system

Abstract

We provide the system. [Solution] Means of collecting individual information, Means for generating an individualized meal and exercise plan based on the aforementioned information, Means for providing the generated plan, A means of receiving progress data and adjusting the plan, A means of evaluating emotional state and providing appropriate messages to maintain motivation, The collected information provides a means for household appliances to adapt to the user's lifestyle, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Creating a diet plan according to an individual's constitution and lifestyle requires information analysis and appropriate plan design, which is complicated by ordinary methods. In particular, busy users find it difficult to find exercises that can be done in a short time and have difficulty maintaining motivation to continue the plan. In such a situation, there is a need for means to automatically provide an optimal plan for users and support continuation.

Means for Solving the Problems

[0005] This invention provides a system that collects individual information and generates meal and exercise plans based on it. In particular, it can receive progress data in real time, including suggestions for short-duration exercises, and adjust the plan accordingly. Furthermore, it provides a function to evaluate the user's emotional state and provide appropriate messages to maintain motivation, thereby realizing the provision of individually optimized plans.

[0006] "Individualized information" refers to data related to the user's physical characteristics and lifestyle, including age, gender, activity level, current and target weight, and daily schedule.

[0007] A "personalized plan" is a set of suggestions regarding diet, exercise, supplements, etc., created based on the user's individual information, with the aim of providing optimal health management for that user.

[0008] "Progress data" refers to information that represents the user's achievements and changes in their diet and health management as numerical data and records.

[0009] "Emotional state" refers to the user's psychological condition and mood, and includes factors that influence motivation and willingness.

[0010] "Short-duration exercises" refer to exercises that users can incorporate into their busy lives and that can be completed in about 1 to 5 minutes. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] The system for implementing this invention is designed to collect individual user information and automatically generate an optimized health management plan based on that data. The program processing of this system is described below in natural language.

[0033] System Configuration

[0034] The device provides a user interface and displays forms for users to input individual information, such as age, gender, activity level, target weight, and daily schedule.

[0035] The server receives data sent from the terminal and uses machine learning algorithms to generate a personalized plan. The algorithms analyze historical data and existing health management databases to identify the optimal combination of diet, exercise, and supplements.

[0036] Users receive the generated plan via their device, use it in their daily lives, and input their progress into the device. Progress data is continuously collected and sent to the server.

[0037] Specific example

[0038] For example, consider a case where a user uses the system with the following goal in mind.

[0039] Age: 35

[0040] Gender: Female

[0041] Current weight: 70kg

[0042] Target weight: 62kg

[0043] Daily activity level: Moderate

[0044] Schedule: I work Monday through Friday, and my time is limited.

[0045] Once the user enters the above information into their device, the server generates a plan optimized for the user's body type and goals based on the data. The server suggests breakfast, lunch, and dinner menus, calculates calories, and also suggests workouts that fit into the user's daily schedule. Short exercises include "2-minute stretches that can be done in the office."

[0046] Users use these plans as daily guides, entering their progress into their devices. The server evaluates the data in real time and automatically adjusts the plan as needed. These adjustments may include specific changes such as increasing exercise intensity or reviewing calorie intake. Furthermore, the server analyzes the user's psychological state and sends encouraging messages to their devices to help maintain motivation.

[0047] In this way, the system of this invention provides users with a sustainable and personalized health management plan, and helps users efficiently achieve their goals.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The device displays a basic information input form to the user. This form includes information such as age, gender, activity level, current weight and target weight, and daily schedule. The user enters this information into the form and sends it to the device.

[0051] Step 2:

[0052] The device sends individual information obtained from the user to the server.

[0053] Step 3:

[0054] The server uses the received information to compare and analyze it with past health management data and information on similar users in the database. This analysis provides the insights necessary to design a plan that is optimal for the user's physical characteristics and lifestyle.

[0055] Step 4:

[0056] The server generates meal plans, exercise plans, and supplement plans based on the analyzed data. The algorithm calculates appropriate calorie intake and exercise frequency, and also includes suggestions for short, manageable "spare-time workouts."

[0057] Step 5:

[0058] The server sends the generated plan information to the device. The device then visually presents this information to the user through its user interface and sets up notifications for workouts during spare time based on the schedule.

[0059] Step 6:

[0060] Users input their daily progress (e.g., weight changes and exercise status) into their device and provide feedback. The device then sends this information to the server.

[0061] Step 7:

[0062] The server evaluates user progress data in real time and adjusts the plan as needed to help users achieve their goals. These adjustments may include reviewing exercise intensity or modifying meal plans.

[0063] Step 8:

[0064] The server analyzes the user's psychological state and, if it determines that their motivation is low, generates encouraging or reminder messages. The device then notifies the user of these messages to help maintain their motivation.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] To lead a healthy life, people need individually optimized diet and exercise plans. However, manually creating these plans to suit individual lifestyles is difficult, and maintaining consistent motivation is also a challenge. Furthermore, adjusting plans in real time to match dynamic lifestyles is difficult. Therefore, there is a need to efficiently and automatically generate personalized health management plans that can be adapted to the user's life.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for generating personalized plans including meals, exercise, and nutrition using machine learning technology; means for receiving progress data and automatically adjusting the plan in real time; and means for evaluating the user's psychological state and generating and providing encouraging messages. This makes it possible to efficiently provide optimal health management tailored to each user's needs and lifestyle, as well as maintain motivation.

[0070] "Information-collecting terminal means" refers to a device or interface that provides a device for obtaining specific individual information from a user, and has the function of securely processing and transmitting that information.

[0071] "Means of sending collected information to the server" refers to a combination of communication protocols and software used to securely and efficiently transfer information obtained from users to the server.

[0072] A "server system that generates personalized plans including diet, exercise, and nutrition using machine learning technology" is a server system that analyzes received data and uses machine learning algorithms to create health management plans tailored to individual needs.

[0073] "Means of providing generated plans via terminals" refers to a system that has the function of displaying and providing health management plans created on a server to a terminal so that users can easily access them.

[0074] "A means of receiving progress data and automatically adjusting the plan in real time" refers to a system operation that dynamically updates the plan content according to the user's progress and provides the most appropriate advice quickly.

[0075] "Means of evaluating psychological state and generating and providing encouraging messages" refers to a process for analyzing a user's emotional and motivational tendencies and providing appropriate positive feedback and encouragement as needed.

[0076] "A plan generation method that proposes physical activities that can be done in a short amount of time" refers to a system function that effectively recommends exercises and physical activities that can be done in a short amount of time according to the user's schedule.

[0077] "Information gathering methods that acquire individual time management information and use it to propose plans" refers to elements of a system that understands the user's lifestyle and how they spend their time, and designs an effective health plan based on the collected information.

[0078] This invention is a system that efficiently collects user-specific information and proposes a personalized health management plan based on that information. Users input information such as age, gender, activity level, target weight, and daily schedule via a terminal. The terminal temporarily stores this information and transmits it to a server via the internet.

[0079] The server analyzes the received information and generates a personalized health management plan using machine learning techniques. Specifically, it preprocesses the data using the Python Pandas library and utilizes machine learning algorithms such as TENSORFLOW® and scikit-learn to derive the optimal combination of diet, exercise, and nutrition. The server also assesses the user's psychological state and generates encouraging messages to maintain motivation using a generative AI model. These messages are sent to the terminal and provided to the user.

[0080] For example, if a 35-year-old woman has a goal of "reaching a target weight of 62kg," the server will propose a plan that takes into account her diet and exercise frequency. This plan might include short exercises like "2-minute stretches at the office." The user then follows this plan daily and reports their progress to the server via their device, allowing the plan to be adjusted in real time.

[0081] An example of a prompt message might be, "35-year-old female user, target weight 62kg, suggest office stretches." This allows users to manage their health in a way that suits their lifestyle and helps maintain motivation towards their goals.

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

[0083] Step 1:

[0084] The terminal provides an interface for users to input individual information. Users enter information such as age, gender, activity level, target weight, and daily schedule. The terminal temporarily stores the input data, verifies the form information, and prepares for the next processing step. The output is user information organized in a communicable data format (e.g., JSON).

[0085] Step 2:

[0086] The terminal sends the organized user data to the server via a secure communication protocol (e.g., HTTPS). The terminal waits for the transmission to complete and displays a success notification to the user. The output is the user information sent to the server.

[0087] Step 3:

[0088] The server analyzes user data received from the terminal. The input data is preprocessed using Python's Pandas library to correct inconsistencies and missing information. The output of the analysis is a clear dataset that can be used as input for machine learning algorithms.

[0089] Step 4:

[0090] The server uses machine learning techniques to generate personalized plans for each user. It employs TensorFlow and scikit-learn algorithms to create meal plans and exercise programs. The output is a personalized health management plan tailored to the user's needs.

[0091] Step 5:

[0092] The server sends the generated plan to the terminal and provides it to the user. The plan is converted to a format that is easy for the terminal to view and displayed in an interface suitable for the user. The output is the health management plan displayed on the terminal.

[0093] Step 6:

[0094] The user performs daily activities according to the plan and enters progress information into the device. The device prepares to send the progress data to the server. The output is data with the progress status properly recorded.

[0095] Step 7:

[0096] The server analyzes the received progress data and automatically adjusts the plan as needed. It processes data in real time and updates the plan using machine learning. The output is the adjusted, up-to-date health management plan.

[0097] Step 8:

[0098] The server uses a generative AI model to assess the user's psychological state and generate messages to maintain motivation. It utilizes prompts to create positive feedback. The output is an encouraging message provided to the user.

[0099] Through these steps, users can consistently implement personalized health management plans and efficiently work towards achieving their goals.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] In today's world, individualized health management and lifestyle improvements are in demand, but many current technologies fail to adequately meet the individual needs of users and lack the support necessary to maintain motivation. A major challenge, in particular, is whether it's possible to support users' health management in a way that seamlessly integrates into their daily lives at home.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for collecting individual information, means for generating personalized meal and exercise plans, and means for adapting the home-use device to the user's lifestyle based on the collected information. This allows the user to continuously manage their health through a natural experience in the home, maintaining motivation and improving their lifestyle.

[0105] "Individualized information" refers to data about a user's specific attributes or status, including age, gender, and activity level.

[0106] A "diet and exercise plan" refers to a proposal that includes a tailored meal plan and exercise program for the user.

[0107] A "generated plan" refers to a diet and exercise plan created based on individual information.

[0108] "Progress data" refers to information regarding the results of the user's plan implementation and changes in their physical condition.

[0109] "Emotional state" refers to the user's psychological and mental condition, and is a factor that the system considers in order to maintain motivation.

[0110] A "household medical device" is a mechanical unit installed in a user's living environment to provide personalized health management support.

[0111] The system that realizes this invention provides an integrated environment for individualized health management within the home. The system includes terminals, a server, and household machinery.

[0112] The server uses machine learning algorithms (such as scikit-learn) to analyze information and generate personalized diet and exercise plans for users. Based on the collected individual information (e.g., age, gender, activity level), the server creates a plan to support the user's health goals.

[0113] The terminal provides a user interface, collects individual information from the user, and displays input forms for tracking generated plans and progress. As the user enters daily progress, the terminal sends new data to the server, helping to optimize the plan.

[0114] Home-use health devices directly engage with users' daily lives, supporting them in tasks such as meal preparation and exercise. This allows users to seamlessly integrate health management into their daily routines.

[0115] For example, in the morning, the device might suggest to the user, "Good morning. You have a little time today, so why not try 15 minutes of yoga?" The server then displays encouraging messages based on the user's emotional state on the terminal or device to help maintain motivation. This system is designed to always provide the user with the optimal situation and naturally support their daily health management.

[0116] An example of a prompt would be, "Please suggest a summer menu for a 35-year-old woman with a moderate activity level and a target weight of 62 kg." Using such prompts allows the generative AI model to create a more precise plan.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The device displays a user interface, and the user enters individual information. This data includes age, gender, activity level, and target weight. The device collects this information and prepares to send it to the server.

[0120] Step 2:

[0121] The server receives individual information sent from the device. Based on this data, it uses machine learning algorithms (such as scikit-learn) to generate personalized meal and exercise plans. The generating AI model creates an optimal plan based on the user's attributes and sends the plan to the device.

[0122] Step 3:

[0123] The device displays a personalized plan received from the server to the user. The user can review the displayed plan and incorporate it into their daily life. The device also provides an input form for tracking progress, allowing the user to enter their daily progress.

[0124] Step 4:

[0125] After the user enters progress data into the device, the device sends it back to the server. The server evaluates the received progress data and adjusts the plan as needed. Specifically, this may involve changing the exercise intensity or modifying the meal plan.

[0126] Step 5:

[0127] The server assesses the user's emotional state and generates appropriate messages to maintain motivation. These messages are sent to the device with the aim of supporting the user's psychological well-being. The device then presents these messages to the user, providing support to help them maintain motivation.

[0128] Step 6:

[0129] Home-use devices implement a plan tailored to the user's lifestyle. For example, they might help prepare breakfast or encourage exercise. The devices harmonize the plan with daily activities, seamlessly supporting the user's health management.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system for recognizing a user's emotions and optimizing their health management and diet plan. The system collects individual user information, evaluates the user's emotional state using an emotion engine, and provides personalized plans and motivational messages.

[0132] System Configuration

[0133] The device provides a user interface and collects individual information from the user. This includes data on age, gender, activity level, current weight, target weight, daily schedule, and the user's emotions. The user enters this information into the device and sends it to the server.

[0134] The server receives individual information sent from the terminal and analyzes the user's information using a database and an emotion engine. This analysis includes generating meal plans, exercise plans, and supplement plans. The emotion engine also analyzes the user's input data and activity records to determine the user's emotional state.

[0135] Based on the results of this emotion recognition, the server generates motivational messages and readjusts exercise and meal plans as needed. This allows users to receive advice that is best suited to their mental state.

[0136] Specific example

[0137] For example, consider a case where a user uses the system under the following conditions.

[0138] Age: 28

[0139] Gender: Male

[0140] Current weight: 85kg

[0141] Target weight: 75kg

[0142] Daily activity level: Low

[0143] Schedule: Irregular work schedule

[0144] When a user enters information into their device, the server uses that data to suggest a low-calorie, nutritious meal plan. In addition, it includes short workouts that can be done in just five minutes, such as "exercises you can do in your office chair."

[0145] (Specific use of the emotional engine)

[0146] The emotion engine evaluates the user's daily emotional state based on their social media activity, surveys, and intuitive input data. If a user inputs that they feel "tired" on a given day, the server uses this data to generate a message suggesting relaxing stretches, along with a message like, "Today is a day to take it easy and focus on getting your body back in shape," and sends it to the device.

[0147] This series of systems aims to maximize the user's continuous health management and weight loss effectiveness, enabling flexible planning that responds to emotions.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The device displays basic information and a mood input form to the user. The user enters their age, gender, activity level, current weight, target weight, daily schedule, and current mood state, and sends the data to the device.

[0151] Step 2:

[0152] The terminal sends the acquired user information to the server. The server receives the user's individual data and stores it in a database.

[0153] Step 3:

[0154] Based on the individual information received, the server uses machine learning algorithms to generate meal plans, exercise plans, and supplement plans tailored to the user's physical characteristics and goals. In doing so, it references success stories from similar users using an existing database to design the optimal plan.

[0155] Step 4:

[0156] The server uses an emotion engine to analyze the user's emotional state. Based on the input emotional data and information obtained from social media activity, it quantifies the user's psychological state and generates appropriate motivational messages.

[0157] Step 5:

[0158] The server sends the generated plan and message to the device. The device then presents this information to the user in a visually clear format. It also sets up notifications for workouts that fit the user's schedule and allow for free time workouts.

[0159] Step 6:

[0160] Users perform daily activities based on the provided plan and input their progress and emotional changes into their device. This information is periodically sent from the device to the server.

[0161] Step 7:

[0162] The server receives progress data and emotional changes from the user and re-evaluates the plan in real time. If necessary, it adjusts the content of meals and exercise and generates new messages to maintain the user's motivation.

[0163] Step 8:

[0164] The device will notify the user of the adjusted plan and any new messages, encouraging them to use it as a guide for their next actions.

[0165] (Example 2)

[0166] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0167] In today's world, while there is a wealth of information and options available for personal health management and dieting, finding a personalized approach is difficult. Furthermore, there is a lack of systems that can flexibly adapt to emotional changes, highlighting the need for support that helps users maintain motivation and achieve their health goals.

[0168] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0169] This invention includes a server that includes a method for collecting personal information, a method for generating personalized diet and exercise plans, and a method for evaluating the user's emotional state and providing appropriate messages for motivation. This enables the provision of flexible health plans tailored to an individual's emotional state and the maintenance of effective motivation.

[0170] "Methods of collecting personal information" refer to means of obtaining data such as age, gender, activity level, weight, and schedule from users and storing it in a format that can be analyzed within the system.

[0171] "Methods for generating personalized diet and exercise plans" refer to means of formulating nutritional intake and exercise plans optimized for a specific user's health condition and goals, based on collected personal data.

[0172] "Methods for providing generated plans" refer to means of visually or audibly presenting the formulated individual meal and exercise plans to the user.

[0173] "A method of receiving progress data and adjusting plans" refers to a means of evaluating current progress based on user feedback and regular data entry, and improving or modifying existing plans as needed.

[0174] "A method for evaluating the emotional state of users and providing appropriate messages for motivation" refers to a means of analyzing text input and activity records to understand the user's emotions and generate encouraging or cautionary messages accordingly.

[0175] "A method for analyzing natural language input to measure daily emotions" is a means of quantifying a user's mood and emotional trends by analyzing free-form emotional words entered by the user.

[0176] This invention is a system for providing users with personalized health management plans. Its main components include a terminal, a server, and multiple software components.

[0177] The terminal provides the user interface and collects input data from users. Each user enters information such as age, gender, activity level, weight, and individual schedule information into the terminal. This information is transmitted to the server using a secure protocol.

[0178] The server stores the received information in a database and performs personalized analysis for each user. This analysis uses a software component called an emotion engine, which evaluates the user's daily emotional state based on social media activity and free-text input data. Based on this evaluation, personalized diet and exercise plans are developed. The server also uses a generative AI model to generate motivational messages that respond to daily emotional changes.

[0179] For example, if a user prefers a low-carbohydrate diet, the server will suggest a meal plan tailored to that. Furthermore, it can generate motivational messages such as, "Let's try exercising today to feel positive."

[0180] When users provide feedback on their experiences and results via their devices, the server uses that information to further optimize the plan. This feedback loop allows users to continuously receive optimized advice and be supported in achieving their health management goals.

[0181] An example of a prompt is, "Create an optimal health plan based on emotions and health data." Upon entering this prompt, the generating AI model performs the necessary analysis and proposes a personalized health plan.

[0182] This system supports individual health improvement by enabling flexible planning that takes into account the user's daily health condition and helping to maintain motivation.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] Users enter personal information through the terminal's interface. This input includes data such as age, gender, activity level, current weight, target weight, daily schedule, and emotional state. The terminal transmits this information to the server using a secure protocol. The entered data is used as basic input data to generate a personalized plan.

[0186] Step 2:

[0187] The server stores personal information received from the terminal in a database and begins analysis using an emotion engine. This analysis evaluates the user's current health and emotional state based on the collected data. In particular, emotional data obtained from social media and free-text input is quantified through text analysis to determine the user's emotional tendencies. The output of this step is a profile of the user's health and emotional state.

[0188] Step 3:

[0189] The server then uses a generative AI model to develop a personalized diet and exercise plan. The input is the profile obtained in step 2, and the generated plan includes specific suggestions to help the user achieve their goals, such as low-calorie meals and short, manageable exercise routines. The output is a specific diet and exercise schedule.

[0190] Step 4:

[0191] The server generates motivational messages based on the user's emotional state. Using the evaluation results of the emotion engine as input, it creates emotionally resonant messages such as, "Let's take a walk today to clear our heads." These messages are provided to the user as feedback, supporting their daily activities and self-awareness.

[0192] Step 5:

[0193] The terminal displays the plan and messages sent from the server to the user. The user acts according to the presented plan and can also input feedback from the terminal. This feedback is sent back to the server and used to improve future plans.

[0194] Step 6:

[0195] The server receives feedback data from users and reflects it in the database. This allows for the optimization of plans based on the feedback. The input is user feedback data, and the output is an improved next plan and updated sentiment evaluation results.

[0196] (Application Example 2)

[0197] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0198] Traditional health management systems lack sufficient personalized support that reflects the user's emotional state, resulting in difficulties in maintaining user motivation for health and weight management. Furthermore, there is a lack of methods to provide highly accurate messages that respond to emotions, and automated support that can be effectively used within the home is not available. This project aims to solve these problems.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0200] In this invention, the server includes means for collecting data to evaluate the user's emotional state on a daily basis, means for generating and providing a personalized health and weight management plan, means for generating and providing encouraging messages according to the user's estimated emotional state, and means for assisting health management by operating home automation equipment. This provides personalized support and encouragement according to the user's emotional state, making effective health management possible even within the home.

[0201] A "user" is an individual who utilizes the system and is the person who receives support for health management and weight management.

[0202] "Emotional state" refers to a user's psychological and emotional state, and is a factor that influences their motivation and mental health.

[0203] "Means of data collection" refers to methods or devices for gathering necessary information from users, and which have the function of acquiring information used to evaluate emotional states.

[0204] A "personalized health and weight management plan" is a diet and fitness plan customized to the user's specific needs and goals.

[0205] An "encouraging message" is a statement designed to boost motivation, generated in response to the user's emotional state, and provides guidance to support individual actions.

[0206] "Household automation equipment" refers to electronic devices and equipment used in the home that can be linked to and operated by systems designed to support health management.

[0207] This invention is implemented as a system including a home-use robot. This system provides various means to support the user's daily health management and weight management. Specifically, it has the function of evaluating the user's emotional state and providing an individualized health management plan based on that.

[0208] The server collects the data necessary to assess the user's emotional state. This uses microphones, cameras, and other sensors typically found in consumer robots. This hardware recognizes the user's speech and physical indicators, and converts them into data. The collected data is sent to the server, where a generative AI model is used to analyze the user's emotional state.

[0209] Based on the analysis results, the server creates a personalized health and weight management plan, which is then delivered to the user by a consumer robot. The plan includes meal suggestions and exercise plans, tailored to the user's current condition and goals. Furthermore, it generates encouraging messages based on the user's emotions and provides feedback. These messages are created using a generative AI model employing natural language processing.

[0210] For example, if a user is feeling stressed, the server might suggest relaxing stretches and provide a message such as, "Take it easy and relax today." A specific example of a prompt might be, "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?"

[0211] This provides continuous and personalized support within the home, enabling users to effectively manage their own health.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The user enters data about themselves into the device. This includes information about their age, gender, current weight, target weight, activity level, and schedule. The device sends this information to the server. The entered data is then processed as basic data used to create a customized plan.

[0215] Step 2:

[0216] The server receives data sent from the terminal and stores it in a database. To analyze the data, it generates a preliminary health management plan based on each user's activity level, schedule, and target weight. Storing the data in the database allows it to be accumulated as long-term management information.

[0217] Step 3:

[0218] Sensors installed in consumer robots detect data related to the user's emotional state in real time. This data is collected via microphones and cameras, and the device transmits it to a server. This information serves as input data for analyzing the user's psychological state and evaluating their emotions.

[0219] Step 4:

[0220] The server uses a generative AI model to analyze the user's emotional state. It uses information related to emotional state as input data and uses it to determine the user's psychological state. The analysis results are output and serve as a basis for generating personalized advice.

[0221] Step 5:

[0222] The server optimizes health and weight management plans, taking into account the analyzed emotional state. It creates plans that include meal suggestions and recommendations for specific exercises, and communicates them to the user via a home-use robot. The optimized plan is output, and feedback best suited to the user's condition is provided.

[0223] Step 6:

[0224] Using a generative AI model, the system generates encouraging messages tailored to the user's emotional state. The prompt is "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?" The system then outputs a clear and actionable message.

[0225] Step 7:

[0226] To support health management within the home, consumer robots perform automated actions. For example, they might demonstrate suggested stretching exercises and instruct the user on how to perform the actions. This allows the user to immediately carry out the suggested actions.

[0227] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0228] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] The system for implementing this invention is designed to collect individual user information and automatically generate an optimized health management plan based on that data. The program processing of this system is described below in natural language.

[0244] System Configuration

[0245] The device provides a user interface and displays forms for users to input individual information, such as age, gender, activity level, target weight, and daily schedule.

[0246] The server receives data sent from the terminal and uses machine learning algorithms to generate a personalized plan. The algorithms analyze historical data and existing health management databases to identify the optimal combination of diet, exercise, and supplements.

[0247] Users receive the generated plan via their device, use it in their daily lives, and input their progress into the device. Progress data is continuously collected and sent to the server.

[0248] Specific example

[0249] For example, consider a case where a user uses the system with the following goal in mind.

[0250] Age: 35

[0251] Gender: Female

[0252] Current weight: 70kg

[0253] Target weight: 62kg

[0254] Daily activity level: Moderate

[0255] Schedule: I work Monday through Friday, and my time is limited.

[0256] Once the user enters the above information into their device, the server generates a plan optimized for the user's body type and goals based on the data. The server suggests breakfast, lunch, and dinner menus, calculates calories, and also suggests workouts that fit into the user's daily schedule. Short exercises include "2-minute stretches that can be done in the office."

[0257] Users use these plans as daily guides, entering their progress into their devices. The server evaluates the data in real time and automatically adjusts the plan as needed. These adjustments may include specific changes such as increasing exercise intensity or reviewing calorie intake. Furthermore, the server analyzes the user's psychological state and sends encouraging messages to their devices to help maintain motivation.

[0258] In this way, the system of this invention provides users with a sustainable and personalized health management plan, and helps users efficiently achieve their goals.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The device displays a basic information input form to the user. This form includes information such as age, gender, activity level, current weight and target weight, and daily schedule. The user enters this information into the form and sends it to the device.

[0262] Step 2:

[0263] The device sends individual information obtained from the user to the server.

[0264] Step 3:

[0265] The server uses the received information to compare and analyze it with past health management data and information on similar users in the database. This analysis provides the insights necessary to design a plan that is optimal for the user's physical characteristics and lifestyle.

[0266] Step 4:

[0267] The server generates meal plans, exercise plans, and supplement plans based on the analyzed data. The algorithm calculates appropriate calorie intake and exercise frequency, and also includes suggestions for short, manageable "spare-time workouts."

[0268] Step 5:

[0269] The server sends the generated plan information to the device. The device then visually presents this information to the user through its user interface and sets up notifications for workouts during spare time based on the schedule.

[0270] Step 6:

[0271] Users input their daily progress (e.g., weight changes and exercise status) into their device and provide feedback. The device then sends this information to the server.

[0272] Step 7:

[0273] The server evaluates user progress data in real time and adjusts the plan as needed to help users achieve their goals. These adjustments may include reviewing exercise intensity or modifying meal plans.

[0274] Step 8:

[0275] The server analyzes the user's psychological state and, if it determines that their motivation is low, generates encouraging or reminder messages. The device then notifies the user of these messages to help maintain their motivation.

[0276] (Example 1)

[0277] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0278] To lead a healthy life, people need individually optimized diet and exercise plans. However, manually creating these plans to suit individual lifestyles is difficult, and maintaining consistent motivation is also a challenge. Furthermore, adjusting plans in real time to match dynamic lifestyles is difficult. Therefore, there is a need to efficiently and automatically generate personalized health management plans that can be adapted to the user's life.

[0279] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes means for generating personalized plans including meals, exercise, and nutrition using machine learning technology; means for receiving progress data and automatically adjusting the plan in real time; and means for evaluating the user's psychological state and generating and providing encouraging messages. This makes it possible to efficiently provide optimal health management tailored to each user's needs and lifestyle, as well as maintain motivation.

[0281] "Information-collecting terminal means" refers to a device or interface that provides a device for obtaining specific individual information from a user, and has the function of securely processing and transmitting that information.

[0282] "Means of sending collected information to the server" refers to a combination of communication protocols and software used to securely and efficiently transfer information obtained from users to the server.

[0283] A "server system that generates personalized plans including diet, exercise, and nutrition using machine learning technology" is a server system that analyzes received data and uses machine learning algorithms to create health management plans tailored to individual needs.

[0284] The means of "providing the generated plan through a terminal" is a system that has the function of displaying and providing on the terminal the health management plan created on the server so that the user can easily access it.

[0285] The means of "receiving progress data and automatically adjusting the plan in real time" is an operation by a system for dynamically updating the content of the plan according to the progress of the user and providing prompt and optimal advice.

[0286] The means of "evaluating the mental state, generating and providing encouraging messages" is a process for analyzing the tendencies of the user's emotions and motivation and appropriately providing positive feedback and encouragement as needed.

[0287] The means of "generating a plan that proposes physical activities that can be done in a short time" is a function of a system that effectively recommends exercises and physical activities that can be carried out in a short time according to the user's schedule.

[0288] The means of "acquiring individual time management information and using it for plan proposal" is an element of a system for grasping the user's life schedule and how time is used and designing an effective health plan based on the collected information.

[0289] This invention is a system that efficiently collects user-specific information and proposes an individualized health management plan based on it. The user inputs information such as age, gender, activity level, target weight, and daily schedule via a terminal. The terminal temporarily stores this information and transmits it to the server via the Internet.

[0290] The server analyzes the received information and generates a personalized health management plan using machine learning techniques. Specifically, it preprocesses the data using the Python Pandas library and utilizes machine learning algorithms such as TensorFlow and scikit-learn to derive the optimal combination of diet, exercise, and nutrition. The server also assesses the user's psychological state and generates motivational messages using a generative AI model. These messages are sent to the terminal and provided to the user.

[0291] For example, if a 35-year-old woman has a goal of "reaching a target weight of 62kg," the server will propose a plan that takes into account her diet and exercise frequency. This plan might include short exercises like "2-minute stretches at the office." The user then follows this plan daily and reports their progress to the server via their device, allowing the plan to be adjusted in real time.

[0292] An example of a prompt message might be, "35-year-old female user, target weight 62kg, suggest office stretches." This allows users to manage their health in a way that suits their lifestyle and helps maintain motivation towards their goals.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The terminal provides an interface for users to input individual information. Users enter information such as age, gender, activity level, target weight, and daily schedule. The terminal temporarily stores the input data, verifies the form information, and prepares for the next processing step. The output is user information organized in a communicable data format (e.g., JSON).

[0296] Step 2:

[0297] The terminal sends the organized user data to the server via a secure communication protocol (e.g., HTTPS). The terminal waits for the transmission to complete and displays a success notification to the user. The output is the user information sent to the server.

[0298] Step 3:

[0299] The server analyzes user data received from the terminal. The input data is preprocessed using Python's Pandas library to correct inconsistencies and missing information. The output of the analysis is a clear dataset that can be used as input for machine learning algorithms.

[0300] Step 4:

[0301] The server uses machine learning techniques to generate personalized plans for each user. It employs TensorFlow and scikit-learn algorithms to create meal plans and exercise programs. The output is a personalized health management plan tailored to the user's needs.

[0302] Step 5:

[0303] The server sends the generated plan to the terminal and provides it to the user. The plan is converted to a format that is easy for the terminal to view and displayed in an interface suitable for the user. The output is the health management plan displayed on the terminal.

[0304] Step 6:

[0305] The user performs daily activities according to the plan and enters progress information into the device. The device prepares to send the progress data to the server. The output is data with the progress status properly recorded.

[0306] Step 7:

[0307] The server analyzes the received progress data and automatically adjusts the plan as needed. It processes the data in real time and updates the plan content through machine learning. The output is the latest adjusted health management plan.

[0308] Step 8:

[0309] The server uses the generative AI model to evaluate the user's mental state and generates messages to maintain motivation. It utilizes prompt sentences to create positive feedback. The output is the encouraging message provided to the user.

[0310] Through these steps, the user can consistently achieve an individualized health management plan and efficiently aim to achieve the goals.

[0311] (Application Example 1)

[0312] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] In modern times, there is a demand for individual health management and improvement of lifestyle habits. However, many current technologies do not fully meet the individual needs of users and lack support for maintaining motivation. In particular, whether it is possible to support the user's health management in a form that naturally blends into daily life at home is a major issue.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0315] In this invention, the server includes means for collecting individual information, means for generating individualized diet and exercise plans, and means for enabling household mechanical devices to adapt to the user's lifestyle habits based on the collected information. Thereby, the user can continuously perform health management through natural experiences within the home, and it becomes possible to improve lifestyle habits while maintaining motivation.

[0316] "Individualized information" refers to data about a user's specific attributes or status, including age, gender, and activity level.

[0317] A "diet and exercise plan" refers to a proposal that includes a tailored meal plan and exercise program for the user.

[0318] A "generated plan" refers to a diet and exercise plan created based on individual information.

[0319] "Progress data" refers to information regarding the results of the user's plan implementation and changes in their physical condition.

[0320] "Emotional state" refers to the user's psychological and mental condition, and is a factor that the system considers in order to maintain motivation.

[0321] A "household medical device" is a mechanical unit installed in a user's living environment to provide personalized health management support.

[0322] The system that realizes this invention provides an integrated environment for individualized health management within the home. The system includes terminals, a server, and household machinery.

[0323] The server uses machine learning algorithms (such as scikit-learn) to analyze information and generate personalized diet and exercise plans for users. Based on the collected individual information (e.g., age, gender, activity level), the server creates a plan to support the user's health goals.

[0324] The terminal provides a user interface, collects individual information from the user, and displays input forms for tracking generated plans and progress. As the user enters daily progress, the terminal sends new data to the server, helping to optimize the plan.

[0325] Home-use health devices directly engage with users' daily lives, supporting them in tasks such as meal preparation and exercise. This allows users to seamlessly integrate health management into their daily routines.

[0326] For example, in the morning, the device might suggest to the user, "Good morning. You have a little time today, so why not try 15 minutes of yoga?" The server then displays encouraging messages based on the user's emotional state on the terminal or device to help maintain motivation. This system is designed to always provide the user with the optimal situation and naturally support their daily health management.

[0327] An example of a prompt would be, "Please suggest a summer menu for a 35-year-old woman with a moderate activity level and a target weight of 62 kg." Using such prompts allows the generative AI model to create a more precise plan.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The device displays a user interface, and the user enters individual information. This data includes age, gender, activity level, and target weight. The device collects this information and prepares to send it to the server.

[0331] Step 2:

[0332] The server receives individual information sent from the device. Based on this data, it uses machine learning algorithms (such as scikit-learn) to generate personalized meal and exercise plans. The generating AI model creates an optimal plan based on the user's attributes and sends the plan to the device.

[0333] Step 3:

[0334] The device displays a personalized plan received from the server to the user. The user can review the displayed plan and incorporate it into their daily life. The device also provides an input form for tracking progress, allowing the user to enter their daily progress.

[0335] Step 4:

[0336] After the user enters progress data into the device, the device sends it back to the server. The server evaluates the received progress data and adjusts the plan as needed. Specifically, this may involve changing the exercise intensity or modifying the meal plan.

[0337] Step 5:

[0338] The server assesses the user's emotional state and generates appropriate messages to maintain motivation. These messages are sent to the device with the aim of supporting the user's psychological well-being. The device then presents these messages to the user, providing support to help them maintain motivation.

[0339] Step 6:

[0340] Home-use devices implement a plan tailored to the user's lifestyle. For example, they might help prepare breakfast or encourage exercise. The devices harmonize the plan with daily activities, seamlessly supporting the user's health management.

[0341] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0342] This invention is a system for recognizing a user's emotions and optimizing their health management and diet plan. The system collects individual user information, evaluates the user's emotional state using an emotion engine, and provides personalized plans and motivational messages.

[0343] System Configuration

[0344] The device provides a user interface and collects individual information from the user. This includes data on age, gender, activity level, current weight, target weight, daily schedule, and the user's emotions. The user enters this information into the device and sends it to the server.

[0345] The server receives individual information sent from the terminal and analyzes the user's information using a database and an emotion engine. This analysis includes generating meal plans, exercise plans, and supplement plans. The emotion engine also analyzes the user's input data and activity records to determine the user's emotional state.

[0346] Based on the results of this emotion recognition, the server generates motivational messages and readjusts exercise and meal plans as needed. This allows users to receive advice that is best suited to their mental state.

[0347] Specific example

[0348] For example, consider a case where a user uses the system under the following conditions.

[0349] Age: 28

[0350] Gender: Male

[0351] Current weight: 85kg

[0352] Target weight: 75kg

[0353] Daily activity level: Low

[0354] Schedule: Irregular work schedule

[0355] When a user enters information into their device, the server uses that data to suggest a low-calorie, nutritious meal plan. In addition, it includes short workouts that can be done in just five minutes, such as "exercises you can do in your office chair."

[0356] (Specific use of the emotional engine)

[0357] The emotion engine evaluates the user's daily emotional state based on their social media activity, surveys, and intuitive input data. If a user inputs that they feel "tired" on a given day, the server uses this data to generate a message suggesting relaxing stretches, along with a message like, "Today is a day to take it easy and focus on getting your body back in shape," and sends it to the device.

[0358] This series of systems aims to maximize the user's continuous health management and weight loss effectiveness, enabling flexible planning that responds to emotions.

[0359] The following describes the processing flow.

[0360] Step 1:

[0361] The device displays basic information and a mood input form to the user. The user enters their age, gender, activity level, current weight, target weight, daily schedule, and current mood state, and sends the data to the device.

[0362] Step 2:

[0363] The terminal sends the acquired user information to the server. The server receives the user's individual data and stores it in a database.

[0364] Step 3:

[0365] Based on the individual information received, the server uses machine learning algorithms to generate meal plans, exercise plans, and supplement plans tailored to the user's physical characteristics and goals. In doing so, it references success stories from similar users using an existing database to design the optimal plan.

[0366] Step 4:

[0367] The server uses an emotion engine to analyze the user's emotional state. Based on the input emotional data and information obtained from social media activity, it quantifies the user's psychological state and generates appropriate motivational messages.

[0368] Step 5:

[0369] The server sends the generated plan and message to the device. The device then presents this information to the user in a visually clear format. It also sets up notifications for workouts that fit the user's schedule and allow for free time workouts.

[0370] Step 6:

[0371] Users perform daily activities based on the provided plan and input their progress and emotional changes into their device. This information is periodically sent from the device to the server.

[0372] Step 7:

[0373] The server receives progress data and emotional changes from the user and re-evaluates the plan in real time. If necessary, it adjusts the content of meals and exercise and generates new messages to maintain the user's motivation.

[0374] Step 8:

[0375] The device will notify the user of the adjusted plan and any new messages, encouraging them to use it as a guide for their next actions.

[0376] (Example 2)

[0377] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0378] In today's world, while there is a wealth of information and options available for personal health management and dieting, finding a personalized approach is difficult. Furthermore, there is a lack of systems that can flexibly adapt to emotional changes, highlighting the need for support that helps users maintain motivation and achieve their health goals.

[0379] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0380] This invention includes a server that includes a method for collecting personal information, a method for generating personalized diet and exercise plans, and a method for evaluating the user's emotional state and providing appropriate messages for motivation. This enables the provision of flexible health plans tailored to an individual's emotional state and the maintenance of effective motivation.

[0381] "Methods of collecting personal information" refer to means of obtaining data such as age, gender, activity level, weight, and schedule from users and storing it in a format that can be analyzed within the system.

[0382] "Methods for generating personalized diet and exercise plans" refer to means of formulating nutritional intake and exercise plans optimized for a specific user's health condition and goals, based on collected personal data.

[0383] "Methods for providing generated plans" refer to means of visually or audibly presenting the formulated individual meal and exercise plans to the user.

[0384] "A method of receiving progress data and adjusting plans" refers to a means of evaluating current progress based on user feedback and regular data entry, and improving or modifying existing plans as needed.

[0385] "A method for evaluating the emotional state of users and providing appropriate messages for motivation" refers to a means of analyzing text input and activity records to understand the user's emotions and generate encouraging or cautionary messages accordingly.

[0386] "A method for analyzing natural language input to measure daily emotions" is a means of quantifying a user's mood and emotional trends by analyzing free-form emotional words entered by the user.

[0387] This invention is a system for providing users with personalized health management plans. Its main components include a terminal, a server, and multiple software components.

[0388] The terminal provides the user interface and collects input data from users. Each user enters information such as age, gender, activity level, weight, and individual schedule information into the terminal. This information is transmitted to the server using a secure protocol.

[0389] The server stores the received information in a database and performs personalized analysis for each user. This analysis uses a software component called an emotion engine, which evaluates the user's daily emotional state based on social media activity and free-text input data. Based on this evaluation, personalized diet and exercise plans are developed. The server also uses a generative AI model to generate motivational messages that respond to daily emotional changes.

[0390] For example, if a user prefers a low-carbohydrate diet, the server will suggest a meal plan tailored to that. Furthermore, it can generate motivational messages such as, "Let's try exercising today to feel positive."

[0391] When users provide feedback on their experiences and results via their devices, the server uses that information to further optimize the plan. This feedback loop allows users to continuously receive optimized advice and be supported in achieving their health management goals.

[0392] An example of a prompt is, "Create an optimal health plan based on emotions and health data." Upon entering this prompt, the generating AI model performs the necessary analysis and proposes a personalized health plan.

[0393] This system supports individual health improvement by enabling flexible planning that takes into account the user's daily health condition and helping to maintain motivation.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] Users enter personal information through the terminal's interface. This input includes data such as age, gender, activity level, current weight, target weight, daily schedule, and emotional state. The terminal transmits this information to the server using a secure protocol. The entered data is used as basic input data to generate a personalized plan.

[0397] Step 2:

[0398] The server stores personal information received from the terminal in a database and begins analysis using an emotion engine. This analysis evaluates the user's current health and emotional state based on the collected data. In particular, emotional data obtained from social media and free-text input is quantified through text analysis to determine the user's emotional tendencies. The output of this step is a profile of the user's health and emotional state.

[0399] Step 3:

[0400] The server then uses a generative AI model to develop a personalized diet and exercise plan. The input is the profile obtained in step 2, and the generated plan includes specific suggestions to help the user achieve their goals, such as low-calorie meals and short, manageable exercise routines. The output is a specific diet and exercise schedule.

[0401] Step 4:

[0402] The server generates motivational messages based on the user's emotional state. Using the evaluation results of the emotion engine as input, it creates emotionally resonant messages such as, "Let's take a walk today to clear our heads." These messages are provided to the user as feedback, supporting their daily activities and self-awareness.

[0403] Step 5:

[0404] The terminal displays the plan and messages sent from the server to the user. The user acts according to the presented plan and can also input feedback from the terminal. This feedback is sent back to the server and used to improve future plans.

[0405] Step 6:

[0406] The server receives feedback data from users and reflects it in the database. This allows for the optimization of plans based on the feedback. The input is user feedback data, and the output is an improved next plan and updated sentiment evaluation results.

[0407] (Application Example 2)

[0408] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0409] Traditional health management systems lack sufficient personalized support that reflects the user's emotional state, resulting in difficulties in maintaining user motivation for health and weight management. Furthermore, there is a lack of methods to provide highly accurate messages that respond to emotions, and automated support that can be effectively used within the home is not available. This project aims to solve these problems.

[0410] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0411] In this invention, the server includes means for collecting data to evaluate the user's emotional state on a daily basis, means for generating and providing a personalized health and weight management plan, means for generating and providing encouraging messages according to the user's estimated emotional state, and means for assisting health management by operating home automation equipment. This provides personalized support and encouragement according to the user's emotional state, making effective health management possible even within the home.

[0412] A "user" is an individual who utilizes the system and is the person who receives support for health management and weight management.

[0413] "Emotional state" refers to a user's psychological and emotional state, and is a factor that influences their motivation and mental health.

[0414] "Means of data collection" refers to methods or devices for gathering necessary information from users, and which have the function of acquiring information used to evaluate emotional states.

[0415] A "personalized health and weight management plan" is a diet and fitness plan customized to the user's specific needs and goals.

[0416] An "encouraging message" is a statement designed to boost motivation, generated in response to the user's emotional state, and provides guidance to support individual actions.

[0417] "Household automation equipment" refers to electronic devices and equipment used in the home that can be linked to and operated by systems designed to support health management.

[0418] This invention is implemented as a system including a home-use robot. This system provides various means to support the user's daily health management and weight management. Specifically, it has the function of evaluating the user's emotional state and providing an individualized health management plan based on that.

[0419] The server collects the data necessary to assess the user's emotional state. This uses microphones, cameras, and other sensors typically found in consumer robots. This hardware recognizes the user's speech and physical indicators, and converts them into data. The collected data is sent to the server, where a generative AI model is used to analyze the user's emotional state.

[0420] Based on the analysis results, the server creates a personalized health and weight management plan, which is then delivered to the user by a consumer robot. The plan includes meal suggestions and exercise plans, tailored to the user's current condition and goals. Furthermore, it generates encouraging messages based on the user's emotions and provides feedback. These messages are created using a generative AI model employing natural language processing.

[0421] For example, if a user is feeling stressed, the server might suggest relaxing stretches and provide a message such as, "Take it easy and relax today." A specific example of a prompt might be, "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?"

[0422] This provides continuous and personalized support within the home, enabling users to effectively manage their own health.

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The user enters data about themselves into the device. This includes information about their age, gender, current weight, target weight, activity level, and schedule. The device sends this information to the server. The entered data is then processed as basic data used to create a customized plan.

[0426] Step 2:

[0427] The server receives data sent from the terminal and stores it in a database. To analyze the data, it generates a preliminary health management plan based on each user's activity level, schedule, and target weight. Storing the data in the database allows it to be accumulated as long-term management information.

[0428] Step 3:

[0429] Sensors installed in consumer robots detect data related to the user's emotional state in real time. This data is collected via microphones and cameras, and the device transmits it to a server. This information serves as input data for analyzing the user's psychological state and evaluating their emotions.

[0430] Step 4:

[0431] The server uses a generative AI model to analyze the user's emotional state. It uses information related to emotional state as input data and uses it to determine the user's psychological state. The analysis results are output and serve as a basis for generating personalized advice.

[0432] Step 5:

[0433] The server optimizes health and weight management plans, taking into account the analyzed emotional state. It creates plans that include meal suggestions and recommendations for specific exercises, and communicates them to the user via a home-use robot. The optimized plan is output, and feedback best suited to the user's condition is provided.

[0434] Step 6:

[0435] Using a generative AI model, the system generates encouraging messages tailored to the user's emotional state. The prompt is "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?" The system then outputs a clear and actionable message.

[0436] Step 7:

[0437] To support health management within the home, consumer robots perform automated actions. For example, they might demonstrate suggested stretching exercises and instruct the user on how to perform the actions. This allows the user to immediately carry out the suggested actions.

[0438] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0439] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0440] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0444] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0445] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0446] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0447] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0448] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0449] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0450] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0451] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0452] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0453] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0454] The system for implementing this invention is designed to collect individual user information and automatically generate an optimized health management plan based on that data. The program processing of this system is described below in natural language.

[0455] System Configuration

[0456] The device provides a user interface and displays forms for users to input individual information, such as age, gender, activity level, target weight, and daily schedule.

[0457] The server receives data sent from the terminal and uses machine learning algorithms to generate a personalized plan. The algorithms analyze historical data and existing health management databases to identify the optimal combination of diet, exercise, and supplements.

[0458] Users receive the generated plan via their device, use it in their daily lives, and input their progress into the device. Progress data is continuously collected and sent to the server.

[0459] Specific example

[0460] For example, consider a case where a user uses the system with the following goal in mind.

[0461] Age: 35

[0462] Gender: Female

[0463] Current weight: 70kg

[0464] Target weight: 62kg

[0465] Daily activity level: Moderate

[0466] Schedule: I work Monday through Friday, and my time is limited.

[0467] Once the user enters the above information into their device, the server generates a plan optimized for the user's body type and goals based on the data. The server suggests breakfast, lunch, and dinner menus, calculates calories, and also suggests workouts that fit into the user's daily schedule. Short exercises include "2-minute stretches that can be done in the office."

[0468] Users use these plans as daily guides, entering their progress into their devices. The server evaluates the data in real time and automatically adjusts the plan as needed. These adjustments may include specific changes such as increasing exercise intensity or reviewing calorie intake. Furthermore, the server analyzes the user's psychological state and sends encouraging messages to their devices to help maintain motivation.

[0469] In this way, the system of this invention provides users with a sustainable and personalized health management plan, and helps users efficiently achieve their goals.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The device displays a basic information input form to the user. This form includes information such as age, gender, activity level, current weight and target weight, and daily schedule. The user enters this information into the form and sends it to the device.

[0473] Step 2:

[0474] The device sends individual information obtained from the user to the server.

[0475] Step 3:

[0476] The server uses the received information to compare and analyze it with past health management data and information on similar users in the database. This analysis provides the insights necessary to design a plan that is optimal for the user's physical characteristics and lifestyle.

[0477] Step 4:

[0478] The server generates meal plans, exercise plans, and supplement plans based on the analyzed data. The algorithm calculates appropriate calorie intake and exercise frequency, and also includes suggestions for short, manageable "spare-time workouts."

[0479] Step 5:

[0480] The server sends the generated plan information to the device. The device then visually presents this information to the user through its user interface and sets up notifications for workouts during spare time based on the schedule.

[0481] Step 6:

[0482] Users input their daily progress (e.g., weight changes and exercise status) into their device and provide feedback. The device then sends this information to the server.

[0483] Step 7:

[0484] The server evaluates user progress data in real time and adjusts the plan as needed to help users achieve their goals. These adjustments may include reviewing exercise intensity or modifying meal plans.

[0485] Step 8:

[0486] The server analyzes the user's psychological state and, if it determines that their motivation is low, generates encouraging or reminder messages. The device then notifies the user of these messages to help maintain their motivation.

[0487] (Example 1)

[0488] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] To lead a healthy life, people need individually optimized diet and exercise plans. However, manually creating these plans to suit individual lifestyles is difficult, and maintaining consistent motivation is also a challenge. Furthermore, adjusting plans in real time to match dynamic lifestyles is difficult. Therefore, there is a need to efficiently and automatically generate personalized health management plans that can be adapted to the user's life.

[0490] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0491] In this invention, the server includes means for generating personalized plans including meals, exercise, and nutrition using machine learning technology; means for receiving progress data and automatically adjusting the plan in real time; and means for evaluating the user's psychological state and generating and providing encouraging messages. This makes it possible to efficiently provide optimal health management tailored to each user's needs and lifestyle, as well as maintain motivation.

[0492] "Information-collecting terminal means" refers to a device or interface that provides a device for obtaining specific individual information from a user, and has the function of securely processing and transmitting that information.

[0493] "Means of sending collected information to the server" refers to a combination of communication protocols and software used to securely and efficiently transfer information obtained from users to the server.

[0494] A "server system that generates personalized plans including diet, exercise, and nutrition using machine learning technology" is a server system that analyzes received data and uses machine learning algorithms to create health management plans tailored to individual needs.

[0495] "Means of providing generated plans via terminals" refers to a system that has the function of displaying and providing health management plans created on a server to a terminal so that users can easily access them.

[0496] "A means of receiving progress data and automatically adjusting the plan in real time" refers to a system operation that dynamically updates the plan content according to the user's progress and provides the most appropriate advice quickly.

[0497] "Means of evaluating psychological state and generating and providing encouraging messages" refers to a process for analyzing a user's emotional and motivational tendencies and providing appropriate positive feedback and encouragement as needed.

[0498] "A plan generation method that proposes physical activities that can be done in a short amount of time" refers to a system function that effectively recommends exercises and physical activities that can be done in a short amount of time according to the user's schedule.

[0499] "Information gathering methods that acquire individual time management information and use it to propose plans" refers to elements of a system that understands the user's lifestyle and how they spend their time, and designs an effective health plan based on the collected information.

[0500] This invention is a system that efficiently collects user-specific information and proposes a personalized health management plan based on that information. Users input information such as age, gender, activity level, target weight, and daily schedule via a terminal. The terminal temporarily stores this information and transmits it to a server via the internet.

[0501] The server analyzes the received information and generates a personalized health management plan using machine learning techniques. Specifically, it preprocesses the data using the Python Pandas library and utilizes machine learning algorithms such as TensorFlow and scikit-learn to derive the optimal combination of diet, exercise, and nutrition. The server also assesses the user's psychological state and generates motivational messages using a generative AI model. These messages are sent to the terminal and provided to the user.

[0502] For example, if a 35-year-old woman has a goal of "reaching a target weight of 62kg," the server will propose a plan that takes into account her diet and exercise frequency. This plan might include short exercises like "2-minute stretches at the office." The user then follows this plan daily and reports their progress to the server via their device, allowing the plan to be adjusted in real time.

[0503] An example of a prompt message might be, "35-year-old female user, target weight 62kg, suggest office stretches." This allows users to manage their health in a way that suits their lifestyle and helps maintain motivation towards their goals.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] The terminal provides an interface for users to input individual information. Users enter information such as age, gender, activity level, target weight, and daily schedule. The terminal temporarily stores the input data, verifies the form information, and prepares for the next processing step. The output is user information organized in a communicable data format (e.g., JSON).

[0507] Step 2:

[0508] The terminal sends the organized user data to the server via a secure communication protocol (e.g., HTTPS). The terminal waits for the transmission to complete and displays a success notification to the user. The output is the user information sent to the server.

[0509] Step 3:

[0510] The server analyzes user data received from the terminal. The input data is preprocessed using Python's Pandas library to correct inconsistencies and missing information. The output of the analysis is a clear dataset that can be used as input for machine learning algorithms.

[0511] Step 4:

[0512] The server uses machine learning techniques to generate personalized plans for each user. It employs TensorFlow and scikit-learn algorithms to create meal plans and exercise programs. The output is a personalized health management plan tailored to the user's needs.

[0513] Step 5:

[0514] The server sends the generated plan to the terminal and provides it to the user. The plan is converted to a format that is easy for the terminal to view and displayed in an interface suitable for the user. The output is the health management plan displayed on the terminal.

[0515] Step 6:

[0516] The user performs daily activities according to the plan and enters progress information into the device. The device prepares to send the progress data to the server. The output is data with the progress status properly recorded.

[0517] Step 7:

[0518] The server analyzes the received progress data and automatically adjusts the plan as needed. It processes data in real time and updates the plan using machine learning. The output is the adjusted, up-to-date health management plan.

[0519] Step 8:

[0520] The server uses a generative AI model to assess the user's psychological state and generate messages to maintain motivation. It utilizes prompts to create positive feedback. The output is an encouraging message provided to the user.

[0521] Through these steps, users can consistently implement personalized health management plans and efficiently work towards achieving their goals.

[0522] (Application Example 1)

[0523] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] In today's world, individualized health management and lifestyle improvements are in demand, but many current technologies fail to adequately meet the individual needs of users and lack the support necessary to maintain motivation. A major challenge, in particular, is whether it's possible to support users' health management in a way that seamlessly integrates into their daily lives at home.

[0525] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0526] In this invention, the server includes means for collecting individual information, means for generating personalized meal and exercise plans, and means for adapting the home-use device to the user's lifestyle based on the collected information. This allows the user to continuously manage their health through a natural experience in the home, maintaining motivation and improving their lifestyle.

[0527] "Individualized information" refers to data about a user's specific attributes or status, including age, gender, and activity level.

[0528] A "diet and exercise plan" refers to a proposal that includes a tailored meal plan and exercise program for the user.

[0529] A "generated plan" refers to a diet and exercise plan created based on individual information.

[0530] "Progress data" refers to information regarding the results of the user's plan implementation and changes in their physical condition.

[0531] "Emotional state" refers to the user's psychological and mental condition, and is a factor that the system considers in order to maintain motivation.

[0532] A "household medical device" is a mechanical unit installed in a user's living environment to provide personalized health management support.

[0533] The system that realizes this invention provides an integrated environment for individualized health management within the home. The system includes terminals, a server, and household machinery.

[0534] The server uses machine learning algorithms (such as scikit-learn) to analyze information and generate personalized diet and exercise plans for users. Based on the collected individual information (e.g., age, gender, activity level), the server creates a plan to support the user's health goals.

[0535] The terminal provides a user interface, collects individual information from the user, and displays input forms for tracking generated plans and progress. As the user enters daily progress, the terminal sends new data to the server, helping to optimize the plan.

[0536] Home-use health devices directly engage with users' daily lives, supporting them in tasks such as meal preparation and exercise. This allows users to seamlessly integrate health management into their daily routines.

[0537] For example, in the morning, the device might suggest to the user, "Good morning. You have a little time today, so why not try 15 minutes of yoga?" The server then displays encouraging messages based on the user's emotional state on the terminal or device to help maintain motivation. This system is designed to always provide the user with the optimal situation and naturally support their daily health management.

[0538] An example of a prompt would be, "Please suggest a summer menu for a 35-year-old woman with a moderate activity level and a target weight of 62 kg." Using such prompts allows the generative AI model to create a more precise plan.

[0539] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0540] Step 1:

[0541] The device displays a user interface, and the user enters individual information. This data includes age, gender, activity level, and target weight. The device collects this information and prepares to send it to the server.

[0542] Step 2:

[0543] The server receives individual information sent from the device. Based on this data, it uses machine learning algorithms (such as scikit-learn) to generate personalized meal and exercise plans. The generating AI model creates an optimal plan based on the user's attributes and sends the plan to the device.

[0544] Step 3:

[0545] The device displays a personalized plan received from the server to the user. The user can review the displayed plan and incorporate it into their daily life. The device also provides an input form for tracking progress, allowing the user to enter their daily progress.

[0546] Step 4:

[0547] After the user enters progress data into the device, the device sends it back to the server. The server evaluates the received progress data and adjusts the plan as needed. Specifically, this may involve changing the exercise intensity or modifying the meal plan.

[0548] Step 5:

[0549] The server assesses the user's emotional state and generates appropriate messages to maintain motivation. These messages are sent to the device with the aim of supporting the user's psychological well-being. The device then presents these messages to the user, providing support to help them maintain motivation.

[0550] Step 6:

[0551] Home-use devices implement a plan tailored to the user's lifestyle. For example, they might help prepare breakfast or encourage exercise. The devices harmonize the plan with daily activities, seamlessly supporting the user's health management.

[0552] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0553] This invention is a system for recognizing a user's emotions and optimizing their health management and diet plan. The system collects individual user information, evaluates the user's emotional state using an emotion engine, and provides personalized plans and motivational messages.

[0554] System Configuration

[0555] The device provides a user interface and collects individual information from the user. This includes data on age, gender, activity level, current weight, target weight, daily schedule, and the user's emotions. The user enters this information into the device and sends it to the server.

[0556] The server receives individual information sent from the terminal and analyzes the user's information using a database and an emotion engine. This analysis includes generating meal plans, exercise plans, and supplement plans. The emotion engine also analyzes the user's input data and activity records to determine the user's emotional state.

[0557] Based on the results of this emotion recognition, the server generates motivational messages and readjusts exercise and meal plans as needed. This allows users to receive advice that is best suited to their mental state.

[0558] Specific example

[0559] For example, consider a case where a user uses the system under the following conditions.

[0560] Age: 28

[0561] Gender: Male

[0562] Current weight: 85kg

[0563] Target weight: 75kg

[0564] Daily activity level: Low

[0565] Schedule: Irregular work schedule

[0566] When a user enters information into their device, the server uses that data to suggest a low-calorie, nutritious meal plan. In addition, it includes short workouts that can be done in just five minutes, such as "exercises you can do in your office chair."

[0567] (Specific use of the emotional engine)

[0568] The emotion engine evaluates the user's daily emotional state based on their social media activity, surveys, and intuitive input data. If a user inputs that they feel "tired" on a given day, the server uses this data to generate a message suggesting relaxing stretches, along with a message like, "Today is a day to take it easy and focus on getting your body back in shape," and sends it to the device.

[0569] This series of systems aims to maximize the user's continuous health management and weight loss effectiveness, enabling flexible planning that responds to emotions.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] The device displays basic information and a mood input form to the user. The user enters their age, gender, activity level, current weight, target weight, daily schedule, and current mood state, and sends the data to the device.

[0573] Step 2:

[0574] The terminal sends the acquired user information to the server. The server receives the user's individual data and stores it in a database.

[0575] Step 3:

[0576] Based on the individual information received, the server uses machine learning algorithms to generate meal plans, exercise plans, and supplement plans tailored to the user's physical characteristics and goals. In doing so, it references success stories from similar users using an existing database to design the optimal plan.

[0577] Step 4:

[0578] The server uses an emotion engine to analyze the user's emotional state. Based on the input emotional data and information obtained from social media activity, it quantifies the user's psychological state and generates appropriate motivational messages.

[0579] Step 5:

[0580] The server sends the generated plan and message to the device. The device then presents this information to the user in a visually clear format. It also sets up notifications for workouts that fit the user's schedule and allow for free time workouts.

[0581] Step 6:

[0582] Users perform daily activities based on the provided plan and input their progress and emotional changes into their device. This information is periodically sent from the device to the server.

[0583] Step 7:

[0584] The server receives progress data and emotional changes from the user and re-evaluates the plan in real time. If necessary, it adjusts the content of meals and exercise and generates new messages to maintain the user's motivation.

[0585] Step 8:

[0586] The device will notify the user of the adjusted plan and any new messages, encouraging them to use it as a guide for their next actions.

[0587] (Example 2)

[0588] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0589] In today's world, while there is a wealth of information and options available for personal health management and dieting, finding a personalized approach is difficult. Furthermore, there is a lack of systems that can flexibly adapt to emotional changes, highlighting the need for support that helps users maintain motivation and achieve their health goals.

[0590] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0591] This invention includes a server that includes a method for collecting personal information, a method for generating personalized diet and exercise plans, and a method for evaluating the user's emotional state and providing appropriate messages for motivation. This enables the provision of flexible health plans tailored to an individual's emotional state and the maintenance of effective motivation.

[0592] "Methods of collecting personal information" refer to means of obtaining data such as age, gender, activity level, weight, and schedule from users and storing it in a format that can be analyzed within the system.

[0593] "Methods for generating personalized diet and exercise plans" refer to means of formulating nutritional intake and exercise plans optimized for a specific user's health condition and goals, based on collected personal data.

[0594] "Methods for providing generated plans" refer to means of visually or audibly presenting the formulated individual meal and exercise plans to the user.

[0595] "A method of receiving progress data and adjusting plans" refers to a means of evaluating current progress based on user feedback and regular data entry, and improving or modifying existing plans as needed.

[0596] "A method for evaluating the emotional state of users and providing appropriate messages for motivation" refers to a means of analyzing text input and activity records to understand the user's emotions and generate encouraging or cautionary messages accordingly.

[0597] "A method for analyzing natural language input to measure daily emotions" is a means of quantifying a user's mood and emotional trends by analyzing free-form emotional words entered by the user.

[0598] This invention is a system for providing users with personalized health management plans. Its main components include a terminal, a server, and multiple software components.

[0599] The terminal provides the user interface and collects input data from users. Each user enters information such as age, gender, activity level, weight, and individual schedule information into the terminal. This information is transmitted to the server using a secure protocol.

[0600] The server stores the received information in a database and performs personalized analysis for each user. This analysis uses a software component called an emotion engine, which evaluates the user's daily emotional state based on social media activity and free-text input data. Based on this evaluation, personalized diet and exercise plans are developed. The server also uses a generative AI model to generate motivational messages that respond to daily emotional changes.

[0601] For example, if a user prefers a low-carbohydrate diet, the server will suggest a meal plan tailored to that. Furthermore, it can generate motivational messages such as, "Let's try exercising today to feel positive."

[0602] When users provide feedback on their experiences and results via their devices, the server uses that information to further optimize the plan. This feedback loop allows users to continuously receive optimized advice and be supported in achieving their health management goals.

[0603] An example of a prompt is, "Create an optimal health plan based on emotions and health data." Upon entering this prompt, the generating AI model performs the necessary analysis and proposes a personalized health plan.

[0604] This system supports individual health improvement by enabling flexible planning that takes into account the user's daily health condition and helping to maintain motivation.

[0605] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0606] Step 1:

[0607] Users enter personal information through the terminal's interface. This input includes data such as age, gender, activity level, current weight, target weight, daily schedule, and emotional state. The terminal transmits this information to the server using a secure protocol. The entered data is used as basic input data to generate a personalized plan.

[0608] Step 2:

[0609] The server stores personal information received from the terminal in a database and begins analysis using an emotion engine. This analysis evaluates the user's current health and emotional state based on the collected data. In particular, emotional data obtained from social media and free-text input is quantified through text analysis to determine the user's emotional tendencies. The output of this step is a profile of the user's health and emotional state.

[0610] Step 3:

[0611] The server then uses a generative AI model to develop a personalized diet and exercise plan. The input is the profile obtained in step 2, and the generated plan includes specific suggestions to help the user achieve their goals, such as low-calorie meals and short, manageable exercise routines. The output is a specific diet and exercise schedule.

[0612] Step 4:

[0613] The server generates motivational messages based on the user's emotional state. Using the evaluation results of the emotion engine as input, it creates emotionally resonant messages such as, "Let's take a walk today to clear our heads." These messages are provided to the user as feedback, supporting their daily activities and self-awareness.

[0614] Step 5:

[0615] The terminal displays the plan and messages sent from the server to the user. The user acts according to the presented plan and can also input feedback from the terminal. This feedback is sent back to the server and used to improve future plans.

[0616] Step 6:

[0617] The server receives feedback data from users and reflects it in the database. This allows for the optimization of plans based on the feedback. The input is user feedback data, and the output is an improved next plan and updated sentiment evaluation results.

[0618] (Application Example 2)

[0619] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0620] Traditional health management systems lack sufficient personalized support that reflects the user's emotional state, resulting in difficulties in maintaining user motivation for health and weight management. Furthermore, there is a lack of methods to provide highly accurate messages that respond to emotions, and automated support that can be effectively used within the home is not available. This project aims to solve these problems.

[0621] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0622] In this invention, the server includes means for collecting data to evaluate the user's emotional state on a daily basis, means for generating and providing a personalized health and weight management plan, means for generating and providing encouraging messages according to the user's estimated emotional state, and means for assisting health management by operating home automation equipment. This provides personalized support and encouragement according to the user's emotional state, making effective health management possible even within the home.

[0623] A "user" is an individual who utilizes the system and is the person who receives support for health management and weight management.

[0624] "Emotional state" refers to a user's psychological and emotional state, and is a factor that influences their motivation and mental health.

[0625] "Means of data collection" refers to methods or devices for gathering necessary information from users, and which have the function of acquiring information used to evaluate emotional states.

[0626] A "personalized health and weight management plan" is a diet and fitness plan customized to the user's specific needs and goals.

[0627] An "encouraging message" is a statement designed to boost motivation, generated in response to the user's emotional state, and provides guidance to support individual actions.

[0628] "Household automation equipment" refers to electronic devices and equipment used in the home that can be linked to and operated by systems designed to support health management.

[0629] This invention is implemented as a system including a home-use robot. This system provides various means to support the user's daily health management and weight management. Specifically, it has the function of evaluating the user's emotional state and providing an individualized health management plan based on that.

[0630] The server collects the data necessary to assess the user's emotional state. This uses microphones, cameras, and other sensors typically found in consumer robots. This hardware recognizes the user's speech and physical indicators, and converts them into data. The collected data is sent to the server, where a generative AI model is used to analyze the user's emotional state.

[0631] Based on the analysis results, the server creates a personalized health and weight management plan, which is then delivered to the user by a consumer robot. The plan includes meal suggestions and exercise plans, tailored to the user's current condition and goals. Furthermore, it generates encouraging messages based on the user's emotions and provides feedback. These messages are created using a generative AI model employing natural language processing.

[0632] For example, if a user is feeling stressed, the server might suggest relaxing stretches and provide a message such as, "Take it easy and relax today." A specific example of a prompt might be, "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?"

[0633] This provides continuous and personalized support within the home, enabling users to effectively manage their own health.

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The user enters data about themselves into the device. This includes information about their age, gender, current weight, target weight, activity level, and schedule. The device sends this information to the server. The entered data is then processed as basic data used to create a customized plan.

[0637] Step 2:

[0638] The server receives data sent from the terminal and stores it in a database. To analyze the data, it generates a preliminary health management plan based on each user's activity level, schedule, and target weight. Storing the data in the database allows it to be accumulated as long-term management information.

[0639] Step 3:

[0640] Sensors installed in consumer robots detect data related to the user's emotional state in real time. This data is collected via microphones and cameras, and the device transmits it to a server. This information serves as input data for analyzing the user's psychological state and evaluating their emotions.

[0641] Step 4:

[0642] The server uses a generative AI model to analyze the user's emotional state. It uses information related to emotional state as input data and uses it to determine the user's psychological state. The analysis results are output and serve as a basis for generating personalized advice.

[0643] Step 5:

[0644] The server optimizes health and weight management plans, taking into account the analyzed emotional state. It creates plans that include meal suggestions and recommendations for specific exercises, and communicates them to the user via a home-use robot. The optimized plan is output, and feedback best suited to the user's condition is provided.

[0645] Step 6:

[0646] Using a generative AI model, the system generates encouraging messages tailored to the user's emotional state. The prompt is "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?" The system then outputs a clear and actionable message.

[0647] Step 7:

[0648] To support health management within the home, consumer robots perform automated actions. For example, they might demonstrate suggested stretching exercises and instruct the user on how to perform the actions. This allows the user to immediately carry out the suggested actions.

[0649] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0650] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0651] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0652] [Fourth Embodiment]

[0653] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0654] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0655] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0656] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0657] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0658] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0659] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0660] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0661] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0662] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0663] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0664] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0665] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] The system for implementing this invention is designed to collect individual user information and automatically generate an optimized health management plan based on that data. The program processing of this system is described below in natural language.

[0667] System Configuration

[0668] The device provides a user interface and displays forms for users to input individual information, such as age, gender, activity level, target weight, and daily schedule.

[0669] The server receives data sent from the terminal and uses machine learning algorithms to generate a personalized plan. The algorithms analyze historical data and existing health management databases to identify the optimal combination of diet, exercise, and supplements.

[0670] Users receive the generated plan via their device, use it in their daily lives, and input their progress into the device. Progress data is continuously collected and sent to the server.

[0671] Specific example

[0672] For example, consider a case where a user uses the system with the following goal in mind.

[0673] Age: 35

[0674] Gender: Female

[0675] Current weight: 70kg

[0676] Target weight: 62kg

[0677] Daily activity level: Moderate

[0678] Schedule: I work Monday through Friday, and my time is limited.

[0679] Once the user enters the above information into their device, the server generates a plan optimized for the user's body type and goals based on the data. The server suggests breakfast, lunch, and dinner menus, calculates calories, and also suggests workouts that fit into the user's daily schedule. Short exercises include "2-minute stretches that can be done in the office."

[0680] Users use these plans as daily guides, entering their progress into their devices. The server evaluates the data in real time and automatically adjusts the plan as needed. These adjustments may include specific changes such as increasing exercise intensity or reviewing calorie intake. Furthermore, the server analyzes the user's psychological state and sends encouraging messages to their devices to help maintain motivation.

[0681] In this way, the system of this invention provides users with a sustainable and personalized health management plan, and helps users efficiently achieve their goals.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The device displays a basic information input form to the user. This form includes information such as age, gender, activity level, current weight and target weight, and daily schedule. The user enters this information into the form and sends it to the device.

[0685] Step 2:

[0686] The device sends individual information obtained from the user to the server.

[0687] Step 3:

[0688] The server uses the received information to compare and analyze it with past health management data and information on similar users in the database. This analysis provides the insights necessary to design a plan that is optimal for the user's physical characteristics and lifestyle.

[0689] Step 4:

[0690] The server generates meal plans, exercise plans, and supplement plans based on the analyzed data. The algorithm calculates appropriate calorie intake and exercise frequency, and also includes suggestions for short, manageable "spare-time workouts."

[0691] Step 5:

[0692] The server sends the generated plan information to the device. The device then visually presents this information to the user through its user interface and sets up notifications for workouts during spare time based on the schedule.

[0693] Step 6:

[0694] Users input their daily progress (e.g., weight changes and exercise status) into their device and provide feedback. The device then sends this information to the server.

[0695] Step 7:

[0696] The server evaluates user progress data in real time and adjusts the plan as needed to help users achieve their goals. These adjustments may include reviewing exercise intensity or modifying meal plans.

[0697] Step 8:

[0698] The server analyzes the user's psychological state and, if it determines that their motivation is low, generates encouraging or reminder messages. The device then notifies the user of these messages to help maintain their motivation.

[0699] (Example 1)

[0700] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] To lead a healthy life, people need individually optimized diet and exercise plans. However, manually creating these plans to suit individual lifestyles is difficult, and maintaining consistent motivation is also a challenge. Furthermore, adjusting plans in real time to match dynamic lifestyles is difficult. Therefore, there is a need to efficiently and automatically generate personalized health management plans that can be adapted to the user's life.

[0702] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0703] In this invention, the server includes means for generating personalized plans including meals, exercise, and nutrition using machine learning technology; means for receiving progress data and automatically adjusting the plan in real time; and means for evaluating the user's psychological state and generating and providing encouraging messages. This makes it possible to efficiently provide optimal health management tailored to each user's needs and lifestyle, as well as maintain motivation.

[0704] "Information-collecting terminal means" refers to a device or interface that provides a device for obtaining specific individual information from a user, and has the function of securely processing and transmitting that information.

[0705] "Means of sending collected information to the server" refers to a combination of communication protocols and software used to securely and efficiently transfer information obtained from users to the server.

[0706] A "server system that generates personalized plans including diet, exercise, and nutrition using machine learning technology" is a server system that analyzes received data and uses machine learning algorithms to create health management plans tailored to individual needs.

[0707] "Means of providing generated plans via terminals" refers to a system that has the function of displaying and providing health management plans created on a server to a terminal so that users can easily access them.

[0708] "A means of receiving progress data and automatically adjusting the plan in real time" refers to a system operation that dynamically updates the plan content according to the user's progress and provides the most appropriate advice quickly.

[0709] "Means of evaluating psychological state and generating and providing encouraging messages" refers to a process for analyzing a user's emotional and motivational tendencies and providing appropriate positive feedback and encouragement as needed.

[0710] "A plan generation method that proposes physical activities that can be done in a short amount of time" refers to a system function that effectively recommends exercises and physical activities that can be done in a short amount of time according to the user's schedule.

[0711] "Information gathering methods that acquire individual time management information and use it to propose plans" refers to elements of a system that understands the user's lifestyle and how they spend their time, and designs an effective health plan based on the collected information.

[0712] This invention is a system that efficiently collects user-specific information and proposes a personalized health management plan based on that information. Users input information such as age, gender, activity level, target weight, and daily schedule via a terminal. The terminal temporarily stores this information and transmits it to a server via the internet.

[0713] The server analyzes the received information and generates a personalized health management plan using machine learning techniques. Specifically, it preprocesses the data using the Python Pandas library and utilizes machine learning algorithms such as TensorFlow and scikit-learn to derive the optimal combination of diet, exercise, and nutrition. The server also assesses the user's psychological state and generates motivational messages using a generative AI model. These messages are sent to the terminal and provided to the user.

[0714] For example, if a 35-year-old woman has a goal of "reaching a target weight of 62kg," the server will propose a plan that takes into account her diet and exercise frequency. This plan might include short exercises like "2-minute stretches at the office." The user then follows this plan daily and reports their progress to the server via their device, allowing the plan to be adjusted in real time.

[0715] An example of a prompt message might be, "35-year-old female user, target weight 62kg, suggest office stretches." This allows users to manage their health in a way that suits their lifestyle and helps maintain motivation towards their goals.

[0716] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0717] Step 1:

[0718] The terminal provides an interface for users to input individual information. Users enter information such as age, gender, activity level, target weight, and daily schedule. The terminal temporarily stores the input data, verifies the form information, and prepares for the next processing step. The output is user information organized in a communicable data format (e.g., JSON).

[0719] Step 2:

[0720] The terminal sends the organized user data to the server via a secure communication protocol (e.g., HTTPS). The terminal waits for the transmission to complete and displays a success notification to the user. The output is the user information sent to the server.

[0721] Step 3:

[0722] The server analyzes user data received from the terminal. The input data is preprocessed using Python's Pandas library to correct inconsistencies and missing information. The output of the analysis is a clear dataset that can be used as input for machine learning algorithms.

[0723] Step 4:

[0724] The server uses machine learning techniques to generate personalized plans for each user. It employs TensorFlow and scikit-learn algorithms to create meal plans and exercise programs. The output is a personalized health management plan tailored to the user's needs.

[0725] Step 5:

[0726] The server sends the generated plan to the terminal and provides it to the user. The plan is converted to a format that is easy for the terminal to view and displayed in an interface suitable for the user. The output is the health management plan displayed on the terminal.

[0727] Step 6:

[0728] The user performs daily activities according to the plan and enters progress information into the device. The device prepares to send the progress data to the server. The output is data with the progress status properly recorded.

[0729] Step 7:

[0730] The server analyzes the received progress data and automatically adjusts the plan as needed. It processes data in real time and updates the plan using machine learning. The output is the adjusted, up-to-date health management plan.

[0731] Step 8:

[0732] The server uses a generative AI model to assess the user's psychological state and generate messages to maintain motivation. It utilizes prompts to create positive feedback. The output is an encouraging message provided to the user.

[0733] Through these steps, users can consistently implement personalized health management plans and efficiently work towards achieving their goals.

[0734] (Application Example 1)

[0735] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In today's world, individualized health management and lifestyle improvements are in demand, but many current technologies fail to adequately meet the individual needs of users and lack the support necessary to maintain motivation. A major challenge, in particular, is whether it's possible to support users' health management in a way that seamlessly integrates into their daily lives at home.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0738] In this invention, the server includes means for collecting individual information, means for generating personalized meal and exercise plans, and means for adapting the home-use device to the user's lifestyle based on the collected information. This allows the user to continuously manage their health through a natural experience in the home, maintaining motivation and improving their lifestyle.

[0739] "Individualized information" refers to data about a user's specific attributes or status, including age, gender, and activity level.

[0740] A "diet and exercise plan" refers to a proposal that includes a tailored meal plan and exercise program for the user.

[0741] A "generated plan" refers to a diet and exercise plan created based on individual information.

[0742] "Progress data" refers to information regarding the results of the user's plan implementation and changes in their physical condition.

[0743] "Emotional state" refers to the user's psychological and mental condition, and is a factor that the system considers in order to maintain motivation.

[0744] A "household medical device" is a mechanical unit installed in a user's living environment to provide personalized health management support.

[0745] The system that realizes this invention provides an integrated environment for individualized health management within the home. The system includes terminals, a server, and household machinery.

[0746] The server uses machine learning algorithms (such as scikit-learn) to analyze information and generate personalized diet and exercise plans for users. Based on the collected individual information (e.g., age, gender, activity level), the server creates a plan to support the user's health goals.

[0747] The terminal provides a user interface, collects individual information from the user, and displays input forms for tracking generated plans and progress. As the user enters daily progress, the terminal sends new data to the server, helping to optimize the plan.

[0748] Home-use health devices directly engage with users' daily lives, supporting them in tasks such as meal preparation and exercise. This allows users to seamlessly integrate health management into their daily routines.

[0749] For example, in the morning, the device might suggest to the user, "Good morning. You have a little time today, so why not try 15 minutes of yoga?" The server then displays encouraging messages based on the user's emotional state on the terminal or device to help maintain motivation. This system is designed to always provide the user with the optimal situation and naturally support their daily health management.

[0750] An example of a prompt would be, "Please suggest a summer menu for a 35-year-old woman with a moderate activity level and a target weight of 62 kg." Using such prompts allows the generative AI model to create a more precise plan.

[0751] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0752] Step 1:

[0753] The device displays a user interface, and the user enters individual information. This data includes age, gender, activity level, and target weight. The device collects this information and prepares to send it to the server.

[0754] Step 2:

[0755] The server receives individual information sent from the device. Based on this data, it uses machine learning algorithms (such as scikit-learn) to generate personalized meal and exercise plans. The generating AI model creates an optimal plan based on the user's attributes and sends the plan to the device.

[0756] Step 3:

[0757] The device displays a personalized plan received from the server to the user. The user can review the displayed plan and incorporate it into their daily life. The device also provides an input form for tracking progress, allowing the user to enter their daily progress.

[0758] Step 4:

[0759] After the user enters progress data into the device, the device sends it back to the server. The server evaluates the received progress data and adjusts the plan as needed. Specifically, this may involve changing the exercise intensity or modifying the meal plan.

[0760] Step 5:

[0761] The server assesses the user's emotional state and generates appropriate messages to maintain motivation. These messages are sent to the device with the aim of supporting the user's psychological well-being. The device then presents these messages to the user, providing support to help them maintain motivation.

[0762] Step 6:

[0763] Home-use devices implement a plan tailored to the user's lifestyle. For example, they might help prepare breakfast or encourage exercise. The devices harmonize the plan with daily activities, seamlessly supporting the user's health management.

[0764] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0765] This invention is a system for recognizing a user's emotions and optimizing their health management and diet plan. The system collects individual user information, evaluates the user's emotional state using an emotion engine, and provides personalized plans and motivational messages.

[0766] System Configuration

[0767] The device provides a user interface and collects individual information from the user. This includes data on age, gender, activity level, current weight, target weight, daily schedule, and the user's emotions. The user enters this information into the device and sends it to the server.

[0768] The server receives individual information sent from the terminal and analyzes the user's information using a database and an emotion engine. This analysis includes generating meal plans, exercise plans, and supplement plans. The emotion engine also analyzes the user's input data and activity records to determine the user's emotional state.

[0769] Based on the results of this emotion recognition, the server generates motivational messages and readjusts exercise and meal plans as needed. This allows users to receive advice that is best suited to their mental state.

[0770] Specific example

[0771] For example, consider a case where a user uses the system under the following conditions.

[0772] Age: 28

[0773] Gender: Male

[0774] Current weight: 85kg

[0775] Target weight: 75kg

[0776] Daily activity level: Low

[0777] Schedule: Irregular work schedule

[0778] When a user enters information into their device, the server uses that data to suggest a low-calorie, nutritious meal plan. In addition, it includes short workouts that can be done in just five minutes, such as "exercises you can do in your office chair."

[0779] (Specific use of the emotional engine)

[0780] The emotion engine evaluates the user's daily emotional state based on their social media activity, surveys, and intuitive input data. If a user inputs that they feel "tired" on a given day, the server uses this data to generate a message suggesting relaxing stretches, along with a message like, "Today is a day to take it easy and focus on getting your body back in shape," and sends it to the device.

[0781] This series of systems aims to maximize the user's continuous health management and weight loss effectiveness, enabling flexible planning that responds to emotions.

[0782] The following describes the processing flow.

[0783] Step 1:

[0784] The device displays basic information and a mood input form to the user. The user enters their age, gender, activity level, current weight, target weight, daily schedule, and current mood state, and sends the data to the device.

[0785] Step 2:

[0786] The terminal sends the acquired user information to the server. The server receives the user's individual data and stores it in a database.

[0787] Step 3:

[0788] Based on the individual information received, the server uses machine learning algorithms to generate meal plans, exercise plans, and supplement plans tailored to the user's physical characteristics and goals. In doing so, it references success stories from similar users using an existing database to design the optimal plan.

[0789] Step 4:

[0790] The server uses an emotion engine to analyze the user's emotional state. Based on the input emotional data and information obtained from social media activity, it quantifies the user's psychological state and generates appropriate motivational messages.

[0791] Step 5:

[0792] The server sends the generated plan and message to the device. The device then presents this information to the user in a visually clear format. It also sets up notifications for workouts that fit the user's schedule and allow for free time workouts.

[0793] Step 6:

[0794] Users perform daily activities based on the provided plan and input their progress and emotional changes into their device. This information is periodically sent from the device to the server.

[0795] Step 7:

[0796] The server receives progress data and emotional changes from the user and re-evaluates the plan in real time. If necessary, it adjusts the content of meals and exercise and generates new messages to maintain the user's motivation.

[0797] Step 8:

[0798] The device will notify the user of the adjusted plan and any new messages, encouraging them to use it as a guide for their next actions.

[0799] (Example 2)

[0800] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0801] In today's world, while there is a wealth of information and options available for personal health management and dieting, finding a personalized approach is difficult. Furthermore, there is a lack of systems that can flexibly adapt to emotional changes, highlighting the need for support that helps users maintain motivation and achieve their health goals.

[0802] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0803] This invention includes a server that includes a method for collecting personal information, a method for generating personalized diet and exercise plans, and a method for evaluating the user's emotional state and providing appropriate messages for motivation. This enables the provision of flexible health plans tailored to an individual's emotional state and the maintenance of effective motivation.

[0804] "Methods of collecting personal information" refer to means of obtaining data such as age, gender, activity level, weight, and schedule from users and storing it in a format that can be analyzed within the system.

[0805] "Methods for generating personalized diet and exercise plans" refer to means of formulating nutritional intake and exercise plans optimized for a specific user's health condition and goals, based on collected personal data.

[0806] "Methods for providing generated plans" refer to means of visually or audibly presenting the formulated individual meal and exercise plans to the user.

[0807] "A method of receiving progress data and adjusting plans" refers to a means of evaluating current progress based on user feedback and regular data entry, and improving or modifying existing plans as needed.

[0808] "A method for evaluating the emotional state of users and providing appropriate messages for motivation" refers to a means of analyzing text input and activity records to understand the user's emotions and generate encouraging or cautionary messages accordingly.

[0809] "A method for analyzing natural language input to measure daily emotions" is a means of quantifying a user's mood and emotional trends by analyzing free-form emotional words entered by the user.

[0810] This invention is a system for providing users with personalized health management plans. Its main components include a terminal, a server, and multiple software components.

[0811] The terminal provides the user interface and collects input data from users. Each user enters information such as age, gender, activity level, weight, and individual schedule information into the terminal. This information is transmitted to the server using a secure protocol.

[0812] The server stores the received information in a database and performs personalized analysis for each user. This analysis uses a software component called an emotion engine, which evaluates the user's daily emotional state based on social media activity and free-text input data. Based on this evaluation, personalized diet and exercise plans are developed. The server also uses a generative AI model to generate motivational messages that respond to daily emotional changes.

[0813] For example, if a user prefers a low-carbohydrate diet, the server will suggest a meal plan tailored to that. Furthermore, it can generate motivational messages such as, "Let's try exercising today to feel positive."

[0814] When users provide feedback on their experiences and results via their devices, the server uses that information to further optimize the plan. This feedback loop allows users to continuously receive optimized advice and be supported in achieving their health management goals.

[0815] An example of a prompt is, "Create an optimal health plan based on emotions and health data." Upon entering this prompt, the generating AI model performs the necessary analysis and proposes a personalized health plan.

[0816] This system supports individual health improvement by enabling flexible planning that takes into account the user's daily health condition and helping to maintain motivation.

[0817] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0818] Step 1:

[0819] Users enter personal information through the terminal's interface. This input includes data such as age, gender, activity level, current weight, target weight, daily schedule, and emotional state. The terminal transmits this information to the server using a secure protocol. The entered data is used as basic input data to generate a personalized plan.

[0820] Step 2:

[0821] The server stores personal information received from the terminal in a database and begins analysis using an emotion engine. This analysis evaluates the user's current health and emotional state based on the collected data. In particular, emotional data obtained from social media and free-text input is quantified through text analysis to determine the user's emotional tendencies. The output of this step is a profile of the user's health and emotional state.

[0822] Step 3:

[0823] The server then uses a generative AI model to develop a personalized diet and exercise plan. The input is the profile obtained in step 2, and the generated plan includes specific suggestions to help the user achieve their goals, such as low-calorie meals and short, manageable exercise routines. The output is a specific diet and exercise schedule.

[0824] Step 4:

[0825] The server generates motivational messages based on the user's emotional state. Using the evaluation results of the emotion engine as input, it creates emotionally resonant messages such as, "Let's take a walk today to clear our heads." These messages are provided to the user as feedback, supporting their daily activities and self-awareness.

[0826] Step 5:

[0827] The terminal displays the plan and messages sent from the server to the user. The user acts according to the presented plan and can also input feedback from the terminal. This feedback is sent back to the server and used to improve future plans.

[0828] Step 6:

[0829] The server receives feedback data from users and reflects it in the database. This allows for the optimization of plans based on the feedback. The input is user feedback data, and the output is an improved next plan and updated sentiment evaluation results.

[0830] (Application Example 2)

[0831] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0832] Traditional health management systems lack sufficient personalized support that reflects the user's emotional state, resulting in difficulties in maintaining user motivation for health and weight management. Furthermore, there is a lack of methods to provide highly accurate messages that respond to emotions, and automated support that can be effectively used within the home is not available. This project aims to solve these problems.

[0833] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0834] In this invention, the server includes means for collecting data to evaluate the user's emotional state on a daily basis, means for generating and providing a personalized health and weight management plan, means for generating and providing encouraging messages according to the user's estimated emotional state, and means for assisting health management by operating home automation equipment. This provides personalized support and encouragement according to the user's emotional state, making effective health management possible even within the home.

[0835] A "user" is an individual who utilizes the system and is the person who receives support for health management and weight management.

[0836] "Emotional state" refers to a user's psychological and emotional state, and is a factor that influences their motivation and mental health.

[0837] "Means of data collection" refers to methods or devices for gathering necessary information from users, and which have the function of acquiring information used to evaluate emotional states.

[0838] A "personalized health and weight management plan" is a diet and fitness plan customized to the user's specific needs and goals.

[0839] An "encouraging message" is a statement designed to boost motivation, generated in response to the user's emotional state, and provides guidance to support individual actions.

[0840] "Household automation equipment" refers to electronic devices and equipment used in the home that can be linked to and operated by systems designed to support health management.

[0841] This invention is implemented as a system including a home-use robot. This system provides various means to support the user's daily health management and weight management. Specifically, it has the function of evaluating the user's emotional state and providing an individualized health management plan based on that.

[0842] The server collects the data necessary to assess the user's emotional state. This uses microphones, cameras, and other sensors typically found in consumer robots. This hardware recognizes the user's speech and physical indicators, and converts them into data. The collected data is sent to the server, where a generative AI model is used to analyze the user's emotional state.

[0843] Based on the analysis results, the server creates a personalized health and weight management plan, which is then delivered to the user by a consumer robot. The plan includes meal suggestions and exercise plans, tailored to the user's current condition and goals. Furthermore, it generates encouraging messages based on the user's emotions and provides feedback. These messages are created using a generative AI model employing natural language processing.

[0844] For example, if a user is feeling stressed, the server might suggest relaxing stretches and provide a message such as, "Take it easy and relax today." A specific example of a prompt might be, "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?"

[0845] This provides continuous and personalized support within the home, enabling users to effectively manage their own health.

[0846] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0847] Step 1:

[0848] The user enters data about themselves into the device. This includes information about their age, gender, current weight, target weight, activity level, and schedule. The device sends this information to the server. The entered data is then processed as basic data used to create a customized plan.

[0849] Step 2:

[0850] The server receives data sent from the terminal and stores it in a database. To analyze the data, it generates a preliminary health management plan based on each user's activity level, schedule, and target weight. Storing the data in the database allows it to be accumulated as long-term management information.

[0851] Step 3:

[0852] Sensors installed in consumer robots detect data related to the user's emotional state in real time. This data is collected via microphones and cameras, and the device transmits it to a server. This information serves as input data for analyzing the user's psychological state and evaluating their emotions.

[0853] Step 4:

[0854] The server uses a generative AI model to analyze the user's emotional state. It uses information related to emotional state as input data and uses it to determine the user's psychological state. The analysis results are output and serve as a basis for generating personalized advice.

[0855] Step 5:

[0856] The server optimizes health and weight management plans, taking into account the analyzed emotional state. It creates plans that include meal suggestions and recommendations for specific exercises, and communicates them to the user via a home-use robot. The optimized plan is output, and feedback best suited to the user's condition is provided.

[0857] Step 6:

[0858] Using a generative AI model, the system generates encouraging messages tailored to the user's emotional state. The prompt is "I'm a 28-year-old male, currently weighing 85kg, and aiming to reduce my weight to 75kg. I'm feeling tired today. What advice would you give me?" The system then outputs a clear and actionable message.

[0859] Step 7:

[0860] To support health management within the home, consumer robots perform automated actions. For example, they might demonstrate suggested stretching exercises and instruct the user on how to perform the actions. This allows the user to immediately carry out the suggested actions.

[0861] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0862] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0863] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0864] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0865] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0866] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0867] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0868] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0869] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0870] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0871] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0872] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0873] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0874] 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.

[0875] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0876] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0877] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0878] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0879] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0880] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0881] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0882] The following is further disclosed regarding the embodiments described above.

[0883] (Claim 1)

[0884] Means of collecting individual information,

[0885] Means for generating an individualized meal and exercise plan based on the aforementioned information,

[0886] Means for providing the generated plan,

[0887] A means of receiving progress data and adjusting the plan,

[0888] A means of evaluating emotional state and providing appropriate messages to maintain motivation,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, wherein the plan generation means includes suggesting exercises that can be performed in a short amount of time.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the information gathering means acquires individual schedule information and uses it for plant proposals.

[0894] "Example 1"

[0895] (Claim 1)

[0896] A terminal means for collecting information,

[0897] A means of sending the collected information to the server,

[0898] A server that generates personalized plans including meals, exercise, and nutrition using machine learning technology,

[0899] A means of providing the generated plan through the terminal,

[0900] A means of receiving progress data and automatically adjusting the plan in real time,

[0901] A means of evaluating psychological state, generating and providing encouraging messages,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, comprising a means for generating a plan that proposes physical activities that can be performed in a short amount of time.

[0905] (Claim 3)

[0906] The system according to claim 1, comprising information gathering means for acquiring individual time management information and using it to propose plans.

[0907] "Application Example 1"

[0908] (Claim 1)

[0909] Means of collecting individual information,

[0910] Means for generating an individualized meal and exercise plan based on the aforementioned information,

[0911] Means for providing the generated plan,

[0912] A means of receiving progress data and adjusting the plan,

[0913] A means of evaluating emotional state and providing appropriate messages to maintain motivation,

[0914] The collected information provides a means for household appliances to adapt to the user's lifestyle,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, wherein the plan generation means includes suggesting exercises that can be performed in a short amount of time.

[0918] (Claim 3)

[0919] The system according to claim 1, wherein the information gathering means acquires individual schedule information and uses it for planning proposals.

[0920] "Example 2 of combining an emotion engine"

[0921] (Claim 1)

[0922] Methods of collecting personal information,

[0923] A method for generating a personalized diet and exercise plan based on the aforementioned information,

[0924] A method for providing the generated plan,

[0925] Methods for receiving progress data and adjusting the plan,

[0926] A method for assessing the emotional state of users and providing appropriate messages for motivation,

[0927] Methods for analyzing natural language input to measure daily emotions,

[0928] A device that includes this.

[0929] (Claim 2)

[0930] The apparatus according to claim 1, wherein the plan generation method proposes an exercise that can be achieved in a short period of time.

[0931] (Claim 3)

[0932] The apparatus according to claim 1, wherein the information gathering method obtains an individual's schedule and uses it to present a plan.

[0933] "Application example 2 when combining with an emotional engine"

[0934] (Claim 1)

[0935] A means of collecting data to evaluate the emotional state of users on a daily basis,

[0936] A means for generating and providing personalized health and weight management plans based on the aforementioned data,

[0937] A means of providing exercise and nutritional suggestions based on an individualized plan,

[0938] A means of generating and providing encouraging messages according to the user's estimated emotional state,

[0939] A means of assisting users in managing their health through the operation of home automation equipment,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, characterized in that the exercise and nutritional suggestions are carried out by automated equipment installed in the home.

[0943] (Claim 3)

[0944] The system according to claim 1, characterized in that a message corresponding to the estimated emotional state of the user is generated by natural language processing using a generative AI model. [Explanation of Symbols]

[0945] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting individual information, Means for generating an individualized meal and exercise plan based on the aforementioned information, Means for providing the generated plan, A means of receiving progress data and adjusting the plan, A means of assessing emotional state and providing appropriate messages to maintain motivation, The collected information provides a means for household appliances to adapt to the user's lifestyle, A system that includes this.

2. The system according to claim 1, wherein the plan generation means includes suggesting exercises that can be performed in a short amount of time.

3. The system according to claim 1, wherein the information gathering means acquires individual schedule information and uses it for planning proposals.

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