old." This information is stored on the server, and the conversational AI asks questions such as "What is your favorite food?" and "Do you have any allergies?" If the user answers "I like Japanese food" or "I'm allergic to dairy products," the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to determine their emotional state. The server uses this information to update the user profile and generate an individually customized meal menu and exercise plan.

[1610] An example of an input prompt for a generative AI model is as follows:

[1611] "Generate a diet menu suitable for the user if the user is 30 years old and the emotion indicated by the user's facial expression is happy."

[1612] In this way, it is possible to provide a user with a customized meal menu and exercise plan that addresses their specific needs and emotional state.

[1613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1614] Step 1:

[1615] A user accesses a diet support application using a device and inputs basic information (name, age, sex, height, current weight, target weight, desired diet period). This information is sent from the device to a server and stored in the server's database. The input data is the information provided by the user, and the output is the user's basic profile information stored on the server.

[1616] Step 2:

[1617] The server uses conversational AI to ask the user additional questions via the device (such as food preferences, allergies, and past dieting experiences). The user enters their answers, and the emotion engine analyzes their emotions from their facial expressions and voice. This additional information and emotion data is sent to the server, which updates the user profile. The input data is the user's additional information and emotion data, and the output is an updated user profile.

[1618] Step 3:

[1619] The server passes the updated user profile to the generative AI model, which generates a meal menu and exercise plan. Specifically, a prompt is input into the generative AI model, which generates a plan that takes into account the user's preferences, physical condition, and emotional state. The generated plan is returned to the server and sent to the device to be displayed to the user. The input data is the updated user profile, and the output is the generated meal menu and exercise plan.

[1620] Step 4:

[1621] Users measure their weight periodically and enter the data into the device. They also take photos of the food they eat and send them to a server via the device. The server then sends this data to a classification AI for analysis. The input data are the weight measurement results and photos of the food, and the output is the data analyzed by the classification AI.

[1622] Step 5:

[1623] The server modifies the user's meal menu and exercise plan as needed based on the data analysis results returned by the discrimination AI. The updated plan is then sent back to the device. It also uses an emotion engine to recognize the user's emotions, and generates encouraging messages based on that state through conversational AI, which then sends them to the device. The input data are the analysis results and the user's emotional data, and the output is the revised meal menu, exercise plan, and encouraging messages.

[1624] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1625] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1626] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1627] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1628] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1629] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1630] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1631] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1632] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1633] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1634] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1635] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1636] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1637] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1638] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1639] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1640] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1641] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1642] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1643] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1644] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1645] The following is further disclosed regarding the above embodiment.

[1646] (Claim 1)

[1647] A means of communicating with individuals considering dieting,

[1648] means for generating a meal menu and an exercise plan based on the information collected from the conversation;

[1649] A means to obtain and analyze weight scale information and photos of what you eat,

[1650] A means for modifying a meal menu or exercise plan based on the analysis results;

[1651] A system including:

[1652] (Claim 2)

[1653] 2. The system according to claim 1, wherein the means for generating the meal menu and exercise plan takes into consideration personal preferences and experience.

[1654] (Claim 3)

[1655] The system according to claim 1, further comprising means for analyzing the data obtained from the acquired weight scale information and photos of food eaten, and sending encouraging messages to maintain motivation based on the results.

[1656] "Example 1"

[1657] (Claim 1)

[1658] a means for communicating with an individual via a communication device;

[1659] means for storing basic information of individuals collected through said conversations in a database;

[1660] A means for sending prompts to the generating AI to generate a personalized meal menu and exercise plan;

[1661] A means of obtaining weight information and meal photos that individuals regularly enter;

[1662] A means to analyze the acquired information and adjust meal menus and exercise plans,

[1663] means for sending an encouraging message to the individual based on the analysis result;

[1664] A system including:

[1665] (Claim 2)

[1666] The system according to claim 1, characterized in that the means for generating meal menus and exercise plans by the generation AI takes into consideration personal preferences and physical condition.

[1667] (Claim 3)

[1668] The system according to claim 1, further comprising means for analyzing data obtained from the acquired weight information and meal photos, and sending encouraging messages to maintain motivation based on the results.

[1669] "Application Example 1"

[1670] (Claim 1)

[1671] A means of communicating with individuals considering dieting,

[1672] means for generating a meal menu and an exercise plan based on the information collected from the conversation;

[1673] a means for collecting basic information about the subjects of health management;

[1674] A means of using conversational AI to gather detailed information about health care recipients; and

[1675] A means for generating a customized health management menu and exercise plan based on the collected information using generative AI;

[1676] A means of acquiring and analyzing weight scale information and photos of food intake,

[1677] A means for modifying a health management menu or exercise plan based on the analysis results;

[1678] A conversational AI means for sending encouraging messages based on the analysis results;

[1679] A system including:

[1680] (Claim 2)

[1681] 2. The system according to claim 1, wherein the means for generating the meal menu and exercise plan takes into consideration personal preferences and experience.

[1682] (Claim 3)

[1683] The system according to claim 1, further comprising means for analyzing data obtained from the acquired weight scale information and photographs of the food consumed, and sending encouraging messages to maintain motivation based on the results.

[1684] "Example 2: Combining Emotion Engines"

[1685] (Claim 1)

[1686] A means for users to enter basic information and submit it for storage in a database;

[1687] A means to gather detailed information and update user profiles using conversational AI and emotion engines;

[1688] A means for the AI ​​to generate and display meal menus and exercise plans based on the updated user profile;

[1689] Users can send weight data and photos of their meals, which are then analyzed by a classification AI.

[1690] A method to modify meal menus and exercise plans based on analytical data and generate messages to maintain motivation.

[1691] A system including:

[1692] (Claim 2)

[1693] 2. The system of claim 1, wherein the conversational AI and emotion engine collects detailed information and updates the user profile, taking into account personal preferences and emotional state.

[1694] (Claim 3)

[1695] The system described in claim 1 is characterized in that the identification AI analyzes the acquired weight data and meal photos, and based on the results, the generation AI generates an amended meal menu and exercise plan and sends messages to maintain motivation.

[1696] "Application example 2 when combining emotion engines"

[1697] (Claim 1)

[1698] A means of communicating with individuals considering dieting,

[1699] means for generating a meal menu and an exercise plan based on the information collected from the conversation;

[1700] A means to obtain and analyze weight scale information and photos of what you eat,

[1701] A means for modifying a meal menu or exercise plan based on the analysis results;

[1702] a means for recognizing an individual's emotional state using an emotion engine and generating a message based thereon;

[1703] A means of providing tailored support to individuals through a virtual environment;

[1704] A system including:

[1705] (Claim 2)

[1706] 2. The system according to claim 1, wherein the means for generating the meal menu and exercise plan takes into account personal preferences, experiences, and emotional state.

[1707] (Claim 3)

[1708] The system according to claim 1, further comprising means for analyzing data obtained from the acquired weight scale information and photos of food eaten, and generating and sending encouraging messages to maintain motivation based on the results. [Explanation of symbols]

[1709] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of communicating with individuals considering dieting, means for generating a meal menu and an exercise plan based on the information collected from the conversation; A means to obtain and analyze weight scale information and photos of what you eat, A means for modifying a meal menu or exercise plan based on the analysis results; A system including:

2. 2. The system according to claim 1, wherein the means for generating the meal menu and exercise plan takes into consideration personal preferences and experiences.

3. The system according to claim 1, further comprising means for analyzing data obtained from the acquired weight scale information and photos of food eaten, and sending encouraging messages to maintain motivation based on the results.

Images