system

Abstract

Provide a system. 【Solution means】 Means for inputting the physical characteristic information of the user, Means for analyzing the physical characteristic information of the user, Means for comparing the past success cases with the physical characteristic information of the user and identifying similar cases, Means for generating an optimal weight loss plan based on the similar cases, Means for presenting the weight loss plan to the user, Means for recommending and promoting the purchase of products related to the weight loss plan, Means for updating the database and performing learning based on the implementation results of the weight loss plan, Automated mechanical means for providing health support in the user's living environment, Means for proposing appropriate food ingredients and exercise equipment based on the health support, A system including the above.

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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, many people are struggling to find the optimal weight loss method for themselves. The reasons include that it is difficult to obtain effective results with a general diet program due to the different physical characteristics and living habits of each individual, and there is also a problem that it is difficult to judge which plan to choose because of the excessive information. Furthermore, there is a lack of support for selecting appropriate products and using them effectively. For these problems, there is a demand for a system that provides an effective and individually optimized weight loss method and supports the selection of appropriate related products.

Means for Solving the Problems

[0005] This invention provides a system that, based on physical characteristic information entered by the user, identifies similar cases from a database of numerous success stories and generates a statistically optimal weight loss plan. The system presents the user with a specific weight loss plan and also recommends products to support its implementation. Furthermore, it updates the database based on feedback from the implementation results and continuously improves it to provide suggestions based on the latest and most effective information. In addition, it includes functions for generating exercise programs tailored to the user and recommending products that take into account preferences and health conditions. In this way, it supports the user's weight loss success through personalized support.

[0006] A "user" refers to an individual who uses the system to input their physical characteristics information and receive a weight loss plan suggestion.

[0007] "Physical characteristics information" refers to information necessary for creating a weight loss plan, such as the user's body shape, weight, age, health status, dietary preferences, and allergies.

[0008] "Analysis" refers to the process of processing data based on inputted physical characteristics information and extracting information to design the optimal weight loss plan for the user.

[0009] A "success story" refers to information about the profile and process of an individual who has successfully achieved weight loss in the past.

[0010] "Similar cases" refer to past success stories that are similar in physical characteristics and weight loss needs of the users.

[0011] A "weight loss plan" refers to a specific set of guidelines, including meal plans and exercise plans, designed to help users lose weight in a healthy way.

[0012] "Products" refers to items, including supplements and protein, that are suggested to complement a weight loss plan.

[0013] "System" refers to a collection of programs and interfaces that provide users with weight loss plans, product recommendations, feedback collection, and database updates.

[0014] An "exercise program" refers to exercises designed to suit the physical characteristics of the user.

[0015] "Preference information" refers to a user's personal preferences and tendencies regarding food and lifestyle. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is an advanced system that proposes a weight loss plan tailored to a specific user, and is implemented through components consisting of a user, a server, and a terminal. This system applies artificial intelligence technology to analyze the user's physical characteristics and compares them with past success stories to provide a reliable weight loss method.

[0038] First, the user enters their physical characteristics information via a device. This information includes the user's height, weight, age, dietary preferences, allergies, and health status. This establishes an accurate profile of the user.

[0039] This information is then sent to a server, which uses an AI model to analyze the data. The analyzed data is compared to numerous existing success stories in the database. Once similar cases are identified, the weight loss plan with the highest statistical success rate is generated. This plan includes a meal plan and exercise program tailored to the user's profile.

[0040] The server then sends the generated weight loss plan to the terminal, which is then presented to the user. This allows the user to receive personalized and specific guidance. Furthermore, products necessary to support the plan are recommended, and the user can purchase them.

[0041] This system also includes a function to collect feedback on the results of implemented weight loss plans. Users record the progress and effectiveness of their plans, and this information is sent back to the server. The server uses this feedback to update its AI model and improve its database. This allows for more accurate suggestions to be made to future users.

[0042] As a concrete example, when a 30-year-old male user inputs his physical characteristics into the system, the server searches for past success stories with similar profiles and generates a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this suggestion, the user can purchase relevant protein products at the terminal. This entire process provides the user with an environment that supports optimal weight loss.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses their device to enter physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information into a form. After completing the input, they operate the confirmation button and send the data to the server.

[0046] Step 2:

[0047] The terminal receives data entered by the user, securely encrypts it, and then sends it to the server. The data is protected using a communication protocol and reaches the server accurately.

[0048] Step 3:

[0049] The server decrypts the user data it receives and begins analysis based on an AI model. Specifically, it calculates indicators such as BMI and basal metabolic rate and performs a detailed analysis of the user's physical characteristics.

[0050] Step 4:

[0051] The server accesses a database of past success stories and searches for cases similar to the analyzed user data. Machine learning algorithms are used to identify the most similar cases.

[0052] Step 5:

[0053] The server generates a statistically reliable weight loss plan based on similar cases. The generated plan includes a meal plan and exercise program, and is optimized for the user.

[0054] Step 6:

[0055] The server sends the generated weight loss plan to the terminal. The terminal receives it and provides an interface to display it in an easy-to-understand manner for the user.

[0056] Step 7:

[0057] The server generates a list of products related to the user's weight loss plan and sends it to the terminal to assist with the purchase. The terminal displays product details and provides access to the purchase process.

[0058] Step 8:

[0059] Users follow a weight loss plan and input their progress and feedback via a terminal. The feedback data is sent to the server through the terminal.

[0060] Step 9:

[0061] The server incorporates the received feedback into the database and retrains the AI ​​model. This creates new success stories, improving the accuracy of future suggestions.

[0062] Step 10:

[0063] After the server updates the AI ​​model and database, it notifies the user of any new information or plan modifications as needed. This ensures that the user always receives optimal support.

[0064] (Example 1)

[0065] 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."

[0066] Modern consumers have access to a vast amount of health information, but they struggle to effectively personalize this information and translate it into concrete action plans. Furthermore, they often lack the means to appropriately select products related to their plans, resulting in ineffective health promotion. Continuously improving the system through user feedback and providing more accurate recommendations in the future is also a crucial challenge.

[0067] 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.

[0068] In this invention, the server includes means for applying a model for analyzing individual user information, means for identifying relevant cases by comparing success story information with the individual user information, and means for creating an appropriate plan based on the relevant cases. This enables the creation of personalized health plans, precise product recommendations based on user characteristics and preferences, and continuous improvement of the system based on feedback.

[0069] "Personalized user information" refers to information about a user's height, weight, age, dietary preferences, allergies, and health status, and is data used to form an individual user profile.

[0070] "Means of applying a model for analysis" refers to a method for performing a process of analyzing data using artificial intelligence technology based on individual user information and extracting features.

[0071] "Success story information" refers to data on cases in the past where users with similar characteristics and conditions successfully lost weight or improved their health.

[0072] "Methods for identifying relevant cases" refers to the process of comparing individual user information with success story information to calculate similarity and identify highly relevant cases.

[0073] "Means of creating an appropriate plan" refers to methods for creating individualized health promotion plans optimized for users, based on identified relevant cases.

[0074] "Product recommendation" refers to the process of providing a list of products that take into account individual preferences and health conditions in order to support users in implementing their plans.

[0075] "Continuous system improvement based on feedback" refers to a method of improving the system's performance and the accuracy of its suggestions by using user feedback and evaluations after the implementation of a plan.

[0076] To implement the invention, the user must first use an appropriate terminal. This terminal is internet-connected and provides an interface for receiving user input. The user inputs their physical characteristics information using, for example, a smartphone or personal computer. The input information includes data such as height, weight, age, dietary preferences, allergies, and health status.

[0077] After receiving this input information, the terminal formats the data and sends it to the server. This server runs on a high-performance cloud computer and uses software such as Python and TENSORFLOW® to analyze the data using generative AI models. The AI ​​model compares the user's information with success stories to develop an optimal, personalized weight loss plan or health program. The server performs similarity calculations to identify highly relevant success stories.

[0078] The plan generated by the server is presented to the user via the terminal, allowing the user to receive a specific health improvement plan. This plan includes details of suggested meals and exercises, as well as recommendations for related products. Based on the information received, the user can select products and purchase them through the terminal.

[0079] As a concrete example, consider a 30-year-old male user who inputs his personal information into the system. Based on this information, the server searches for past success stories and proposes a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this proposal, a suitable protein product is recommended, and the user can purchase it through the terminal.

[0080] The following are examples of prompt messages in the system.

[0081] "A 30-year-old man is asking the AI ​​system for a weight loss plan that includes a high-protein, low-carbohydrate meal plan. Please suggest a solution that includes running three times a week and tracking its effective progress."

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

[0083] Step 1:

[0084] The user inputs their physical characteristics into the terminal. This data includes height, weight, age, dietary preferences, allergies, and health status. The terminal receives this information through the user interface, formats it, and prepares it for transmission to the server. At this stage, the input is raw user data, while the output is formatted digital data.

[0085] Step 2:

[0086] The server receives formatted digital data sent from the terminal. The server performs data preprocessing, including data cleaning and handling of missing values. This process transforms the data into a format necessary for analysis, making it ready for the AI ​​model to receive. The input is formatted data from the terminal, and the output is preprocessed, formatted data.

[0087] Step 3:

[0088] The server uses a generative AI model to analyze pre-processed, formatted data. It extracts features from user data using machine learning libraries such as TensorFlow. The server identifies related cases by comparing them with successful case information in the database and calculating similarity. The input is formatted data, and the output is a list of similar cases.

[0089] Step 4:

[0090] Based on similar cases identified by the server, an appropriate health promotion plan is created. The plan includes suggested meal plans and exercise programs. Subsequently, a generative AI model is used to develop the plan and generate personalized suggestions for the user. The input is a list of similar cases, and the output is personalized plan data.

[0091] Step 5:

[0092] The server sends the generated plan data to the terminal. The terminal presents this to the user through a user interface. Furthermore, products related to the plan are recommended in a list format. The terminal visually displays this product information, allowing the user to select and purchase them. The input is the plan data, and the output is the information presented to the user.

[0093] Step 6:

[0094] The user records the progress and effectiveness of their weight loss plan on their device. The device sends this information to the server. The server collects feedback data and updates the system's generating AI model to enhance the database. This establishes a process that improves the accuracy of future suggestions. The input is feedback data, and the output is the updated model and database.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern society, many individuals seek ways to maintain a healthy lifestyle and manage their weight efficiently. However, existing weight loss plans and health support services typically offer generalized approaches, making it difficult to adequately address individual needs. Furthermore, there is a lack of automated means to provide comprehensive health support within the user's living environment. Therefore, there is a need to develop systems that efficiently deliver personalized health plans and are easily implementable at home.

[0098] 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.

[0099] In this invention, the server includes means for inputting the user's physical characteristics information, means for analyzing the physical characteristics information and identifying similar cases by comparing it with past success stories, and means for generating an optimal weight loss plan based on similar cases. This enables the provision of a personalized and healthy weight loss plan and support tailored to individual needs. Furthermore, it provides automated machine means to support health in the user's living environment, suggesting appropriate foods and exercise equipment to facilitate implementation at home.

[0100] "User physical characteristics information" refers to physical and health-related data concerning individual users, such as height, weight, age, dietary preferences, allergy information, and health status.

[0101] "Means of analysis" refer to methods and mechanisms for processing input physical characteristic information and analyzing the data using pattern recognition and statistical techniques.

[0102] "Past success stories" refer to a database of information about weight loss plans that have been successfully implemented by users with similar profiles in the past.

[0103] "Similar cases" refer to cases identified from current users' physical characteristics information and past success stories that share corresponding characteristics and conditions.

[0104] An "optimal weight loss plan" is a personalized diet and exercise plan that is expected to yield maximum results, based on the user's physical characteristics and similar cases.

[0105] "Automated mechanical means that provide health support in the user's living environment" refers to mechanized devices or systems that operate in the user's home or daily environment for the purpose of supporting health maintenance or weight loss.

[0106] "Methods for recommending and encouraging the purchase of products" refers to the process of suggesting appropriate products to users and motivating them to purchase them in order to achieve health improvements related to the generated weight loss plan.

[0107] The "means of updating and learning from the database" refer to a function that stores the results of actually implemented weight loss plans into the database, uses those results through the AI ​​model, and improves the overall accuracy of the system.

[0108] In this invention, the user inputs their physical characteristics information using a dedicated terminal, and this information is transmitted to a central server. The server analyzes the information using an AI analysis engine built on a cloud computing platform and compares it with a database of past success stories. A machine learning model using Python plays a role in processing this data. Once similar cases are identified, the server generates an optimal weight loss plan based on those cases. The generated plan includes an individualized meal plan and exercise program, which is then transmitted to the user terminal.

[0109] In the user's home environment, automated mechanical means support exercise and meal planning. The robot assistant monitors the user's activities in real time and provides feedback via voice and visuals as needed. For example, it can show the user the correct form for exercise or offer suggestions regarding meals. The machine also recommends health-related products and has a function to encourage the purchase of these products via a terminal.

[0110] Furthermore, the results of the weight loss plans implemented by users are returned to the server as feedback, and the AI ​​model uses this as training data to strengthen the database. This will enable more accurate suggestions in the future.

[0111] As a concrete example, suppose a user is following a plan that includes a high-protein diet and running three times a week. In this case, the user can receive guidance on proper form during exercise from a robot assistant and also receive a shopping list of protein-rich foods on their device. An example of a prompt to the generating AI model would be: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Refer to past success stories to suggest the optimal plan."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users input physical characteristics information using a dedicated terminal. This input includes height, weight, age, dietary preferences, allergy information, and health status. This information forms the basis for creating the user's individual profile.

[0115] Step 2:

[0116] The device sends the entered physical characteristics information to the server. The server then passes this information to an AI analysis engine, which begins comparing it with a database. Specifically, a Python®-based machine learning model located in the cloud receives the information and prepares it for analysis.

[0117] Step 3:

[0118] The server's AI analysis engine analyzes the input physical characteristics information and compares it to a database of past success stories. This identifies similar cases. The analysis engine uses data-driven pattern recognition and statistical methods to derive the most effective weight loss plan based on the identified cases.

[0119] Step 4:

[0120] The server sends an optimal weight loss plan, generated based on similar cases, to the user's terminal. This plan includes a meal plan and exercise program, tailored to the user's profile. The terminal visually presents this plan to the user, formatting it for easy implementation.

[0121] Step 5:

[0122] In the user's home environment, automated mechanical means support the execution of the weight loss plan. The robotic assistant guides the user according to suggested exercises and diets, providing alerts and supplementary information as needed. During this process, motion feedback and event recording are performed.

[0123] Step 6:

[0124] After completing the weight loss plan, the user enters feedback into their device and sends the results to the server. The server receives this feedback, updates its database, and trains its AI model. This improves the accuracy of future weight loss plans.

[0125] Step 7:

[0126] The process involves inputting a prompt into the AI ​​model to obtain additional suggestions and improvements. For example, the prompt might read: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Referencing past success stories, suggest the optimal plan."

[0127] 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.

[0128] This invention is an innovative system that proposes a weight loss plan based on the user's physical characteristics and also adjusts it to take into account the user's emotional state. This system is implemented using a user, server, terminal, and emotion engine to provide personalized health support.

[0129] First, the user enters their physical characteristics information via a device. The device collects various data about the user, such as height, weight, age, dietary preferences, allergies, and health status. The entered data is encrypted by the device and sent to the server.

[0130] Next, the server analyzes the received data using an AI model. This creates a detailed profile of the user, which is then compared to past success stories in the database. Based on these similar cases, the server generates an optimal weight loss plan and determines specific meal plans and exercise programs.

[0131] Simultaneously, the emotion engine recognizes the user's emotional state in real time. This emotional data reflects the user's current motivation, stress level, and psychological state. The server uses this information to make the weight loss plan more flexible. For example, if the user is under high stress, the plan will be adjusted to focus on lighter exercise and relaxation techniques.

[0132] The server also sends a weight loss plan optimized for the user to the device, which then presents a visualized version of the plan to the user. The user acts according to the plan and reports their feedback and emotional state again through the device. This feedback is sent to the server and re-evaluated by the emotion engine.

[0133] This feedback loop allows users to always receive support best suited to their current state and long-term health goals. The results are used to update the database and retrain the AI ​​model, enabling the system to prepare more refined next suggestions.

[0134] For example, if a user is aiming for healthy weight loss, the emotion engine analyzes the user's emotional data and identifies that their motivation tends to be lower on weekdays. Taking this into account, the server adjusts the plan to focus on exercise on weekends. Furthermore, if stress levels are high, it incorporates meditation or yoga to provide a more sustainable overall plan. In this way, the system provides personalized, adaptive health support based on the user's emotions.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user uses a device to input physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information. The device processes this information, encrypts it, and then sends it to the server.

[0138] Step 2:

[0139] The server decrypts the user data received from the terminal and inputs it into an AI model. The AI ​​model calculates indicators based on physical characteristics and analyzes the user's profile in detail.

[0140] Step 3:

[0141] The server uses the analyzed profile to reference past success stories in the database. Machine learning algorithms are used to identify past cases that most closely resemble the user's profile.

[0142] Step 4:

[0143] The server generates an optimal weight loss plan based on identified similar cases. This plan includes a meal plan and exercise program, customized to the user's specific characteristics.

[0144] Step 5:

[0145] The emotion engine is accessed from the user's device and collects the user's emotional data in real time. This emotional data reflects the user's current state of mind and motivation.

[0146] Step 6:

[0147] The server uses emotional data provided by the emotion engine to further adjust the generated weight loss plan. For example, if motivation is low, it reduces the amount of exercise and increases stress management activities.

[0148] Step 7:

[0149] A customized weight loss plan is sent from the server to the terminal. The terminal displays this plan to the user in an easy-to-understand format.

[0150] Step 8:

[0151] Users follow a plan and input progress and feedback via their device. This includes the amount of exercise performed, what they ate, and changes in their emotions.

[0152] Step 9:

[0153] The device sends user feedback to the server, which records the feedback data in a database. Furthermore, this data, along with sentiment data, is used to retrain the AI ​​model.

[0154] Step 10:

[0155] Based on the learning results, the server provides users with new information and improved weight loss plans as needed. This ensures that users always receive the latest health support.

[0156] (Example 2)

[0157] 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".

[0158] When providing personalized health support plans, the challenge lies in generating more effective and sustainable plans by considering not only the user's physical characteristics but also their real-time emotional state. Conventional systems have struggled to dynamically adjust plans while considering emotional states, and have failed to alleviate the psychological burden on users.

[0159] 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.

[0160] In this invention, the server includes means for encrypting and communicating the user's physical characteristics information, means for using an artificial intelligence model for analyzing the user's physical characteristics information, and means for recognizing the user's emotional state in real time and adjusting the plan accordingly. This makes it possible to provide a personalized plan that takes into account the user's physical and emotional characteristics.

[0161] "User's physical characteristics information" refers to basic data related to individual health management, including an individual's height, weight, age, dietary preferences, allergies, and health status.

[0162] "Encryption" is a technology that uses specific algorithms to transform information in order to enhance data security and prevent unauthorized access.

[0163] An "artificial intelligence model" is a computer program or computational technique used to analyze large amounts of data and find patterns and relationships.

[0164] A "similar case" refers to a past successful case within the database that has characteristics similar to the current user's situation.

[0165] "Emotional state" refers to variables that indicate the user's psychological motivations, stress levels, and emotions such as joy, anger, sadness, and happiness.

[0166] "Adjusting the plan" refers to the action of modifying the generated health support plan based on the user's real-time emotions and feedback in order to provide the most appropriate support.

[0167] "Database updating" is a procedure to keep the accumulated information up-to-date based on new feedback and results from users.

[0168] This invention is a system that provides personalized health support based on the user's physical characteristics and also takes into account their emotional state. This system mainly consists of a user, a server, and a terminal.

[0169] First, the user uses a device to input their physical characteristics information. This includes height, weight, age, dietary preferences, allergies, etc. The device encrypts this data using an encryption library (e.g., common encryption software) and securely transmits it to the server.

[0170] The server receives the transmitted data and analyzes it using a generative AI model. This AI model is built on a common deep learning framework such as TensorFlow and generates a user profile by comparing it to a database of past success stories. Based on this profile, the server creates a customized weight loss plan. The plan includes individual meal plans and exercise programs. The server also uses an emotion engine to acquire real-time emotional data and adjust the plan to take stress levels and motivation into account. The emotion engine uses emotion analysis software (e.g., general emotion recognition software).

[0171] Once a plan is created, the server sends it to the terminal. The terminal then presents this information to the user in a visually easy-to-understand format. For example, the user's weekly exercise schedule can be displayed in a calendar or graph format.

[0172] For example, if a user sets a weight loss goal, and the emotion engine identifies a tendency for low motivation during weekdays, the server will adjust the plan to focus on exercise on weekends. It will also suggest incorporating relaxation activities during periods of high stress. In this way, the system adapts to the user's emotions to provide a sustainable health plan.

[0173] An example of a prompt message might be, "Considering this user's current emotional state, suggest an exercise plan for next weekend." This allows the system to automatically generate appropriate suggestions tailored to the user's needs.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users input physical characteristics information using their device. Specifically, they enter information such as height, weight, age, dietary preferences, allergies, and health status into forms on the application interface. The input data is temporarily stored on the device and securely protected using an encryption algorithm. Encrypted user data is generated as output.

[0177] Step 2:

[0178] The terminal sends encrypted user data to the server. A communication protocol (e.g., HTTPS) is used in this process. The input is encrypted user data, and the output is a secure data transmission to the server.

[0179] Step 3:

[0180] The server decrypts the received data. The decrypted data is input into a generating AI model for analysis and profile generation. This model uses a common deep learning framework and compares past success stories with newly acquired user data. As output, a personalized health profile is generated.

[0181] Step 4:

[0182] The server generates an optimal weight loss plan based on the generated health profile. Using the profile data as input, the AI ​​performs calorie calculations and nutritional analysis. The output is an optimal plan that includes a meal plan and exercise program.

[0183] Step 5:

[0184] In parallel, the server uses an emotion engine to analyze the user's current emotional state in real time. User messages and voice data are used as input data and analyzed by emotion analysis software. The output is data indicating the user's real-time emotional state.

[0185] Step 6:

[0186] The server takes emotional data into consideration and adjusts the weight loss plan generated earlier. Depending on the emotional state, it may change the frequency and type of exercise. The inputs are the health profile and emotional data, and the output is the adjusted health support plan.

[0187] Step 7:

[0188] Finally, the server sends the adjusted health support plan to the device. The device receives this and presents it to the user in a visualized format. The display methods vary widely, including graphs, calendars, or reminders. The output provides the user with a health support plan that is visually easy to understand.

[0189] Step 8:

[0190] The user acts based on the presented plan and inputs the results and feedback again on the terminal. The input feedback is encrypted on the terminal and sent to the server. The output is feedback data used to improve the next plan.

[0191] (Application Example 2)

[0192] 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".

[0193] Traditional weight loss plans and health support systems are limited to suggestions based on the user's physical characteristics and are unable to flexibly respond to changes in the user's emotions or daily life. As a result, plans often fail to adapt to the user and are not sustainable. Furthermore, existing systems have difficulty adjusting plans in response to changes in emotional state, resulting in the challenge of maintaining user motivation.

[0194] 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.

[0195] In this invention, the server includes means for analyzing the user's physical characteristics information, means for acquiring emotional state in real time and adjusting the weight loss plan, and means for supporting the user's daily activities with a home robot and collecting feedback. This makes it possible to provide individually optimized health support based on the user's physical information and emotional state.

[0196] "User physical characteristics information" refers to individual data about the user, such as height, weight, age, dietary preferences, allergies, and health status.

[0197] "Means of analysis" refers to methods and technical devices for analyzing acquired data to create a user profile.

[0198] "Means for identifying similar cases" refers to methods and technical devices for comparing past successful cases with current user data to find similarities.

[0199] A "weight loss plan" is a specific schedule of meals and exercise provided to users with the aim of maintaining their health and losing weight.

[0200] "Means of adjustment" refers to a technical process that dynamically revises existing weight loss plans based on emotional information acquired in real time.

[0201] "Household robots" refer to devices and equipment used to support the activities and daily lives of users in their home environment.

[0202] "Feedback" refers to the reactions and comments provided by users, and this data is used to improve and adjust the system.

[0203] To implement this invention, a terminal for user use and a home robot to be installed in the home are prepared. The terminal provides an interface for inputting the user's physical characteristics information and transmits this data encrypted to a server. On the server, an AI model is running to analyze this data and create a user profile. This profile is compared to past success stories in a database and generates an optimal weight loss plan based on similar cases.

[0204] Home robots are equipped with sensors and cameras to acquire emotional states in real time while assisting users with their daily activities. OpenCV and TensorFlow may be used as emotion recognition AI. The emotional information acquired by the robot is sent to a server and used to dynamically adjust the weight loss plan. For example, during periods of high stress, the plan might be modified to recommend lighter exercise or relaxation.

[0205] The weight loss plan and recommended activities generated by the server are visualized and presented to the user via a terminal. The user acts based on this plan and reports feedback on the results and emotional state to the server via a home robot. This feedback is used to improve the AI ​​model and update the database, which will be used to inform future suggestions.

[0206] For example, if a user is aiming for a target weight and the emotional engine identifies a tendency for the user's motivation to be higher on weekends, it may adjust the plan to incorporate intensive exercise on weekends.

[0207] An example of a prompt message for input to the generating AI model is: "Consider the user's current emotional state and provide an optimal health maintenance plan. User data: Height 170cm, Weight 70kg, Age 30, Stress level high, Exercise motivation low."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The terminal receives physical characteristics information from the user as input. The user enters data such as height, weight, age, dietary preferences, allergies, and health status, and then encrypts this data before sending it to the server. The entered information is protected by a data encryption algorithm and transmitted through a secure communication channel.

[0211] Step 2:

[0212] The server analyzes the received physical characteristics information of the user. Using data analysis AI, it builds a user profile and compares it to past success stories in the database. This process uses data mining techniques to recognize patterns in order to identify similar cases. As a result of the analysis, data obtained from similar past cases is output.

[0213] Step 3:

[0214] The server generates an optimal weight loss plan based on similar cases. The AI ​​model takes user-specific physical information and analysis results as input to generate an optimized weight loss plan, including meal plans and exercise programs. This plan generation process uses machine learning algorithms to adjust suggestions based on user preferences. The generated weight loss plan is then output.

[0215] Step 4:

[0216] Home robots use sensors and cameras to acquire the user's emotional state. Real-time emotion recognition AI analyzes facial expressions, tone of voice, and other factors to identify the user's emotions. In this process, the acquired emotional data is used as input, and information such as the user's motivation and stress level is output.

[0217] Step 5:

[0218] The server dynamically adjusts the weight loss plan based on the user's emotional state. Using acquired emotional data as input, it modifies the plan according to the situation, such as whether the user is stressed or highly motivated. This process is carried out through conditional judgment by a generative AI model, and the adjusted weight loss plan is output.

[0219] Step 6:

[0220] The device presents the user with a visualized weight loss plan. Using plan data provided by the server as input, it visualizes it through a user-friendly interface and provides actionable guidance to the user. Based on the displayed information, the user can begin activities in accordance with the plan.

[0221] Step 7:

[0222] The home robot collects user feedback and reports it to a server. Feedback on the progress of the weight loss plan and changes in emotions is entered and resent to the server. This feedback is stored as data to be used to improve the plan in the future.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is an advanced system that proposes a weight loss plan tailored to a specific user, and is implemented through components consisting of a user, a server, and a terminal. This system applies artificial intelligence technology to analyze the user's physical characteristics and compares them with past success stories to provide a reliable weight loss method.

[0240] First, the user enters their physical characteristics information via a device. This information includes the user's height, weight, age, dietary preferences, allergies, and health status. This establishes an accurate profile of the user.

[0241] This information is then sent to a server, which uses an AI model to analyze the data. The analyzed data is compared to numerous existing success stories in the database. Once similar cases are identified, the weight loss plan with the highest statistical success rate is generated. This plan includes a meal plan and exercise program tailored to the user's profile.

[0242] The server then sends the generated weight loss plan to the terminal, which is then presented to the user. This allows the user to receive personalized and specific guidance. Furthermore, products necessary to support the plan are recommended, and the user can purchase them.

[0243] This system also includes a function to collect feedback on the results of implemented weight loss plans. Users record the progress and effectiveness of their plans, and this information is sent back to the server. The server uses this feedback to update its AI model and improve its database. This allows for more accurate suggestions to be made to future users.

[0244] As a concrete example, when a 30-year-old male user inputs his physical characteristics into the system, the server searches for past success stories with similar profiles and generates a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this suggestion, the user can purchase relevant protein products at the terminal. This entire process provides the user with an environment that supports optimal weight loss.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user uses their device to enter physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information into a form. After completing the input, they operate the confirmation button and send the data to the server.

[0248] Step 2:

[0249] The terminal receives data entered by the user, securely encrypts it, and then sends it to the server. The data is protected using a communication protocol and reaches the server accurately.

[0250] Step 3:

[0251] The server decrypts the user data it receives and begins analysis based on an AI model. Specifically, it calculates indicators such as BMI and basal metabolic rate and performs a detailed analysis of the user's physical characteristics.

[0252] Step 4:

[0253] The server accesses a database of past success stories and searches for cases similar to the analyzed user data. Machine learning algorithms are used to identify the most similar cases.

[0254] Step 5:

[0255] The server generates a statistically reliable weight loss plan based on similar cases. The generated plan includes a meal plan and exercise program, and is optimized for the user.

[0256] Step 6:

[0257] The server sends the generated weight loss plan to the terminal. The terminal receives it and provides an interface to display it in an easy-to-understand manner for the user.

[0258] Step 7:

[0259] The server generates a list of products related to the user's weight loss plan and sends it to the terminal to assist with the purchase. The terminal displays product details and provides access to the purchase process.

[0260] Step 8:

[0261] Users follow a weight loss plan and input their progress and feedback via a terminal. The feedback data is sent to the server through the terminal.

[0262] Step 9:

[0263] The server incorporates the received feedback into the database and retrains the AI ​​model. This creates new success stories, improving the accuracy of future suggestions.

[0264] Step 10:

[0265] After the server updates the AI ​​model and database, it notifies the user of any new information or plan modifications as needed. This ensures that the user always receives optimal support.

[0266] (Example 1)

[0267] 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."

[0268] Modern consumers have access to a vast amount of health information, but they struggle to effectively personalize this information and translate it into concrete action plans. Furthermore, they often lack the means to appropriately select products related to their plans, resulting in ineffective health promotion. Continuously improving the system through user feedback and providing more accurate recommendations in the future is also a crucial challenge.

[0269] 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.

[0270] In this invention, the server includes means for applying a model for analyzing individual user information, means for identifying relevant cases by comparing success story information with the individual user information, and means for creating an appropriate plan based on the relevant cases. This enables the creation of personalized health plans, precise product recommendations based on user characteristics and preferences, and continuous improvement of the system based on feedback.

[0271] "Personalized user information" refers to information about a user's height, weight, age, dietary preferences, allergies, and health status, and is data used to form an individual user profile.

[0272] "Means of applying a model for analysis" refers to a method for performing a process of analyzing data using artificial intelligence technology based on individual user information and extracting features.

[0273] "Success story information" refers to data on cases in the past where users with similar characteristics and conditions successfully lost weight or improved their health.

[0274] "Methods for identifying relevant cases" refers to the process of comparing individual user information with success story information to calculate similarity and identify highly relevant cases.

[0275] "Means of creating an appropriate plan" refers to methods for creating individualized health promotion plans optimized for users, based on identified relevant cases.

[0276] "Product recommendation" refers to the process of providing a list of products that take into account individual preferences and health conditions in order to support users in implementing their plans.

[0277] "Continuous system improvement based on feedback" refers to a method of improving the system's performance and the accuracy of its suggestions by using user feedback and evaluations after the implementation of a plan.

[0278] To implement the invention, the user must first use an appropriate terminal. This terminal is internet-connected and provides an interface for receiving user input. The user inputs their physical characteristics information using, for example, a smartphone or personal computer. The input information includes data such as height, weight, age, dietary preferences, allergies, and health status.

[0279] After receiving this input information, the terminal formats the data and sends it to the server. This server runs on a high-performance cloud computer and uses software such as Python and TensorFlow to analyze the data using generative AI models. The AI ​​model compares the user's information with success stories to develop an optimal, personalized weight loss plan or health program. The server performs similarity calculations to identify highly relevant success stories.

[0280] The plan generated by the server is presented to the user via the terminal, allowing the user to receive a specific health improvement plan. This plan includes details of suggested meals and exercises, as well as recommendations for related products. Based on the information received, the user can select products and purchase them through the terminal.

[0281] As a concrete example, consider a 30-year-old male user who inputs his personal information into the system. Based on this information, the server searches for past success stories and proposes a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this proposal, a suitable protein product is recommended, and the user can purchase it through the terminal.

[0282] The following are examples of prompt messages in the system.

[0283] "A 30-year-old man is asking the AI ​​system for a weight loss plan that includes a high-protein, low-carbohydrate meal plan. Please suggest a solution that includes running three times a week and tracking its effective progress."

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user inputs their own physical characteristic information into the terminal. The data to be input includes height, weight, age, dietary preferences, allergies, health status, etc. The terminal receives this information through the user interface and formats the data into a form that can be transmitted to the server. The input at this stage is raw user data, and the output is formatted digital data.

[0287] Step 2:

[0288] The server receives the formatted digital data transmitted from the terminal. The server performs data preprocessing, including data cleaning and handling missing values. Through this processing, the data is converted into a form required for analysis and made ready for the AI model to receive. The input is the formatted data from the terminal, and the output is the preprocessed and formatted data.

[0289] Step 3:

[0290] The server uses the generated AI model to analyze the preprocessed and formatted data. Using a machine learning library such as TensorFlow, it extracts the characteristics of the user data and compares it with the successful case information in the database to identify relevant cases by calculating the similarity. The input is the formatted data, and the output is a list of similar cases.

[0291] Step 4:

[0292] Based on similar cases identified by the server, an appropriate health promotion plan is created. The plan includes suggested meal plans and exercise programs. Subsequently, a generative AI model is used to develop the plan and generate personalized suggestions for the user. The input is a list of similar cases, and the output is personalized plan data.

[0293] Step 5:

[0294] The server sends the generated plan data to the terminal. The terminal presents this to the user through a user interface. Furthermore, products related to the plan are recommended in a list format. The terminal visually displays this product information, allowing the user to select and purchase them. The input is the plan data, and the output is the information presented to the user.

[0295] Step 6:

[0296] The user records the progress and effectiveness of their weight loss plan on their device. The device sends this information to the server. The server collects feedback data and updates the system's generating AI model to enhance the database. This establishes a process that improves the accuracy of future suggestions. The input is feedback data, and the output is the updated model and database.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] In modern society, many individuals seek ways to maintain a healthy lifestyle and manage their weight efficiently. However, existing weight loss plans and health support services typically offer generalized approaches, making it difficult to adequately address individual needs. Furthermore, there is a lack of automated means to provide comprehensive health support within the user's living environment. Therefore, there is a need to develop systems that efficiently deliver personalized health plans and are easily implementable at home.

[0300] 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.

[0301] In this invention, the server includes means for inputting the user's physical characteristics information, means for analyzing the physical characteristics information and identifying similar cases by comparing it with past success stories, and means for generating an optimal weight loss plan based on similar cases. This enables the provision of a personalized and healthy weight loss plan and support tailored to individual needs. Furthermore, it provides automated machine means to support health in the user's living environment, suggesting appropriate foods and exercise equipment to facilitate implementation at home.

[0302] "User physical characteristics information" refers to physical and health-related data concerning individual users, such as height, weight, age, dietary preferences, allergy information, and health status.

[0303] "Means of analysis" refer to methods and mechanisms for processing input physical characteristic information and analyzing the data using pattern recognition and statistical techniques.

[0304] "Past success stories" refer to a database of information about weight loss plans that have been successfully implemented by users with similar profiles in the past.

[0305] "Similar cases" refer to cases identified from current users' physical characteristics information and past success stories that share corresponding characteristics and conditions.

[0306] The "optimal weight loss plan" is an individualized diet and exercise plan based on the user's physical characteristic information and similar cases, expecting maximum effects.

[0307] The "automated mechanical means for providing health support in the user's living environment" are mechanized devices or systems that operate for the purpose of maintaining health and supporting weight loss in the user's home and daily environment.

[0308] The "means for recommending products and promoting purchases" is a process of proposing appropriate products to the user and motivating them to purchase in order to aim for health improvement related to the generated weight loss plan.

[0309] The "means for updating the database and performing learning" is a function of newly storing the results of the actually implemented weight loss plan in the database, using those results through an AI model, and improving the accuracy of the entire system.

[0310] In this invention, the user inputs their physical characteristic information using a dedicated terminal, and this information is transmitted to the central server. The server analyzes the information using an AI analysis engine built on a cloud computing platform and matches it with the past success case database. A machine learning model using Python plays a role in processing these data. When similar cases are identified, the server generates an optimal weight loss plan based on the corresponding cases. The generated plan includes an individualized diet plan and exercise program, and this is transmitted to the user terminal.

[0311] In the user's home environment, automated mechanical means support the execution of exercise and diet. The robot assistant monitors the user's activities in real time and provides feedback through voice and visual means as needed. For example, it shows the user the correct form of exercise or makes suggestions regarding diet. This machine also has a function of recommending products related to maintaining health and promoting the purchase of these products via the terminal.

[0312] Furthermore, the results of the weight loss plans implemented by users are returned to the server as feedback, and the AI ​​model uses this as training data to strengthen the database. This will enable more accurate suggestions in the future.

[0313] As a concrete example, suppose a user is following a plan that includes a high-protein diet and running three times a week. In this case, the user can receive guidance on proper form during exercise from a robot assistant and also receive a shopping list of protein-rich foods on their device. An example of a prompt to the generating AI model would be: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Refer to past success stories to suggest the optimal plan."

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] Users input physical characteristics information using a dedicated terminal. This input includes height, weight, age, dietary preferences, allergy information, and health status. This information forms the basis for creating the user's individual profile.

[0317] Step 2:

[0318] The device sends the entered physical characteristics information to the server. The server then passes this information to an AI analysis engine, which begins comparing it with a database. Specifically, a Python-based machine learning model located in the cloud receives the information and prepares it for analysis.

[0319] Step 3:

[0320] The server's AI analysis engine analyzes the input physical characteristics information and compares it to a database of past success stories. This identifies similar cases. The analysis engine uses data-driven pattern recognition and statistical methods to derive the most effective weight loss plan based on the identified cases.

[0321] Step 4:

[0322] The server sends an optimal weight loss plan, generated based on similar cases, to the user's terminal. This plan includes a meal plan and exercise program, tailored to the user's profile. The terminal visually presents this plan to the user, formatting it for easy implementation.

[0323] Step 5:

[0324] In the user's home environment, automated mechanical means support the execution of the weight loss plan. The robotic assistant guides the user according to suggested exercises and diets, providing alerts and supplementary information as needed. During this process, motion feedback and event recording are performed.

[0325] Step 6:

[0326] After completing the weight loss plan, the user enters feedback into their device and sends the results to the server. The server receives this feedback, updates its database, and trains its AI model. This improves the accuracy of future weight loss plans.

[0327] Step 7:

[0328] The process involves inputting a prompt into the AI ​​model to obtain additional suggestions and improvements. For example, the prompt might read: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Referencing past success stories, suggest the optimal plan."

[0329] 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.

[0330] This invention is an innovative system that proposes a weight loss plan based on the user's physical characteristics and also adjusts it to take into account the user's emotional state. This system is implemented using a user, server, terminal, and emotion engine to provide personalized health support.

[0331] First, the user enters their physical characteristics information via a device. The device collects various data about the user, such as height, weight, age, dietary preferences, allergies, and health status. The entered data is encrypted by the device and sent to the server.

[0332] Next, the server analyzes the received data using an AI model. This creates a detailed profile of the user, which is then compared to past success stories in the database. Based on these similar cases, the server generates an optimal weight loss plan and determines specific meal plans and exercise programs.

[0333] Simultaneously, the emotion engine recognizes the user's emotional state in real time. This emotional data reflects the user's current motivation, stress level, and psychological state. The server uses this information to make the weight loss plan more flexible. For example, if the user is under high stress, the plan will be adjusted to focus on lighter exercise and relaxation techniques.

[0334] The server also sends a weight loss plan optimized for the user to the device, which then presents a visualized version of the plan to the user. The user acts according to the plan and reports their feedback and emotional state again through the device. This feedback is sent to the server and re-evaluated by the emotion engine.

[0335] This feedback loop allows users to always receive support best suited to their current state and long-term health goals. The results are used to update the database and retrain the AI ​​model, enabling the system to prepare more refined next suggestions.

[0336] For example, if a user is aiming for healthy weight loss, the emotion engine analyzes the user's emotional data and identifies that their motivation tends to be lower on weekdays. Taking this into account, the server adjusts the plan to focus on exercise on weekends. Furthermore, if stress levels are high, it incorporates meditation or yoga to provide a more sustainable overall plan. In this way, the system provides personalized, adaptive health support based on the user's emotions.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The user uses a device to input physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information. The device processes this information, encrypts it, and then sends it to the server.

[0340] Step 2:

[0341] The server decrypts the user data received from the terminal and inputs it into an AI model. The AI ​​model calculates indicators based on physical characteristics and analyzes the user's profile in detail.

[0342] Step 3:

[0343] The server uses the analyzed profile to reference past success stories in the database. Machine learning algorithms are used to identify past cases that most closely resemble the user's profile.

[0344] Step 4:

[0345] The server generates an optimal weight loss plan based on identified similar cases. This plan includes a meal plan and exercise program, customized to the user's specific characteristics.

[0346] Step 5:

[0347] The emotion engine is accessed from the user's device and collects the user's emotional data in real time. This emotional data reflects the user's current state of mind and motivation.

[0348] Step 6:

[0349] The server uses emotional data provided by the emotion engine to further adjust the generated weight loss plan. For example, if motivation is low, it reduces the amount of exercise and increases stress management activities.

[0350] Step 7:

[0351] A customized weight loss plan is sent from the server to the terminal. The terminal displays this plan to the user in an easy-to-understand format.

[0352] Step 8:

[0353] Users follow a plan and input progress and feedback via their device. This includes the amount of exercise performed, what they ate, and changes in their emotions.

[0354] Step 9:

[0355] The device sends user feedback to the server, which records the feedback data in a database. Furthermore, this data, along with sentiment data, is used to retrain the AI ​​model.

[0356] Step 10:

[0357] Based on the learning results, the server provides users with new information and improved weight loss plans as needed. This ensures that users always receive the latest health support.

[0358] (Example 2)

[0359] 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".

[0360] When providing personalized health support plans, the challenge lies in generating more effective and sustainable plans by considering not only the user's physical characteristics but also their real-time emotional state. Conventional systems have struggled to dynamically adjust plans while considering emotional states, and have failed to alleviate the psychological burden on users.

[0361] 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.

[0362] In this invention, the server includes means for encrypting and communicating the user's physical characteristics information, means for using an artificial intelligence model for analyzing the user's physical characteristics information, and means for recognizing the user's emotional state in real time and adjusting the plan accordingly. This makes it possible to provide a personalized plan that takes into account the user's physical and emotional characteristics.

[0363] "User's physical characteristics information" refers to basic data related to individual health management, including an individual's height, weight, age, dietary preferences, allergies, and health status.

[0364] "Encryption" is a technology that uses specific algorithms to transform information in order to enhance data security and prevent unauthorized access.

[0365] An "artificial intelligence model" is a computer program or computational technique used to analyze large amounts of data and find patterns and relationships.

[0366] A "similar case" refers to a past successful case within the database that has characteristics similar to the current user's situation.

[0367] "Emotional state" refers to variables that indicate the user's psychological motivations, stress levels, and emotions such as joy, anger, sadness, and happiness.

[0368] "Adjusting the plan" refers to the action of modifying the generated health support plan based on the user's real-time emotions and feedback in order to provide the most appropriate support.

[0369] "Database updating" is a procedure to keep the accumulated information up-to-date based on new feedback and results from users.

[0370] This invention is a system that provides personalized health support based on the user's physical characteristics and also takes into account their emotional state. This system mainly consists of a user, a server, and a terminal.

[0371] First, the user uses a device to input their physical characteristics information. This includes height, weight, age, dietary preferences, allergies, etc. The device encrypts this data using an encryption library (e.g., common encryption software) and securely transmits it to the server.

[0372] The server receives the transmitted data and analyzes it using a generative AI model. This AI model is built on a common deep learning framework such as TensorFlow and generates a user profile by comparing it to a database of past success stories. Based on this profile, the server creates a customized weight loss plan. The plan includes individual meal plans and exercise programs. The server also uses an emotion engine to acquire real-time emotional data and adjust the plan to take stress levels and motivation into account. The emotion engine uses emotion analysis software (e.g., general emotion recognition software).

[0373] Once a plan is created, the server sends it to the terminal. The terminal then presents this information to the user in a visually easy-to-understand format. For example, the user's weekly exercise schedule can be displayed in a calendar or graph format.

[0374] For example, if a user sets a weight loss goal, and the emotion engine identifies a tendency for low motivation during weekdays, the server will adjust the plan to focus on exercise on weekends. It will also suggest incorporating relaxation activities during periods of high stress. In this way, the system adapts to the user's emotions to provide a sustainable health plan.

[0375] An example of a prompt message might be, "Considering this user's current emotional state, suggest an exercise plan for next weekend." This allows the system to automatically generate appropriate suggestions tailored to the user's needs.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] Users input physical characteristics information using their device. Specifically, they enter information such as height, weight, age, dietary preferences, allergies, and health status into forms on the application interface. The input data is temporarily stored on the device and securely protected using an encryption algorithm. Encrypted user data is generated as output.

[0379] Step 2:

[0380] The terminal sends encrypted user data to the server. A communication protocol (e.g., HTTPS) is used in this process. The input is encrypted user data, and the output is a secure data transmission to the server.

[0381] Step 3:

[0382] The server decrypts the received data. The decrypted data is input into a generating AI model for analysis and profile generation. This model uses a common deep learning framework and compares past success stories with newly acquired user data. As output, a personalized health profile is generated.

[0383] Step 4:

[0384] The server generates an optimal weight loss plan based on the generated health profile. Using the profile data as input, the AI ​​performs calorie calculations and nutritional analysis. The output is an optimal plan that includes a meal plan and exercise program.

[0385] Step 5:

[0386] In parallel, the server uses an emotion engine to analyze the user's current emotional state in real time. User messages and voice data are used as input data and analyzed by emotion analysis software. The output is data indicating the user's real-time emotional state.

[0387] Step 6:

[0388] The server takes emotional data into consideration and adjusts the weight loss plan generated earlier. Depending on the emotional state, it may change the frequency and type of exercise. The inputs are the health profile and emotional data, and the output is the adjusted health support plan.

[0389] Step 7:

[0390] Finally, the server sends the adjusted health support plan to the device. The device receives this and presents it to the user in a visualized format. The display methods vary widely, including graphs, calendars, or reminders. The output provides the user with a health support plan that is visually easy to understand.

[0391] Step 8:

[0392] The user acts based on the presented plan and inputs the results and feedback again on the terminal. The input feedback is encrypted on the terminal and sent to the server. The output is feedback data used to improve the next plan.

[0393] (Application Example 2)

[0394] 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."

[0395] Traditional weight loss plans and health support systems are limited to suggestions based on the user's physical characteristics and are unable to flexibly respond to changes in the user's emotions or daily life. As a result, plans often fail to adapt to the user and are not sustainable. Furthermore, existing systems have difficulty adjusting plans in response to changes in emotional state, resulting in the challenge of maintaining user motivation.

[0396] 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.

[0397] In this invention, the server includes means for analyzing the user's physical characteristics information, means for acquiring emotional state in real time and adjusting the weight loss plan, and means for supporting the user's daily activities with a home robot and collecting feedback. This makes it possible to provide individually optimized health support based on the user's physical information and emotional state.

[0398] "User physical characteristics information" refers to individual data about the user, such as height, weight, age, dietary preferences, allergies, and health status.

[0399] "Means of analysis" refers to methods and technical devices for analyzing acquired data to create a user profile.

[0400] "Means for identifying similar cases" refers to methods and technical devices for comparing past successful cases with current user data to find similarities.

[0401] A "weight loss plan" is a specific schedule of meals and exercise provided to users with the aim of maintaining their health and losing weight.

[0402] "Means of adjustment" refers to a technical process that dynamically revises existing weight loss plans based on emotional information acquired in real time.

[0403] "Household robots" refer to devices and equipment used to support the activities and daily lives of users in their home environment.

[0404] "Feedback" refers to the reactions and comments provided by users, and this data is used to improve and adjust the system.

[0405] To implement this invention, a terminal for user use and a home robot to be installed in the home are prepared. The terminal provides an interface for inputting the user's physical characteristics information and transmits this data encrypted to a server. On the server, an AI model is running to analyze this data and create a user profile. This profile is compared to past success stories in a database and generates an optimal weight loss plan based on similar cases.

[0406] Home robots are equipped with sensors and cameras to acquire emotional states in real time while assisting users with their daily activities. OpenCV and TensorFlow may be used as emotion recognition AI. The emotional information acquired by the robot is sent to a server and used to dynamically adjust the weight loss plan. For example, during periods of high stress, the plan might be modified to recommend lighter exercise or relaxation.

[0407] The weight loss plan and recommended activities generated by the server are visualized and presented to the user via a terminal. The user acts based on this plan and reports feedback on the results and emotional state to the server via a home robot. This feedback is used to improve the AI ​​model and update the database, which will be used to inform future suggestions.

[0408] For example, if a user is aiming for a target weight and the emotional engine identifies a tendency for the user's motivation to be higher on weekends, it may adjust the plan to incorporate intensive exercise on weekends.

[0409] An example of a prompt message for input to the generating AI model is: "Consider the user's current emotional state and provide an optimal health maintenance plan. User data: Height 170cm, Weight 70kg, Age 30, Stress level high, Exercise motivation low."

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The terminal receives physical characteristics information from the user as input. The user enters data such as height, weight, age, dietary preferences, allergies, and health status, and then encrypts this data before sending it to the server. The entered information is protected by a data encryption algorithm and transmitted through a secure communication channel.

[0413] Step 2:

[0414] The server analyzes the received physical characteristics information of the user. Using data analysis AI, it builds a user profile and compares it to past success stories in the database. This process uses data mining techniques to recognize patterns in order to identify similar cases. As a result of the analysis, data obtained from similar past cases is output.

[0415] Step 3:

[0416] The server generates an optimal weight loss plan based on similar cases. The AI ​​model takes user-specific physical information and analysis results as input to generate an optimized weight loss plan, including meal plans and exercise programs. This plan generation process uses machine learning algorithms to adjust suggestions based on user preferences. The generated weight loss plan is then output.

[0417] Step 4:

[0418] Home robots use sensors and cameras to acquire the user's emotional state. Real-time emotion recognition AI analyzes facial expressions, tone of voice, and other factors to identify the user's emotions. In this process, the acquired emotional data is used as input, and information such as the user's motivation and stress level is output.

[0419] Step 5:

[0420] The server dynamically adjusts the weight loss plan based on the user's emotional state. Using acquired emotional data as input, it modifies the plan according to the situation, such as whether the user is stressed or highly motivated. This process is carried out through conditional judgment by a generative AI model, and the adjusted weight loss plan is output.

[0421] Step 6:

[0422] The device presents the user with a visualized weight loss plan. Using plan data provided by the server as input, it visualizes it through a user-friendly interface and provides actionable guidance to the user. Based on the displayed information, the user can begin activities in accordance with the plan.

[0423] Step 7:

[0424] The home robot collects user feedback and reports it to a server. Feedback on the progress of the weight loss plan and changes in emotions is entered and resent to the server. This feedback is stored as data to be used to improve the plan in the future.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This invention is an advanced system that proposes a weight loss plan tailored to a specific user, and is implemented through components consisting of a user, a server, and a terminal. This system applies artificial intelligence technology to analyze the user's physical characteristics and compares them with past success stories to provide a reliable weight loss method.

[0442] First, the user enters their physical characteristics information via a device. This information includes the user's height, weight, age, dietary preferences, allergies, and health status. This establishes an accurate profile of the user.

[0443] This information is then sent to a server, which uses an AI model to analyze the data. The analyzed data is compared to numerous existing success stories in the database. Once similar cases are identified, the weight loss plan with the highest statistical success rate is generated. This plan includes a meal plan and exercise program tailored to the user's profile.

[0444] The server then sends the generated weight loss plan to the terminal, which is then presented to the user. This allows the user to receive personalized and specific guidance. Furthermore, products necessary to support the plan are recommended, and the user can purchase them.

[0445] This system also includes a function to collect feedback on the results of implemented weight loss plans. Users record the progress and effectiveness of their plans, and this information is sent back to the server. The server uses this feedback to update its AI model and improve its database. This allows for more accurate suggestions to be made to future users.

[0446] As a concrete example, when a 30-year-old male user inputs his physical characteristics into the system, the server searches for past success stories with similar profiles and generates a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this suggestion, the user can purchase relevant protein products at the terminal. This entire process provides the user with an environment that supports optimal weight loss.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The user uses their device to enter physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information into a form. After completing the input, they operate the confirmation button and send the data to the server.

[0450] Step 2:

[0451] The terminal receives data entered by the user, securely encrypts it, and then sends it to the server. The data is protected using a communication protocol and reaches the server accurately.

[0452] Step 3:

[0453] The server decrypts the user data it receives and begins analysis based on an AI model. Specifically, it calculates indicators such as BMI and basal metabolic rate and performs a detailed analysis of the user's physical characteristics.

[0454] Step 4:

[0455] The server accesses a database of past success stories and searches for cases similar to the analyzed user data. Machine learning algorithms are used to identify the most similar cases.

[0456] Step 5:

[0457] The server generates a statistically reliable weight loss plan based on similar cases. The generated plan includes a meal plan and exercise program, and is optimized for the user.

[0458] Step 6:

[0459] The server sends the generated weight loss plan to the terminal. The terminal receives it and provides an interface to display it in an easy-to-understand manner for the user.

[0460] Step 7:

[0461] The server generates a list of products related to the user's weight loss plan and sends it to the terminal to assist with the purchase. The terminal displays product details and provides access to the purchase process.

[0462] Step 8:

[0463] Users follow a weight loss plan and input their progress and feedback via a terminal. The feedback data is sent to the server through the terminal.

[0464] Step 9:

[0465] The server incorporates the received feedback into the database and retrains the AI ​​model. This creates new success stories, improving the accuracy of future suggestions.

[0466] Step 10:

[0467] After the server updates the AI ​​model and database, it notifies the user of any new information or plan modifications as needed. This ensures that the user always receives optimal support.

[0468] (Example 1)

[0469] 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."

[0470] Modern consumers have access to a vast amount of health information, but they struggle to effectively personalize this information and translate it into concrete action plans. Furthermore, they often lack the means to appropriately select products related to their plans, resulting in ineffective health promotion. Continuously improving the system through user feedback and providing more accurate recommendations in the future is also a crucial challenge.

[0471] 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.

[0472] In this invention, the server includes means for applying a model for analyzing individual user information, means for identifying relevant cases by comparing success story information with the individual user information, and means for creating an appropriate plan based on the relevant cases. This enables the creation of personalized health plans, precise product recommendations based on user characteristics and preferences, and continuous improvement of the system based on feedback.

[0473] "Personalized user information" refers to information about a user's height, weight, age, dietary preferences, allergies, and health status, and is data used to form an individual user profile.

[0474] "Means of applying a model for analysis" refers to a method for performing a process of analyzing data using artificial intelligence technology based on individual user information and extracting features.

[0475] "Success story information" refers to data on cases in the past where users with similar characteristics and conditions successfully lost weight or improved their health.

[0476] "Methods for identifying relevant cases" refers to the process of comparing individual user information with success story information to calculate similarity and identify highly relevant cases.

[0477] "Means of creating an appropriate plan" refers to methods for creating individualized health promotion plans optimized for users, based on identified relevant cases.

[0478] "Product recommendation" refers to the process of providing a list of products that take into account individual preferences and health conditions in order to support users in implementing their plans.

[0479] "Continuous system improvement based on feedback" refers to a method of improving the system's performance and the accuracy of its suggestions by using user feedback and evaluations after the implementation of a plan.

[0480] To implement the invention, the user must first use an appropriate terminal. This terminal is internet-connected and provides an interface for receiving user input. The user inputs their physical characteristics information using, for example, a smartphone or personal computer. The input information includes data such as height, weight, age, dietary preferences, allergies, and health status.

[0481] After receiving this input information, the terminal formats the data and sends it to the server. This server runs on a high-performance cloud computer and uses software such as Python and TensorFlow to analyze the data using generative AI models. The AI ​​model compares the user's information with success stories to develop an optimal, personalized weight loss plan or health program. The server performs similarity calculations to identify highly relevant success stories.

[0482] The plan generated by the server is presented to the user via the terminal, allowing the user to receive a specific health improvement plan. This plan includes details of suggested meals and exercises, as well as recommendations for related products. Based on the information received, the user can select products and purchase them through the terminal.

[0483] As a concrete example, consider a 30-year-old male user who inputs his personal information into the system. Based on this information, the server searches for past success stories and proposes a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this proposal, a suitable protein product is recommended, and the user can purchase it through the terminal.

[0484] The following are examples of prompt messages in the system.

[0485] "A 30-year-old man is asking the AI ​​system for a weight loss plan that includes a high-protein, low-carbohydrate meal plan. Please suggest a solution that includes running three times a week and tracking its effective progress."

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user inputs their physical characteristics into the terminal. This data includes height, weight, age, dietary preferences, allergies, and health status. The terminal receives this information through the user interface, formats it, and prepares it for transmission to the server. At this stage, the input is raw user data, while the output is formatted digital data.

[0489] Step 2:

[0490] The server receives formatted digital data sent from the terminal. The server performs data preprocessing, including data cleaning and handling of missing values. This process transforms the data into a format necessary for analysis, making it ready for the AI ​​model to receive. The input is formatted data from the terminal, and the output is preprocessed, formatted data.

[0491] Step 3:

[0492] The server uses a generative AI model to analyze pre-processed, formatted data. It extracts features from user data using machine learning libraries such as TensorFlow. The server identifies related cases by comparing them with successful case information in the database and calculating similarity. The input is formatted data, and the output is a list of similar cases.

[0493] Step 4:

[0494] Based on similar cases identified by the server, an appropriate health promotion plan is created. The plan includes suggested meal plans and exercise programs. Subsequently, a generative AI model is used to develop the plan and generate personalized suggestions for the user. The input is a list of similar cases, and the output is personalized plan data.

[0495] Step 5:

[0496] The server sends the generated plan data to the terminal. The terminal presents this to the user through a user interface. Furthermore, products related to the plan are recommended in a list format. The terminal visually displays this product information, allowing the user to select and purchase them. The input is the plan data, and the output is the information presented to the user.

[0497] Step 6:

[0498] The user records the progress and effectiveness of their weight loss plan on their device. The device sends this information to the server. The server collects feedback data and updates the system's generating AI model to enhance the database. This establishes a process that improves the accuracy of future suggestions. The input is feedback data, and the output is the updated model and database.

[0499] (Application Example 1)

[0500] 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."

[0501] In modern society, many individuals seek ways to maintain a healthy lifestyle and manage their weight efficiently. However, existing weight loss plans and health support services typically offer generalized approaches, making it difficult to adequately address individual needs. Furthermore, there is a lack of automated means to provide comprehensive health support within the user's living environment. Therefore, there is a need to develop systems that efficiently deliver personalized health plans and are easily implementable at home.

[0502] 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.

[0503] In this invention, the server includes means for inputting the user's physical characteristics information, means for analyzing the physical characteristics information and identifying similar cases by comparing it with past success stories, and means for generating an optimal weight loss plan based on similar cases. This enables the provision of a personalized and healthy weight loss plan and support tailored to individual needs. Furthermore, it provides automated machine means to support health in the user's living environment, suggesting appropriate foods and exercise equipment to facilitate implementation at home.

[0504] "User physical characteristics information" refers to physical and health-related data concerning individual users, such as height, weight, age, dietary preferences, allergy information, and health status.

[0505] "Means of analysis" refer to methods and mechanisms for processing input physical characteristic information and analyzing the data using pattern recognition and statistical techniques.

[0506] "Past success stories" refer to a database of information about weight loss plans that have been successfully implemented by users with similar profiles in the past.

[0507] "Similar cases" refer to cases identified from current users' physical characteristics information and past success stories that share corresponding characteristics and conditions.

[0508] An "optimal weight loss plan" is a personalized diet and exercise plan that is expected to yield maximum results, based on the user's physical characteristics and similar cases.

[0509] "Automated mechanical means that provide health support in the user's living environment" refers to mechanized devices or systems that operate in the user's home or daily environment for the purpose of supporting health maintenance or weight loss.

[0510] "Methods for recommending and encouraging the purchase of products" refers to the process of suggesting appropriate products to users and motivating them to purchase them in order to achieve health improvements related to the generated weight loss plan.

[0511] The "means of updating and learning from the database" refer to a function that stores the results of actually implemented weight loss plans into the database, uses those results through the AI ​​model, and improves the overall accuracy of the system.

[0512] In this invention, the user inputs their physical characteristics information using a dedicated terminal, and this information is transmitted to a central server. The server analyzes the information using an AI analysis engine built on a cloud computing platform and compares it with a database of past success stories. A machine learning model using Python plays a role in processing this data. Once similar cases are identified, the server generates an optimal weight loss plan based on those cases. The generated plan includes an individualized meal plan and exercise program, which is then transmitted to the user terminal.

[0513] In the user's home environment, automated mechanical means support exercise and meal planning. The robot assistant monitors the user's activities in real time and provides feedback via voice and visuals as needed. For example, it can show the user the correct form for exercise or offer suggestions regarding meals. The machine also recommends health-related products and has a function to encourage the purchase of these products via a terminal.

[0514] Furthermore, the results of the weight loss plans implemented by users are returned to the server as feedback, and the AI ​​model uses this as training data to strengthen the database. This will enable more accurate suggestions in the future.

[0515] As a concrete example, suppose a user is following a plan that includes a high-protein diet and running three times a week. In this case, the user can receive guidance on proper form during exercise from a robot assistant and also receive a shopping list of protein-rich foods on their device. An example of a prompt to the generating AI model would be: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Refer to past success stories to suggest the optimal plan."

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] Users input physical characteristics information using a dedicated terminal. This input includes height, weight, age, dietary preferences, allergy information, and health status. This information forms the basis for creating the user's individual profile.

[0519] Step 2:

[0520] The device sends the entered physical characteristics information to the server. The server then passes this information to an AI analysis engine, which begins comparing it with a database. Specifically, a Python-based machine learning model located in the cloud receives the information and prepares it for analysis.

[0521] Step 3:

[0522] The server's AI analysis engine analyzes the input physical characteristics information and compares it to a database of past success stories. This identifies similar cases. The analysis engine uses data-driven pattern recognition and statistical methods to derive the most effective weight loss plan based on the identified cases.

[0523] Step 4:

[0524] The server sends an optimal weight loss plan, generated based on similar cases, to the user's terminal. This plan includes a meal plan and exercise program, tailored to the user's profile. The terminal visually presents this plan to the user, formatting it for easy implementation.

[0525] Step 5:

[0526] In the user's home environment, automated mechanical means support the execution of the weight loss plan. The robotic assistant guides the user according to suggested exercises and diets, providing alerts and supplementary information as needed. During this process, motion feedback and event recording are performed.

[0527] Step 6:

[0528] After completing the weight loss plan, the user enters feedback into their device and sends the results to the server. The server receives this feedback, updates its database, and trains its AI model. This improves the accuracy of future weight loss plans.

[0529] Step 7:

[0530] The process involves inputting a prompt into the AI ​​model to obtain additional suggestions and improvements. For example, the prompt might read: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Referencing past success stories, suggest the optimal plan."

[0531] 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.

[0532] This invention is an innovative system that proposes a weight loss plan based on the user's physical characteristics and also adjusts it to take into account the user's emotional state. This system is implemented using a user, server, terminal, and emotion engine to provide personalized health support.

[0533] First, the user enters their physical characteristics information via a device. The device collects various data about the user, such as height, weight, age, dietary preferences, allergies, and health status. The entered data is encrypted by the device and sent to the server.

[0534] Next, the server analyzes the received data using an AI model. This creates a detailed profile of the user, which is then compared to past success stories in the database. Based on these similar cases, the server generates an optimal weight loss plan and determines specific meal plans and exercise programs.

[0535] Simultaneously, the emotion engine recognizes the user's emotional state in real time. This emotional data reflects the user's current motivation, stress level, and psychological state. The server uses this information to make the weight loss plan more flexible. For example, if the user is under high stress, the plan will be adjusted to focus on lighter exercise and relaxation techniques.

[0536] The server also sends a weight loss plan optimized for the user to the device, which then presents a visualized version of the plan to the user. The user acts according to the plan and reports their feedback and emotional state again through the device. This feedback is sent to the server and re-evaluated by the emotion engine.

[0537] This feedback loop allows users to always receive support best suited to their current state and long-term health goals. The results are used to update the database and retrain the AI ​​model, enabling the system to prepare more refined next suggestions.

[0538] For example, if a user is aiming for healthy weight loss, the emotion engine analyzes the user's emotional data and identifies that their motivation tends to be lower on weekdays. Taking this into account, the server adjusts the plan to focus on exercise on weekends. Furthermore, if stress levels are high, it incorporates meditation or yoga to provide a more sustainable overall plan. In this way, the system provides personalized, adaptive health support based on the user's emotions.

[0539] The following describes the processing flow.

[0540] Step 1:

[0541] The user uses a device to input physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information. The device processes this information, encrypts it, and then sends it to the server.

[0542] Step 2:

[0543] The server decrypts the user data received from the terminal and inputs it into an AI model. The AI ​​model calculates indicators based on physical characteristics and analyzes the user's profile in detail.

[0544] Step 3:

[0545] The server uses the analyzed profile to reference past success stories in the database. Machine learning algorithms are used to identify past cases that most closely resemble the user's profile.

[0546] Step 4:

[0547] The server generates an optimal weight loss plan based on identified similar cases. This plan includes a meal plan and exercise program, customized to the user's specific characteristics.

[0548] Step 5:

[0549] The emotion engine is accessed from the user's device and collects the user's emotional data in real time. This emotional data reflects the user's current state of mind and motivation.

[0550] Step 6:

[0551] The server uses emotional data provided by the emotion engine to further adjust the generated weight loss plan. For example, if motivation is low, it reduces the amount of exercise and increases stress management activities.

[0552] Step 7:

[0553] A customized weight loss plan is sent from the server to the terminal. The terminal displays this plan to the user in an easy-to-understand format.

[0554] Step 8:

[0555] Users follow a plan and input progress and feedback via their device. This includes the amount of exercise performed, what they ate, and changes in their emotions.

[0556] Step 9:

[0557] The device sends user feedback to the server, which records the feedback data in a database. Furthermore, this data, along with sentiment data, is used to retrain the AI ​​model.

[0558] Step 10:

[0559] Based on the learning results, the server provides users with new information and improved weight loss plans as needed. This ensures that users always receive the latest health support.

[0560] (Example 2)

[0561] 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."

[0562] When providing personalized health support plans, the challenge lies in generating more effective and sustainable plans by considering not only the user's physical characteristics but also their real-time emotional state. Conventional systems have struggled to dynamically adjust plans while considering emotional states, and have failed to alleviate the psychological burden on users.

[0563] 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.

[0564] In this invention, the server includes means for encrypting and communicating the user's physical characteristics information, means for using an artificial intelligence model for analyzing the user's physical characteristics information, and means for recognizing the user's emotional state in real time and adjusting the plan accordingly. This makes it possible to provide a personalized plan that takes into account the user's physical and emotional characteristics.

[0565] "User's physical characteristics information" refers to basic data related to individual health management, including an individual's height, weight, age, dietary preferences, allergies, and health status.

[0566] "Encryption" is a technology that uses specific algorithms to transform information in order to enhance data security and prevent unauthorized access.

[0567] An "artificial intelligence model" is a computer program or computational technique used to analyze large amounts of data and find patterns and relationships.

[0568] A "similar case" refers to a past successful case within the database that has characteristics similar to the current user's situation.

[0569] "Emotional state" refers to variables that indicate the user's psychological motivations, stress levels, and emotions such as joy, anger, sadness, and happiness.

[0570] "Adjusting the plan" refers to the action of modifying the generated health support plan based on the user's real-time emotions and feedback in order to provide the most appropriate support.

[0571] "Database updating" is a procedure to keep the accumulated information up-to-date based on new feedback and results from users.

[0572] This invention is a system that provides personalized health support based on the user's physical characteristics and also takes into account their emotional state. This system mainly consists of a user, a server, and a terminal.

[0573] First, the user uses a device to input their physical characteristics information. This includes height, weight, age, dietary preferences, allergies, etc. The device encrypts this data using an encryption library (e.g., common encryption software) and securely transmits it to the server.

[0574] The server receives the transmitted data and analyzes it using a generative AI model. This AI model is built on a common deep learning framework such as TensorFlow and generates a user profile by comparing it to a database of past success stories. Based on this profile, the server creates a customized weight loss plan. The plan includes individual meal plans and exercise programs. The server also uses an emotion engine to acquire real-time emotional data and adjust the plan to take stress levels and motivation into account. The emotion engine uses emotion analysis software (e.g., general emotion recognition software).

[0575] Once a plan is created, the server sends it to the terminal. The terminal then presents this information to the user in a visually easy-to-understand format. For example, the user's weekly exercise schedule can be displayed in a calendar or graph format.

[0576] For example, if a user sets a weight loss goal, and the emotion engine identifies a tendency for low motivation during weekdays, the server will adjust the plan to focus on exercise on weekends. It will also suggest incorporating relaxation activities during periods of high stress. In this way, the system adapts to the user's emotions to provide a sustainable health plan.

[0577] An example of a prompt message might be, "Considering this user's current emotional state, suggest an exercise plan for next weekend." This allows the system to automatically generate appropriate suggestions tailored to the user's needs.

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] Users input physical characteristics information using their device. Specifically, they enter information such as height, weight, age, dietary preferences, allergies, and health status into forms on the application interface. The input data is temporarily stored on the device and securely protected using an encryption algorithm. Encrypted user data is generated as output.

[0581] Step 2:

[0582] The terminal sends encrypted user data to the server. A communication protocol (e.g., HTTPS) is used in this process. The input is encrypted user data, and the output is a secure data transmission to the server.

[0583] Step 3:

[0584] The server decrypts the received data. The decrypted data is input into a generating AI model for analysis and profile generation. This model uses a common deep learning framework and compares past success stories with newly acquired user data. As output, a personalized health profile is generated.

[0585] Step 4:

[0586] The server generates an optimal weight loss plan based on the generated health profile. Using the profile data as input, the AI ​​performs calorie calculations and nutritional analysis. The output is an optimal plan that includes a meal plan and exercise program.

[0587] Step 5:

[0588] In parallel, the server uses an emotion engine to analyze the user's current emotional state in real time. User messages and voice data are used as input data and analyzed by emotion analysis software. The output is data showing the user's real-time emotional state.

[0589] Step 6:

[0590] The server takes emotional data into consideration and adjusts the weight loss plan generated earlier. Depending on the emotional state, it may change the frequency and type of exercise. The inputs are the health profile and emotional data, and the output is the adjusted health support plan.

[0591] Step 7:

[0592] Finally, the server sends the adjusted health support plan to the device. The device receives this and presents it to the user in a visualized format. The display methods vary widely, including graphs, calendars, or reminders. The output provides the user with a health support plan that is visually easy to understand.

[0593] Step 8:

[0594] The user acts based on the presented plan and inputs the results and feedback again on the terminal. The input feedback is encrypted on the terminal and sent to the server. The output is feedback data used to improve the next plan.

[0595] (Application Example 2)

[0596] 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."

[0597] Traditional weight loss plans and health support systems are limited to suggestions based on the user's physical characteristics and are unable to flexibly respond to changes in the user's emotions or daily life. As a result, plans often fail to adapt to the user and are not sustainable. Furthermore, existing systems have difficulty adjusting plans in response to changes in emotional state, resulting in the challenge of maintaining user motivation.

[0598] 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.

[0599] In this invention, the server includes means for analyzing the user's physical characteristics information, means for acquiring emotional state in real time and adjusting the weight loss plan, and means for supporting the user's daily activities with a home robot and collecting feedback. This makes it possible to provide individually optimized health support based on the user's physical information and emotional state.

[0600] "User physical characteristics information" refers to individual data about the user, such as height, weight, age, dietary preferences, allergies, and health status.

[0601] "Means of analysis" refers to methods and technical devices for analyzing acquired data to create a user profile.

[0602] "Means for identifying similar cases" refers to methods and technical devices for comparing past successful cases with current user data to find similarities.

[0603] A "weight loss plan" is a specific schedule of meals and exercise provided to users with the aim of maintaining their health and losing weight.

[0604] "Means of adjustment" refers to a technical process that dynamically revises existing weight loss plans based on emotional information acquired in real time.

[0605] "Household robots" refer to devices and equipment used to support the activities and daily lives of users in their home environment.

[0606] "Feedback" refers to the reactions and comments provided by users, and this data is used to improve and adjust the system.

[0607] To implement this invention, a terminal for user use and a home robot to be installed in the home are prepared. The terminal provides an interface for inputting the user's physical characteristics information and transmits this data encrypted to a server. On the server, an AI model is running to analyze this data and create a user profile. This profile is compared to past success stories in a database and generates an optimal weight loss plan based on similar cases.

[0608] Home robots are equipped with sensors and cameras to acquire emotional states in real time while assisting users with their daily activities. OpenCV and TensorFlow may be used as emotion recognition AI. The emotional information acquired by the robot is sent to a server and used to dynamically adjust the weight loss plan. For example, during periods of high stress, the plan might be modified to recommend lighter exercise or relaxation.

[0609] The weight loss plan and recommended activities generated by the server are visualized and presented to the user via a terminal. The user acts based on this plan and reports feedback on the results and emotional state to the server via a home robot. This feedback is used to improve the AI ​​model and update the database, which will be used to inform future suggestions.

[0610] For example, if a user is aiming for a target weight and the emotional engine identifies a tendency for the user's motivation to be higher on weekends, it may adjust the plan to incorporate intensive exercise on weekends.

[0611] An example of a prompt message for input to the generating AI model is: "Consider the user's current emotional state and provide an optimal health maintenance plan. User data: Height 170cm, Weight 70kg, Age 30, Stress level high, Exercise motivation low."

[0612] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0613] Step 1:

[0614] The terminal receives physical characteristics information from the user as input. The user enters data such as height, weight, age, dietary preferences, allergies, and health status, and then encrypts this data before sending it to the server. The entered information is protected by a data encryption algorithm and transmitted through a secure communication channel.

[0615] Step 2:

[0616] The server analyzes the received physical characteristics information of the user. Using data analysis AI, it builds a user profile and compares it to past success stories in the database. This process uses data mining techniques to recognize patterns in order to identify similar cases. As a result of the analysis, data obtained from similar past cases is output.

[0617] Step 3:

[0618] The server generates an optimal weight loss plan based on similar cases. The AI ​​model takes user-specific physical information and analysis results as input to generate an optimized weight loss plan, including meal plans and exercise programs. This plan generation process uses machine learning algorithms to adjust suggestions based on user preferences. The generated weight loss plan is then output.

[0619] Step 4:

[0620] Home robots use sensors and cameras to acquire the user's emotional state. Real-time emotion recognition AI analyzes facial expressions, tone of voice, and other factors to identify the user's emotions. In this process, the acquired emotional data is used as input, and information such as the user's motivation and stress level is output.

[0621] Step 5:

[0622] The server dynamically adjusts the weight loss plan based on the user's emotional state. Using acquired emotional data as input, it modifies the plan according to the situation, such as whether the user is stressed or highly motivated. This process is carried out through conditional judgment by a generative AI model, and the adjusted weight loss plan is output.

[0623] Step 6:

[0624] The device presents the user with a visualized weight loss plan. Using plan data provided by the server as input, it visualizes it through a user-friendly interface and provides actionable guidance to the user. Based on the displayed information, the user can begin activities in accordance with the plan.

[0625] Step 7:

[0626] The home robot collects user feedback and reports it to a server. Feedback on the progress of the weight loss plan and changes in emotions is entered and resent to the server. This feedback is stored as data to be used to improve the plan in the future.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] This invention is an advanced system that proposes a weight loss plan tailored to a specific user, and is implemented through components consisting of a user, a server, and a terminal. This system applies artificial intelligence technology to analyze the user's physical characteristics and compares them with past success stories to provide a reliable weight loss method.

[0645] First, the user enters their physical characteristics information via a device. This information includes the user's height, weight, age, dietary preferences, allergies, and health status. This establishes an accurate profile of the user.

[0646] This information is then sent to a server, which uses an AI model to analyze the data. The analyzed data is compared to numerous existing success stories in the database. Once similar cases are identified, the weight loss plan with the highest statistical success rate is generated. This plan includes a meal plan and exercise program tailored to the user's profile.

[0647] The server then sends the generated weight loss plan to the terminal, which is then presented to the user. This allows the user to receive personalized and specific guidance. Furthermore, products necessary to support the plan are recommended, and the user can purchase them.

[0648] This system also includes a function to collect feedback on the results of implemented weight loss plans. Users record the progress and effectiveness of their plans, and this information is sent back to the server. The server uses this feedback to update its AI model and improve its database. This allows for more accurate suggestions to be made to future users.

[0649] As a concrete example, when a 30-year-old male user inputs his physical characteristics into the system, the server searches for past success stories with similar profiles and generates a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this suggestion, the user can purchase relevant protein products at the terminal. This entire process provides the user with an environment that supports optimal weight loss.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The user uses their device to enter physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information into a form. After completing the input, they operate the confirmation button and send the data to the server.

[0653] Step 2:

[0654] The terminal receives data entered by the user, securely encrypts it, and then sends it to the server. The data is protected using a communication protocol and reaches the server accurately.

[0655] Step 3:

[0656] The server decrypts the user data it receives and begins analysis based on an AI model. Specifically, it calculates indicators such as BMI and basal metabolic rate and performs a detailed analysis of the user's physical characteristics.

[0657] Step 4:

[0658] The server accesses a database of past success stories and searches for cases similar to the analyzed user data. Machine learning algorithms are used to identify the most similar cases.

[0659] Step 5:

[0660] The server generates a statistically reliable weight loss plan based on similar cases. The generated plan includes a meal plan and exercise program, and is optimized for the user.

[0661] Step 6:

[0662] The server sends the generated weight loss plan to the terminal. The terminal receives it and provides an interface to display it in an easy-to-understand manner for the user.

[0663] Step 7:

[0664] The server generates a list of products related to the user's weight loss plan and sends it to the terminal to assist with the purchase. The terminal displays product details and provides access to the purchase process.

[0665] Step 8:

[0666] Users follow a weight loss plan and input their progress and feedback via a terminal. The feedback data is sent to the server through the terminal.

[0667] Step 9:

[0668] The server incorporates the received feedback into the database and retrains the AI ​​model. This creates new success stories, improving the accuracy of future suggestions.

[0669] Step 10:

[0670] After the server updates the AI ​​model and database, it notifies the user of any new information or plan modifications as needed. This ensures that the user always receives optimal support.

[0671] (Example 1)

[0672] 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".

[0673] Modern consumers have access to a vast amount of health information, but they struggle to effectively personalize this information and translate it into concrete action plans. Furthermore, they often lack the means to appropriately select products related to their plans, resulting in ineffective health promotion. Continuously improving the system through user feedback and providing more accurate recommendations in the future is also a crucial challenge.

[0674] 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.

[0675] In this invention, the server includes means for applying a model for analyzing individual user information, means for identifying relevant cases by comparing success story information with the individual user information, and means for creating an appropriate plan based on the relevant cases. This enables the creation of personalized health plans, precise product recommendations based on user characteristics and preferences, and continuous improvement of the system based on feedback.

[0676] "Personalized user information" refers to information about a user's height, weight, age, dietary preferences, allergies, and health status, and is data used to form an individual user profile.

[0677] "Means of applying a model for analysis" refers to a method for performing a process of analyzing data using artificial intelligence technology based on individual user information and extracting features.

[0678] "Success story information" refers to data on cases in the past where users with similar characteristics and conditions successfully lost weight or improved their health.

[0679] "Methods for identifying relevant cases" refers to the process of comparing individual user information with success story information to calculate similarity and identify highly relevant cases.

[0680] "Means of creating an appropriate plan" refers to methods for creating individualized health promotion plans optimized for users, based on identified relevant cases.

[0681] "Product recommendation" refers to the process of providing a list of products that take into account individual preferences and health conditions in order to support users in implementing their plans.

[0682] "Continuous system improvement based on feedback" refers to a method of improving the system's performance and the accuracy of its suggestions by using user feedback and evaluations after the implementation of a plan.

[0683] To implement the invention, the user must first use an appropriate terminal. This terminal is internet-connected and provides an interface for receiving user input. The user inputs their physical characteristics information using, for example, a smartphone or personal computer. The input information includes data such as height, weight, age, dietary preferences, allergies, and health status.

[0684] After receiving this input information, the terminal formats the data and sends it to the server. This server runs on a high-performance cloud computer and uses software such as Python and TensorFlow to analyze the data using generative AI models. The AI ​​model compares the user's information with success stories to develop an optimal, personalized weight loss plan or health program. The server performs similarity calculations to identify highly relevant success stories.

[0685] The plan generated by the server is presented to the user via the terminal, allowing the user to receive a specific health improvement plan. This plan includes details of suggested meals and exercises, as well as recommendations for related products. Based on the information received, the user can select products and purchase them through the terminal.

[0686] As a concrete example, consider a 30-year-old male user who inputs his personal information into the system. Based on this information, the server searches for past success stories and proposes a high-protein, low-carbohydrate meal plan and an exercise program including three runs per week. Based on this proposal, a suitable protein product is recommended, and the user can purchase it through the terminal.

[0687] The following are examples of prompt messages in the system.

[0688] "A 30-year-old man is asking the AI ​​system for a weight loss plan that includes a high-protein, low-carbohydrate meal plan. Please suggest a solution that includes running three times a week and tracking its effective progress."

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The user inputs their physical characteristics into the terminal. This data includes height, weight, age, dietary preferences, allergies, and health status. The terminal receives this information through the user interface, formats it, and prepares it for transmission to the server. At this stage, the input is raw user data, while the output is formatted digital data.

[0692] Step 2:

[0693] The server receives formatted digital data sent from the terminal. The server performs data preprocessing, including data cleaning and handling of missing values. This process transforms the data into a format necessary for analysis, making it ready for the AI ​​model to receive. The input is formatted data from the terminal, and the output is preprocessed, formatted data.

[0694] Step 3:

[0695] The server uses a generative AI model to analyze pre-processed, formatted data. It extracts features from user data using machine learning libraries such as TensorFlow. The server identifies related cases by comparing them with successful case information in the database and calculating similarity. The input is formatted data, and the output is a list of similar cases.

[0696] Step 4:

[0697] Based on similar cases identified by the server, an appropriate health promotion plan is created. The plan includes suggested meal plans and exercise programs. Subsequently, a generative AI model is used to develop the plan and generate personalized suggestions for the user. The input is a list of similar cases, and the output is personalized plan data.

[0698] Step 5:

[0699] The server sends the generated plan data to the terminal. The terminal presents this to the user through a user interface. Furthermore, products related to the plan are recommended in a list format. The terminal visually displays this product information, allowing the user to select and purchase them. The input is the plan data, and the output is the information presented to the user.

[0700] Step 6:

[0701] The user records the progress and effectiveness of their weight loss plan on their device. The device sends this information to the server. The server collects feedback data and updates the system's generating AI model to enhance the database. This establishes a process that improves the accuracy of future suggestions. The input is feedback data, and the output is the updated model and database.

[0702] (Application Example 1)

[0703] 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".

[0704] In modern society, many individuals seek ways to maintain a healthy lifestyle and manage their weight efficiently. However, existing weight loss plans and health support services typically offer generalized approaches, making it difficult to adequately address individual needs. Furthermore, there is a lack of automated means to provide comprehensive health support within the user's living environment. Therefore, there is a need to develop systems that efficiently deliver personalized health plans and are easily implementable at home.

[0705] 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.

[0706] In this invention, the server includes means for inputting the user's physical characteristics information, means for analyzing the physical characteristics information and identifying similar cases by comparing it with past success stories, and means for generating an optimal weight loss plan based on similar cases. This enables the provision of a personalized and healthy weight loss plan and support tailored to individual needs. Furthermore, it provides automated machine means to support health in the user's living environment, suggesting appropriate foods and exercise equipment to facilitate implementation at home.

[0707] "User physical characteristics information" refers to physical and health-related data concerning individual users, such as height, weight, age, dietary preferences, allergy information, and health status.

[0708] "Means of analysis" refer to methods and mechanisms for processing input physical characteristic information and analyzing the data using pattern recognition and statistical techniques.

[0709] "Past success stories" refer to a database of information about weight loss plans that have been successfully implemented by users with similar profiles in the past.

[0710] "Similar cases" refer to cases identified from current users' physical characteristics information and past success stories that share corresponding characteristics and conditions.

[0711] An "optimal weight loss plan" is a personalized diet and exercise plan that is expected to yield maximum results, based on the user's physical characteristics and similar cases.

[0712] "Automated mechanical means that provide health support in the user's living environment" refers to mechanized devices or systems that operate in the user's home or daily environment for the purpose of supporting health maintenance or weight loss.

[0713] "Methods for recommending and encouraging the purchase of products" refers to the process of suggesting appropriate products to users and motivating them to purchase them in order to achieve health improvements related to the generated weight loss plan.

[0714] The "means of updating and learning from the database" refer to a function that stores the results of actually implemented weight loss plans into the database, uses those results through the AI ​​model, and improves the overall accuracy of the system.

[0715] In this invention, the user inputs their physical characteristics information using a dedicated terminal, and this information is transmitted to a central server. The server analyzes the information using an AI analysis engine built on a cloud computing platform and compares it with a database of past success stories. A machine learning model using Python plays a role in processing this data. Once similar cases are identified, the server generates an optimal weight loss plan based on those cases. The generated plan includes an individualized meal plan and exercise program, which is then transmitted to the user terminal.

[0716] In the user's home environment, automated mechanical means support exercise and meal planning. The robot assistant monitors the user's activities in real time and provides feedback via voice and visuals as needed. For example, it can show the user the correct form for exercise or offer suggestions regarding meals. The machine also recommends health-related products and has a function to encourage the purchase of these products via a terminal.

[0717] Furthermore, the results of the weight loss plans implemented by users are returned to the server as feedback, and the AI ​​model uses this as training data to strengthen the database. This will enable more accurate suggestions in the future.

[0718] As a concrete example, suppose a user is following a plan that includes a high-protein diet and running three times a week. In this case, the user can receive guidance on proper form during exercise from a robot assistant and also receive a shopping list of protein-rich foods on their device. An example of a prompt to the generating AI model would be: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Refer to past success stories to suggest the optimal plan."

[0719] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0720] Step 1:

[0721] Users input physical characteristics information using a dedicated terminal. This input includes height, weight, age, dietary preferences, allergy information, and health status. This information forms the basis for creating the user's individual profile.

[0722] Step 2:

[0723] The device sends the entered physical characteristics information to the server. The server then passes this information to an AI analysis engine, which begins comparing it with a database. Specifically, a Python-based machine learning model located in the cloud receives the information and prepares it for analysis.

[0724] Step 3:

[0725] The server's AI analysis engine analyzes the input physical characteristics information and compares it to a database of past success stories. This identifies similar cases. The analysis engine uses data-driven pattern recognition and statistical methods to derive the most effective weight loss plan based on the identified cases.

[0726] Step 4:

[0727] The server sends an optimal weight loss plan, generated based on similar cases, to the user's terminal. This plan includes a meal plan and exercise program, tailored to the user's profile. The terminal visually presents this plan to the user, formatting it for easy implementation.

[0728] Step 5:

[0729] In the user's home environment, automated mechanical means support the execution of the weight loss plan. The robotic assistant guides the user according to suggested exercises and diets, providing alerts and supplementary information as needed. During this process, motion feedback and event recording are performed.

[0730] Step 6:

[0731] After completing the weight loss plan, the user enters feedback into their device and sends the results to the server. The server receives this feedback, updates its database, and trains its AI model. This improves the accuracy of future weight loss plans.

[0732] Step 7:

[0733] The process involves inputting a prompt into the AI ​​model to obtain additional suggestions and improvements. For example, the prompt might read: "Develop an AI strategy to provide a customized weight loss plan for a 30-year-old male. Health data includes height, weight, and dietary preferences. Referencing past success stories, suggest the optimal plan."

[0734] 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.

[0735] This invention is an innovative system that proposes a weight loss plan based on the user's physical characteristics and also adjusts it to take into account the user's emotional state. This system is implemented using a user, server, terminal, and emotion engine to provide personalized health support.

[0736] First, the user enters their physical characteristics information via a device. The device collects various data about the user, such as height, weight, age, dietary preferences, allergies, and health status. The entered data is encrypted by the device and sent to the server.

[0737] Next, the server analyzes the received data using an AI model. This creates a detailed profile of the user, which is then compared to past success stories in the database. Based on these similar cases, the server generates an optimal weight loss plan and determines specific meal plans and exercise programs.

[0738] Simultaneously, the emotion engine recognizes the user's emotional state in real time. This emotional data reflects the user's current motivation, stress level, and psychological state. The server uses this information to make the weight loss plan more flexible. For example, if the user is under high stress, the plan will be adjusted to focus on lighter exercise and relaxation techniques.

[0739] The server also sends a weight loss plan optimized for the user to the device, which then presents a visualized version of the plan to the user. The user acts according to the plan and reports their feedback and emotional state again through the device. This feedback is sent to the server and re-evaluated by the emotion engine.

[0740] This feedback loop allows users to always receive support best suited to their current state and long-term health goals. The results are used to update the database and retrain the AI ​​model, enabling the system to prepare more refined next suggestions.

[0741] For example, if a user is aiming for healthy weight loss, the emotion engine analyzes the user's emotional data and identifies that their motivation tends to be lower on weekdays. Taking this into account, the server adjusts the plan to focus on exercise on weekends. Furthermore, if stress levels are high, it incorporates meditation or yoga to provide a more sustainable overall plan. In this way, the system provides personalized, adaptive health support based on the user's emotions.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] The user uses a device to input physical characteristics information such as body shape, weight, age, health status, dietary preferences, and allergy information. The device processes this information, encrypts it, and then sends it to the server.

[0745] Step 2:

[0746] The server decrypts the user data received from the terminal and inputs it into an AI model. The AI ​​model calculates indicators based on physical characteristics and analyzes the user's profile in detail.

[0747] Step 3:

[0748] The server uses the analyzed profile to reference past success stories in the database. Machine learning algorithms are used to identify past cases that most closely resemble the user's profile.

[0749] Step 4:

[0750] The server generates an optimal weight loss plan based on identified similar cases. This plan includes a meal plan and exercise program, customized to the user's specific characteristics.

[0751] Step 5:

[0752] The emotion engine is accessed from the user's device and collects the user's emotional data in real time. This emotional data reflects the user's current state of mind and motivation.

[0753] Step 6:

[0754] The server uses emotional data provided by the emotion engine to further adjust the generated weight loss plan. For example, if motivation is low, it reduces the amount of exercise and increases stress management activities.

[0755] Step 7:

[0756] A customized weight loss plan is sent from the server to the terminal. The terminal displays this plan to the user in an easy-to-understand format.

[0757] Step 8:

[0758] Users follow a plan and input progress and feedback via their device. This includes the amount of exercise performed, what they ate, and changes in their emotions.

[0759] Step 9:

[0760] The device sends user feedback to the server, which records the feedback data in a database. Furthermore, this data, along with sentiment data, is used to retrain the AI ​​model.

[0761] Step 10:

[0762] Based on the learning results, the server provides users with new information and improved weight loss plans as needed. This ensures that users always receive the latest health support.

[0763] (Example 2)

[0764] 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".

[0765] When providing personalized health support plans, the challenge lies in generating more effective and sustainable plans by considering not only the user's physical characteristics but also their real-time emotional state. Conventional systems have struggled to dynamically adjust plans while considering emotional states, and have failed to alleviate the psychological burden on users.

[0766] 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.

[0767] In this invention, the server includes means for encrypting and communicating the user's physical characteristics information, means for using an artificial intelligence model for analyzing the user's physical characteristics information, and means for recognizing the user's emotional state in real time and adjusting the plan accordingly. This makes it possible to provide a personalized plan that takes into account the user's physical and emotional characteristics.

[0768] "User's physical characteristics information" refers to basic data related to individual health management, including an individual's height, weight, age, dietary preferences, allergies, and health status.

[0769] "Encryption" is a technology that uses specific algorithms to transform information in order to enhance data security and prevent unauthorized access.

[0770] An "artificial intelligence model" is a computer program or computational technique used to analyze large amounts of data and find patterns and relationships.

[0771] A "similar case" refers to a past successful case within the database that has characteristics similar to the current user's situation.

[0772] "Emotional state" refers to variables that indicate the user's psychological motivations, stress levels, and emotions such as joy, anger, sadness, and happiness.

[0773] "Adjusting the plan" refers to the action of modifying the generated health support plan based on the user's real-time emotions and feedback in order to provide the most appropriate support.

[0774] "Database updating" is a procedure to keep the accumulated information up-to-date based on new feedback and results from users.

[0775] This invention is a system that provides personalized health support based on the user's physical characteristics and also takes into account their emotional state. This system mainly consists of a user, a server, and a terminal.

[0776] First, the user uses a device to input their physical characteristics information. This includes height, weight, age, dietary preferences, allergies, etc. The device encrypts this data using an encryption library (e.g., common encryption software) and securely transmits it to the server.

[0777] The server receives the transmitted data and analyzes it using a generative AI model. This AI model is built on a common deep learning framework such as TensorFlow and generates a user profile by comparing it to a database of past success stories. Based on this profile, the server creates a customized weight loss plan. The plan includes individual meal plans and exercise programs. The server also uses an emotion engine to acquire real-time emotional data and adjust the plan to take stress levels and motivation into account. The emotion engine uses emotion analysis software (e.g., general emotion recognition software).

[0778] Once a plan is created, the server sends it to the terminal. The terminal then presents this information to the user in a visually easy-to-understand format. For example, the user's weekly exercise schedule can be displayed in a calendar or graph format.

[0779] For example, if a user sets a weight loss goal, and the emotion engine identifies a tendency for low motivation during weekdays, the server will adjust the plan to focus on exercise on weekends. It will also suggest incorporating relaxation activities during periods of high stress. In this way, the system adapts to the user's emotions to provide a sustainable health plan.

[0780] An example of a prompt message might be, "Considering this user's current emotional state, suggest an exercise plan for next weekend." This allows the system to automatically generate appropriate suggestions tailored to the user's needs.

[0781] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0782] Step 1:

[0783] Users input physical characteristics information using their device. Specifically, they enter information such as height, weight, age, dietary preferences, allergies, and health status into forms on the application interface. The input data is temporarily stored on the device and securely protected using an encryption algorithm. Encrypted user data is generated as output.

[0784] Step 2:

[0785] The terminal sends encrypted user data to the server. A communication protocol (e.g., HTTPS) is used in this process. The input is encrypted user data, and the output is a secure data transmission to the server.

[0786] Step 3:

[0787] The server decrypts the received data. The decrypted data is input into a generating AI model for analysis and profile generation. This model uses a common deep learning framework and compares past success stories with newly acquired user data. As output, a personalized health profile is generated.

[0788] Step 4:

[0789] The server generates an optimal weight loss plan based on the generated health profile. Using the profile data as input, the AI ​​performs calorie calculations and nutritional analysis. The output is an optimal plan that includes a meal plan and exercise program.

[0790] Step 5:

[0791] In parallel, the server uses an emotion engine to analyze the user's current emotional state in real time. User messages and voice data are used as input data and analyzed by emotion analysis software. The output is data indicating the user's real-time emotional state.

[0792] Step 6:

[0793] The server takes emotional data into consideration and adjusts the weight loss plan generated earlier. Depending on the emotional state, it may change the frequency and type of exercise. The inputs are the health profile and emotional data, and the output is the adjusted health support plan.

[0794] Step 7:

[0795] Finally, the server sends the adjusted health support plan to the device. The device receives this and presents it to the user in a visualized format. The display methods vary widely, including graphs, calendars, or reminders. The output provides the user with a health support plan that is visually easy to understand.

[0796] Step 8:

[0797] The user acts based on the presented plan and inputs the results and feedback again on the terminal. The input feedback is encrypted on the terminal and sent to the server. The output is feedback data used to improve the next plan.

[0798] (Application Example 2)

[0799] 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".

[0800] Traditional weight loss plans and health support systems are limited to suggestions based on the user's physical characteristics and are unable to flexibly respond to changes in the user's emotions or daily life. As a result, plans often fail to adapt to the user and are not sustainable. Furthermore, existing systems have difficulty adjusting plans in response to changes in emotional state, resulting in the challenge of maintaining user motivation.

[0801] 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.

[0802] In this invention, the server includes means for analyzing the user's physical characteristics information, means for acquiring emotional state in real time and adjusting the weight loss plan, and means for supporting the user's daily activities with a home robot and collecting feedback. This makes it possible to provide individually optimized health support based on the user's physical information and emotional state.

[0803] "User physical characteristics information" refers to individual data about the user, such as height, weight, age, dietary preferences, allergies, and health status.

[0804] "Means of analysis" refers to methods and technical devices for analyzing acquired data to create a user profile.

[0805] "Means for identifying similar cases" refers to methods and technical devices for comparing past successful cases with current user data to find similarities.

[0806] A "weight loss plan" is a specific schedule of meals and exercise provided to users with the aim of maintaining their health and losing weight.

[0807] "Means of adjustment" refers to a technical process that dynamically revises existing weight loss plans based on emotional information acquired in real time.

[0808] "Household robots" refer to devices and equipment used to support the activities and daily lives of users in their home environment.

[0809] "Feedback" refers to the reactions and comments provided by users, and this data is used to improve and adjust the system.

[0810] To implement this invention, a terminal for user use and a home robot to be installed in the home are prepared. The terminal provides an interface for inputting the user's physical characteristics information and transmits this data encrypted to a server. On the server, an AI model is running to analyze this data and create a user profile. This profile is compared to past success stories in a database and generates an optimal weight loss plan based on similar cases.

[0811] Home robots are equipped with sensors and cameras to acquire emotional states in real time while assisting users with their daily activities. OpenCV and TensorFlow may be used as emotion recognition AI. The emotional information acquired by the robot is sent to a server and used to dynamically adjust the weight loss plan. For example, during periods of high stress, the plan might be modified to recommend lighter exercise or relaxation.

[0812] The weight loss plan and recommended activities generated by the server are visualized and presented to the user via a terminal. The user acts based on this plan and reports feedback on the results and emotional state to the server via a home robot. This feedback is used to improve the AI ​​model and update the database, which will be used to inform future suggestions.

[0813] For example, if a user is aiming for a target weight and the emotional engine identifies a tendency for the user's motivation to be higher on weekends, it may adjust the plan to incorporate intensive exercise on weekends.

[0814] An example of a prompt message for input to the generating AI model is: "Consider the user's current emotional state and provide an optimal health maintenance plan. User data: Height 170cm, Weight 70kg, Age 30, Stress level high, Exercise motivation low."

[0815] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0816] Step 1:

[0817] The terminal receives physical characteristics information from the user as input. The user enters data such as height, weight, age, dietary preferences, allergies, and health status, and then encrypts this data before sending it to the server. The entered information is protected by a data encryption algorithm and transmitted through a secure communication channel.

[0818] Step 2:

[0819] The server analyzes the received physical characteristics information of the user. Using data analysis AI, it builds a user profile and compares it to past success stories in the database. This process uses data mining techniques to recognize patterns in order to identify similar cases. As a result of the analysis, data obtained from similar past cases is output.

[0820] Step 3:

[0821] The server generates an optimal weight loss plan based on similar cases. The AI ​​model takes user-specific physical information and analysis results as input to generate an optimized weight loss plan, including meal plans and exercise programs. This plan generation process uses machine learning algorithms to adjust suggestions based on user preferences. The generated weight loss plan is then output.

[0822] Step 4:

[0823] Home robots use sensors and cameras to acquire the user's emotional state. Real-time emotion recognition AI analyzes facial expressions, tone of voice, and other factors to identify the user's emotions. In this process, the acquired emotional data is used as input, and information such as the user's motivation and stress level is output.

[0824] Step 5:

[0825] The server dynamically adjusts the weight loss plan based on the user's emotional state. Using acquired emotional data as input, it modifies the plan according to the situation, such as whether the user is stressed or highly motivated. This process is carried out through conditional judgment by a generative AI model, and the adjusted weight loss plan is output.

[0826] Step 6:

[0827] The device presents the user with a visualized weight loss plan. Using plan data provided by the server as input, it visualizes it through a user-friendly interface and provides actionable guidance to the user. Based on the displayed information, the user can begin activities in accordance with the plan.

[0828] Step 7:

[0829] The home robot collects user feedback and reports it to a server. Feedback on the progress of the weight loss plan and changes in emotions is entered and resent to the server. This feedback is stored as data to be used to improve the plan in the future.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] A means for inputting the user's physical characteristics information,

[0854] A means for analyzing the physical characteristics information of the user,

[0855] A means of identifying similar cases by comparing past success stories with the physical characteristics information of users,

[0856] A means for generating an optimal weight loss plan based on the aforementioned similar cases,

[0857] A means for presenting the aforementioned weight loss plan to the user,

[0858] A means of recommending and encouraging the purchase of products related to the aforementioned weight loss plan,

[0859] A means for updating and learning from the results of the aforementioned weight loss plan,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, further comprising means for generating an exercise program tailored to the user based on the user's physical characteristics information.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising means for selecting products based on user preferences and health status in the recommendation of the aforementioned products.

[0865] "Example 1"

[0866] (Claim 1)

[0867] A means for entering individual user information,

[0868] Means for applying a model for analyzing the individual information of the aforementioned users,

[0869] A means of identifying related cases by comparing success story information with the individual information of the aforementioned users,

[0870] Means for creating an appropriate plan based on the aforementioned related cases,

[0871] Means for providing the aforementioned plan to users,

[0872] Means for recommending and encouraging the acquisition of products related to the aforementioned plan,

[0873] A means of updating and adapting the information set based on feedback from the implementation of the aforementioned plan,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for creating exercise instruction for a user based on the user's individual information.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for making a selection that takes into account user preference information and health conditions in recommending the aforementioned product.

[0879] "Application Example 1"

[0880] (Claim 1)

[0881] A means of inputting the user's physical characteristics information,

[0882] A means for analyzing the physical characteristics information of the user,

[0883] A means of identifying similar cases by comparing past success stories with the physical characteristics information of users,

[0884] A means for generating an optimal weight loss plan based on the aforementioned similar cases,

[0885] A means for presenting the aforementioned weight loss plan to the user,

[0886] A means of recommending and encouraging the purchase of products related to the aforementioned weight loss plan,

[0887] A means for updating and learning from the results of the aforementioned weight loss plan,

[0888] An automated mechanical means that provides health support in the user's living environment,

[0889] A means of suggesting appropriate foods and exercise equipment based on the aforementioned health support,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, further comprising means for generating an exercise program tailored to the user based on the user's physical characteristics information.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising means for selecting products based on user preferences and health status in the recommendation of the aforementioned products.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] A means of inputting the user's physical characteristics information,

[0898] A means for encrypting and communicating the user's physical characteristics information,

[0899] A means of using an artificial intelligence model to analyze the physical characteristics information of the user,

[0900] A means of identifying similar cases by comparing past success stories with the physical characteristics information of users,

[0901] Means for generating an optimal plan based on the aforementioned similar cases,

[0902] A means of recognizing the user's emotional state in real time and adjusting the plan accordingly,

[0903] A means of presenting the aforementioned plan to the user,

[0904] Means of recommending and encouraging the purchase of products related to the aforementioned plan,

[0905] A means of updating the database and performing learning based on the results of the aforementioned plan,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, further comprising means for generating an exercise program tailored to the user based on the user's physical characteristics and emotional state.

[0909] (Claim 3)

[0910] The system according to claim 1, further comprising means for selecting products that take into account the user's preferences and emotional state in the recommendation of the aforementioned products.

[0911] "Application example 2 when combining with an emotional engine"

[0912] (Claim 1)

[0913] A means for inputting the user's physical characteristics information,

[0914] A means for analyzing the physical characteristics information of the user,

[0915] A means of identifying similar cases by comparing past success stories with the physical characteristics information of users,

[0916] A means for generating an optimal weight loss plan based on the aforementioned similar cases,

[0917] A means for presenting the aforementioned weight loss plan to the user,

[0918] A means of recommending and encouraging the purchase of products related to the aforementioned weight loss plan,

[0919] A means for updating and learning from the results of the aforementioned weight loss plan,

[0920] A means for acquiring the user's emotional state in real time and adjusting the weight loss plan accordingly,

[0921] A means for collecting user feedback when a home robot assists the user's daily activities and providing it to the server,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, further comprising means for generating an exercise program tailored to the user based on the user's physical characteristics information.

[0925] (Claim 3)

[0926] The system according to claim 1, further comprising means for selecting products based on user preferences and health status in the recommendation of the aforementioned products. [Explanation of Symbols]

[0927] 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. A means of inputting the user's physical characteristics information, A means for analyzing the physical characteristics information of the user, A means of identifying similar cases by comparing past success stories with the physical characteristics information of users, A means for generating an optimal weight loss plan based on the aforementioned similar cases, A means for presenting the aforementioned weight loss plan to the user, A means of recommending and encouraging the purchase of products related to the aforementioned weight loss plan, A means for updating and learning from the results of the aforementioned weight loss plan, An automated mechanical means that provides health support in the user's living environment, A means of suggesting appropriate foods and exercise equipment based on the aforementioned health support, A system that includes this.

2. The system according to claim 1, further comprising means for generating an exercise program tailored to the user based on the user's physical characteristics information.

3. The system according to claim 1, further comprising means for selecting products based on user preferences and health status.

Images