system

Abstract

We provide the system. [Solution] Means for collecting personal data from user devices, The means for transmitting the collected data to a server device and storing the data, A means for analyzing the aforementioned accumulated data and learning each user's lifestyle habits, A means for generating improvement measures or advice suitable for the user based on the aforementioned learning results, A system including means for notifying the user device of the generated advice.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern AI systems rely on user inputs and prompts and cannot fully meet individual needs. Also, it is difficult to grasp the entire lifestyle of users and provide consistent advice for improving the quality of life from a long-term perspective. In such a situation, there is a demand for a personal AI system that can precisely understand users' behaviors and health conditions and respond immediately to daily changes.

Means for Solving the Problems

[0005] This invention provides a system that learns the user's lifestyle habits by collecting personal data from the user's device and transmitting it to a server device for storage. The system analyzes the stored data to generate individually tailored advice and notifies the user's device. Furthermore, it receives feedback from the user and adjusts the diagnostic and analysis algorithms based on that feedback to provide continuously optimized improvement measures. Through these means, the system utilizes data such as health status, behavioral history, and hobbies to provide comprehensive life support, encompassing finance, career, and family relationships.

[0006] A "user device" refers to a device, such as a smartphone or wearable device used by a user, that has the function of collecting personal data and transmitting it to a server device.

[0007] A "server device" is a computer system that receives data transmitted from a user device, stores and analyzes it to generate advice tailored to the user, and then transmits it back to the user device.

[0008] "Means of data collection" refers to hardware and software functions installed on user devices that acquire information on the user's behavioral history and health status.

[0009] "Means of storage" refers to a function that includes the process of saving received data in a database so that it can be analyzed and referenced as needed.

[0010] "Means of analysis" refers to a function within a system that uses accumulated data to analyze and understand user patterns and trends.

[0011] "Means for generating advice" refers to a function that creates specific content to suggest actions and improvement measures to the user based on the analysis results.

[0012] A "means for receiving feedback" refers to a function that receives opinions on usability and effectiveness transmitted from user devices and uses them to improve the system.

[0013] "Means for adjusting the analysis algorithm" refers to a system component that processes data analysis methods to improve accuracy by appropriately modifying them based on the received feedback. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 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

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a personal support system realized through the cooperation of a user device and a server device. It primarily collects, analyzes, and generates advice on everyday data to support the user's daily life.

[0036] First, users record their daily activities and health information using smartphones or wearable devices (user devices). This allows for the acquisition of various data, such as steps taken, heart rate, activity history, and location information.

[0037] This user device transmits collected data to a server device via the internet. The server receives this data and securely stores it in a database. The server uses machine learning algorithms to analyze the stored data and understand the user's lifestyle, health status, hobbies, and preferences.

[0038] Next, the server generates personalized health advice and lifestyle improvement suggestions based on the analysis results. This advice might include, for example, providing an efficient exercise plan that takes into account the user's daily activity patterns, or suggesting a nutritionally balanced diet.

[0039] Once advice is generated, the server notifies the user device. The user device then assists in the execution of this advice by informing the user and, if necessary, incorporating it into the schedule.

[0040] Users can send feedback on the advice they receive through their device. The server incorporates this feedback into its analysis and adjusts the algorithm accordingly to improve the accuracy of the advice. This cyclical feedback process allows the system to evolve in a way that is more adapted to the user's needs.

[0041] For example, if a user sets weight loss as their goal, the system analyzes the user's lack of exercise and eating patterns, and proposes a meal plan based on calorie balance. Furthermore, it incorporates alerts into the schedule to encourage increased daily exercise, creating an environment that makes it easier for the user to achieve their goal.

[0042] Thus, the system of the present invention aims to function as a platform that supports the user's overall lifestyle and facilitates long-term improvement in their quality of life.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device continuously collects data such as the user's heart rate, steps taken, and application usage information using sensors. This data is appropriately formatted according to the user's active time periods.

[0046] Step 2:

[0047] The device sends collected data to the server at regular intervals. This data includes the user's activity history and current location information.

[0048] Step 3:

[0049] The server stores the data received from the terminal in a database. Here, the data is organized into categories such as health information, location information, and activity history.

[0050] Step 4:

[0051] The server begins analyzing the accumulated data using machine learning algorithms. It primarily identifies user behavior patterns and extracts trends in specific health conditions and hobbies.

[0052] Step 5:

[0053] Based on the analysis results, the server generates personalized lifestyle improvement advice for each user. In this process, it develops plans based on past data to contribute to future lifestyle improvements and incorporates them into the advice.

[0054] Step 6:

[0055] The server sends the generated advice to the device. The device displays the advice as a notification and, if necessary, adds an action plan to its schedule.

[0056] Step 7:

[0057] Users input their thoughts and impressions about the advice they receive as feedback from their device. This feedback is then sent from the device to the server.

[0058] Step 8:

[0059] The server analyzes user feedback and uses it to further refine its analysis algorithm. This cycle ensures that subsequent advice generation becomes more accurate.

[0060] (Example 1)

[0061] 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."

[0062] With the increasing diversification of living environments in recent years, there is a growing demand for personalized lifestyle improvement advice and health support. However, traditional approaches struggle to provide adaptive and highly accurate support tailored to individual circumstances and changes, resulting in a lack of effective means to improve users' quality of life. Therefore, a system is needed that learns in real time based on individual user data and generates appropriate advice.

[0063] 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.

[0064] In this invention, the server includes means for collecting diverse activity information from a user terminal, means for transmitting the collected information to a remote server via a network and securely storing the information, and means for analyzing the stored information using a machine learning algorithm and learning the individual's lifestyle. This enables the generation of lifestyle improvement measures and health advice optimized for the user in real time, thereby improving the user's life and maintaining their health.

[0065] A "user terminal" is a device used by an individual to record and transmit activity information and health information, and this includes smartphones and wearable devices.

[0066] "Activity information" refers to data about a user's daily activities and physiological state, and includes things like steps taken, heart rate, activity history, and location information.

[0067] A "network" refers to a communication environment for data transmission, acting as a medium for exchanging information between servers and terminals via the internet.

[0068] A "remote server" refers to a centrally managed computer system used to receive, store, and analyze user activity information.

[0069] A "machine learning algorithm" refers to a series of computational methods that enable computers to automatically find patterns and rules from given data, making analysis and prediction possible.

[0070] "Lifestyle" refers to an individual's lifestyle, encompassing a comprehensive range of factors including the user's daily habits, activity patterns, health status, hobbies, and preferences.

[0071] "Lifestyle improvement measures" refer to action plans and suggestions recommended to improve the user's health and quality of life, and include exercise plans and dietary advice.

[0072] "Health advice" refers to individual recommendations aimed at maintaining or improving the user's health, and specifically includes suggestions regarding exercise and nutrition.

[0073] This invention is a personal support system realized through the cooperation of a user terminal and a server. Users use smartphones or wearable devices (user terminals) to record daily activity and health information. Specifically, these terminals have built-in pedometers and heart rate sensors to accurately capture the user's daily activities.

[0074] This information is transmitted from the device to a remote server via the internet. After receiving the information, the server securely stores it in a database and analyzes it using machine learning algorithms with generating AI models. Specifically, algorithms using programming languages ​​such as Python analyze the user's lifestyle and health status to identify trends and patterns. Based on this analysis, personalized health advice and lifestyle improvement measures are generated for the user.

[0075] For example, if a user wants to avoid weight gain due to lack of exercise, the server can suggest an exercise plan based on past data analysis. This includes how many times a week they need to run and the target distance for each session. This advice is then notified to the user's device by the server. The device then uses this notification to connect with a scheduling app and presents the user with a concrete action plan.

[0076] Furthermore, users can send feedback on the advice from their device to the server. The server uses this feedback to adapt its machine learning algorithms and improve the accuracy of the advice.

[0077] An example of a prompt message might be, "Generate effective weight loss advice based on the user's exercise data."

[0078] In this way, the entire system adapts to the user's lifestyle and health condition, providing efficient and personalized support.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users record their daily activity and health information using smartphones and wearable devices. For example, they might launch an app to measure steps or heart rate, and acquire data using the device's built-in sensors. The input is the user's activity itself, and the output is the activity information stored in the device.

[0082] Step 2:

[0083] The device transmits recorded activity information to a remote server via the internet at regular intervals. Specifically, a dedicated application on the device runs in the background and automatically uploads the data to the server in batch processing. The input is raw data stored on the device, and the output is data securely transferred to the server via the network.

[0084] Step 3:

[0085] The server stores the received data in a database. Next, it analyzes the accumulated data using a generative AI model to understand the user's activity patterns. This process utilizes machine learning algorithms written in programming languages ​​such as Python. The input is activity information sent to the server, and the output is the analysis results regarding the user's lifestyle and health status. Specifically, trend analysis and pattern recognition are performed.

[0086] Step 4:

[0087] The server generates personalized lifestyle improvement plans and health advice based on the analysis results. A generation AI model is used, and the suggested advice includes specific activity plans and dietary recommendations. The input is the analysis results, and the output is specific advice provided to the user. For example, a one-week walking plan might be generated.

[0088] Step 5:

[0089] The server notifies the user's device of the generated advice. The device receives this notification, informs the user, and reflects it in the scheduling app. The input is the generated advice, and the output is the alerts and reminders displayed on the user's device. Specifically, notifications such as "Today's exercise goal: 30 minutes of walking" are sent.

[0090] Step 6:

[0091] After implementing the advice, the user sends feedback to the server via their device. This feedback provides information about the effectiveness of the advice. The server uses this feedback to refine its machine learning algorithm and improve the accuracy of future advice. The input is the user's feedback data, and the output is the improved advice generated by the updated analysis algorithm.

[0092] (Application Example 1)

[0093] 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."

[0094] In factory operations, there is a lack of concrete support to properly manage workers' health and fatigue levels, and to improve work efficiency while maintaining productivity. Furthermore, optimizing worker performance through appropriate advice and schedule adjustments for the work environment is a challenge.

[0095] 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.

[0096] In this invention, the server includes means for collecting physiological indicators from user equipment, means for transmitting the collected physiological indicators to a storage device via a communication device and storing them as information resources, and means for analyzing the stored information resources and learning the health status of each user. This makes it possible to grasp the health status of workers in real time and provide appropriate advice and adjust work schedules according to the work environment.

[0097] A "user device" is an electronic device worn or carried by an individual and used to collect physiological indicators and behavioral data.

[0098] "Physiological indicators" are a set of data that shows a person's physiological state, such as heart rate, body temperature, and amount of movement.

[0099] A "communication device" is an electronic device equipped with an interface for transmitting data to a storage device located at a remote location.

[0100] A "storage device" is a device that stores received data and manages it as an information resource for later analysis.

[0101] "Information resources" refer to a collection of physiological indicators and behavioral data used as foundational data for analysis and learning.

[0102] "Analysis" is a computational process performed to derive patterns and relationships based on accumulated data.

[0103] "Health status" is a collection of information that indicates an individual's physiological and psychological balance.

[0104] "Advice" refers to specific instructions or suggestions provided to the user for solving problems or making improvements.

[0105] "Work environment" refers to the place and conditions in which a person lives or works, and whose conditions affect an individual's productivity and health.

[0106] "Schedule adjustment" is the process of rearranging schedules, taking into account the user's activity patterns, in order to optimize work start times and break times.

[0107] The system in this invention is designed to optimize the health status of individual workers and create an efficient work environment. This system uses smart glasses or wearable devices as user equipment to collect physiological indicators in real time. The devices record physiological indicators such as heart rate, body temperature, and movement, and transmit the data to a storage device via a communication device.

[0108] The server stores the received data in a storage device and manages it as an information resource. Based on this, machine learning algorithms are used to perform analysis and learn about each worker's health status and work performance. When performing the analysis, cloud-based analysis services (for example, Azure® Machine Learning) are utilized.

[0109] Based on the analysis results, the server generates optimal advice for each worker and notifies the user's device. For example, if it is found that concentration levels drop during a specific time period, it suggests taking a break at that time. It also adjusts the schedule according to the work environment, allowing workers to concentrate on their tasks at the optimal time.

[0110] As a concrete example, for workers who tend to expend more energy in the morning, a schedule could be proposed that includes a break in the morning and lighter tasks in the afternoon. In this way, the server can dynamically adjust workers' schedules and improve overall productivity.

[0111] An example of a prompt for a generating AI model is: "Generate optimal rest and stretching suggestions based on today's fatigue level data. These suggestions aim to improve afternoon work efficiency." Using this prompt, the server can quickly generate appropriate advice.

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

[0113] Step 1:

[0114] The user device acquires physiological indicators in real time using sensors. The input consists of worker health-related data (heart rate, body temperature, and movement), which is formatted as data packets. The output is a dataset that can be transmitted to a communication device.

[0115] Step 2:

[0116] The terminal transmits formatted data packets to the communication device via Bluetooth or Wi-Fi. At this stage, a checksum is added to prevent accidental data transmission. The input is the data packet sent from the user device. The output is the data packet converted into a format that the server can receive.

[0117] Step 3:

[0118] The server records received data packets in a storage device for analysis. A database engine is used to verify data integrity. The input is data packets from communication devices. The output is organized information resources.

[0119] Step 4:

[0120] The server analyzes recorded information resources using machine learning algorithms. The input consists of physiological indicators stored in a data storage device, and the output is optimized health status and performance data for the user as a result of the analysis. Specifically, it uses Azure Machine Learning for pattern recognition.

[0121] Step 5:

[0122] The server generates advice using a generative AI model based on the analysis results. It uses prompts to formulate suggestions for breaks and stretches during specific time periods. The input is the analysis results obtained in step 4, and the output is the proposed activity plan.

[0123] Step 6:

[0124] The server sends the generated advice to the user's device. The user can receive notifications in real time through the device. The input is the activity plan generated on the server side, and the output is specific instructions and suggestions displayed to the user. Specific notification actions include text-to-speech reading on the smart glasses display.

[0125] 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.

[0126] This invention is a system that supports the improvement of a user's lifestyle by recognizing the user's emotional state from data acquired in the user's daily life and generating personalized advice based on that. The present invention is implemented by combining a user device, a server device, and an emotion engine.

[0127] First, the user device collects data such as the user's heart rate, location, activity history, and even voice and facial expression data through their daily activities. This data contains important information that suggests the user's emotional state.

[0128] The user device transmits the collected data to the server via the internet. The server receives this data in bulk and securely stores it in a database. During this storage process, the emotional data is analyzed in real time using an emotion engine.

[0129] The emotion engine analyzes voice tone, facial expressions, and behavioral patterns to recognize the emotions the user is currently experiencing. This recognition then categorizes the user's emotional state into specific categories (e.g., stress, euphoria, depression).

[0130] The server generates personalized lifestyle improvement advice for the user based on analysis results from the emotion engine and other data. This advice includes suggestions for mental health care tailored to the user's emotional state, as well as recommended actions to improve their mood.

[0131] Furthermore, advice is sent to the user's device, and the terminal notifies the user. The user can then improve their behavior and lifestyle based on the advice and provide feedback. This feedback is sent to the server and used to further refine the algorithm.

[0132] For example, if a user is experiencing work-related stress, the emotion engine recognizes their emotions through voice and facial expression analysis, and the server generates recommendations for exercise and relaxation to alleviate stress. The device then notifies the user of these recommendations, and the user incorporates the recommended exercises into their daily routine.

[0133] In this way, the present invention aims to provide personalized lifestyle improvement advice based on emotional data and to continuously improve the user's quality of life.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The device quietly collects the user's heart rate, steps, voice data, and facial expression data using sensors and cameras. Activity data from the user's daily life is also acquired simultaneously.

[0137] Step 2:

[0138] The device transmits the collected data to a server via the internet. This data includes voice tone and facial expression changes necessary for emotion recognition.

[0139] Step 3:

[0140] The server stores the data received from the terminal in a database and performs real-time analysis using a dedicated emotion engine. This analysis recognizes the user's emotional state from their voice and facial expressions.

[0141] Step 4:

[0142] The server utilizes emotional data obtained by the emotion engine and combines it with other stored data to evaluate the user's current mental state. Based on this, it generates appropriate advice and improvement measures.

[0143] Step 5:

[0144] The advice generated by the server includes mental health care suggestions tailored to the user's emotional state, as well as action plans for improving mood. This advice is then sent to the device.

[0145] Step 6:

[0146] The device displays advice sent from the server to the user as a notification. The user can then incorporate this information into their daily routine and take concrete action.

[0147] Step 7:

[0148] Users input the results and their impressions of actions taken based on the suggested advice into their device and send this feedback to the server. This feedback will be reflected in subsequent interactions.

[0149] Step 8:

[0150] The server analyzes user feedback and adjusts its sentiment recognition algorithms and advice generation models accordingly. This allows for the provision of more refined and helpful advice.

[0151] (Example 2)

[0152] 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".

[0153] In modern society, personalized advice tailored to each user's emotional state is needed to improve their quality of life. However, conventional methods have struggled to accurately recognize individual emotional states and provide specific and effective solutions. To address this challenge, there is a need for technology that analyzes diverse user data in real time and provides individually optimized advice.

[0154] 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.

[0155] In this invention, the server includes means for accumulating and analyzing biometric information and behavioral data transmitted from the user, means for recognizing the user's emotional state in real time using an emotion analysis engine, and means for generating lifestyle improvement advice tailored to each individual user using a generative AI model. This makes it possible to provide individually optimized advice to improve the user's quality of life.

[0156] A "user device" is a hardware device that collects biometric information from the user, such as heart rate and behavioral data, and includes smartphones and smartwatches.

[0157] A "server" is a computer system that stores and analyzes data transmitted from user devices.

[0158] A "database" is a digital storage system that efficiently and securely stores collected data.

[0159] An "emotion analysis engine" is a software component that analyzes data such as voice and facial expressions to identify the user's emotional state.

[0160] A "generative AI model" is a machine learning-based algorithm that generates individually optimized lifestyle improvement advice based on the user's emotional state.

[0161] "Feedback" refers to the evaluations and comments that users provide regarding the advice they receive, and this information is used to improve the system.

[0162] "Advice" refers to specific suggestions generated based on the user's emotional state and lifestyle data, aimed at encouraging improvements in lifestyle habits.

[0163] The system of this invention is comprised of a user device, a server, an emotion analysis engine, and a generative AI model, all designed to improve the user's quality of life. The user device consists of a smartphone or smartwatch and is responsible for collecting heart rate, location information, behavioral data, voice data, and facial expression data from the user's daily life. This data is an important indicator of the user's physiological and psychological state.

[0164] The data collected by the user's device is securely transmitted to the server via the internet. The server stores and manages the received data in an advanced database. After the data is stored, an emotion analysis engine is used to analyze voice tone, facial expression changes, and behavioral patterns to recognize the user's emotional state in real time. For example, if the user's voice tone is high, suggesting stress, the emotion analysis engine will recognize this as "stress."

[0165] The server uses a generative AI model to create personalized lifestyle improvement advice based on the recognized emotional state. This generative AI model analyzes diverse emotional data based on machine learning algorithms and provides the user with specific action suggestions. For example, it might generate a prompt message such as, "Analyze the user's emotional state based on recent behavioral history and voice tone, and generate appropriate lifestyle improvement advice." In this way, the most suitable advice for the user is created and sent to the user's device.

[0166] The user terminal relays advice notified from the server to the user, facilitating specific actions. The user improves their daily life based on the suggested advice and provides feedback to the server, allowing the system to generate more effective, user-specific advice. This feedback is also used to improve the system's analysis algorithms.

[0167] In this way, the present invention aims to provide personalized advice based on user emotional data and continuously improve the user's quality of life.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user device collects heart rate, location information, behavioral data, voice data, and facial expression data. This data is acquired in real time from the user's daily activities and temporarily stored within the device. The data input is sensor data obtained from the user's living environment, and the output is the accumulated raw data. Specifically, the user's smartwatch measures heart rate, and the smartphone's microphone records voice.

[0171] Step 2:

[0172] The user device sends the collected data to the server in batch transfer mode at regular intervals. The input is the raw data accumulated in step 1, and the output is the data securely sent to the server. Specifically, the data is encrypted using the AES encryption algorithm before being transmitted over the internet.

[0173] Step 3:

[0174] The server automatically stores received data in a database. Input is data sent from user devices, and output is structured data stored in the database. Specifically, the server standardizes the data format and stores it in the database with indexes.

[0175] Step 4:

[0176] The server analyzes the accumulated data using an emotion analysis engine. The input is structured data obtained from a database, and the output is an analysis result indicating the user's emotional state. Data processing includes real-time frequency analysis of voice tone and pattern recognition of facial expression data to classify emotional states into categories such as stress and exhilaration. Specifically, the emotion analysis engine performs emotion recognition from voice data using a machine learning model.

[0177] Step 5:

[0178] The server uses a generative AI model to generate lifestyle improvement advice for the user based on the emotion analysis results. The input is emotional state data obtained from the emotion analysis engine, and the output is specific advice sentences tailored to the user. One prompt is in the format of "Generate relaxation suggestions tailored to the user's stress level." The generative AI model compares this with past data to generate the optimal advice.

[0179] Step 6:

[0180] The server sends the generated advice to the user's device. The input is the generated advice text, and the output is a notification on the user's device. Specifically, the advice message pops up in the notification center of the user's smartphone.

[0181] Step 7:

[0182] Users improve their daily lives based on the advice they receive and send their results and opinions as feedback to the server. The input is information about the effectiveness of the advice the user felt, and the output is feedback data. Specifically, the user enters their thoughts into the feedback form and presses the submit button, which sends the information to the server.

[0183] (Application Example 2)

[0184] 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".

[0185] Traditional in-store customer service often involves providing standardized services without considering the customer's emotional state. This makes it difficult to offer appropriate services or products based on the customer's emotional state within the store environment. As a result, this may not lead to increased customer satisfaction or a greater desire to purchase. To improve this situation, there is a need to develop technologies that accurately understand the customer's emotional state and provide services and suggestions accordingly.

[0186] 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.

[0187] In this invention, the server includes means for collecting audio and visual data obtained from the user, means for determining the emotional state using an emotion engine for analyzing the collected data, and means for generating and outputting service or product information that corresponds to the customer's emotions. This makes it possible to provide personalized services based on individual emotions within the store.

[0188] "Audio and visual data obtained from users" refers to information acquired within the store, such as customers' voices and facial expressions, through cameras and recording devices.

[0189] An "emotion engine" is software or an algorithm that analyzes collected audio and visual data to identify a customer's emotions.

[0190] "Means for generating and outputting service or product information" refers to a method of creating information about products and services suitable for the customer based on sentiment analysis results and presenting it on a display device or mobile terminal within the store.

[0191] "Means of notifying in-store display devices or staff's mobile terminals" refers to hardware and communication technologies for transmitting generated information to customers and store staff in real time.

[0192] The system for implementing this invention mainly consists of a user terminal, a server, and an emotion engine. First, the user terminal installed in the store collects customer voice and visual data using a camera and microphone. This data is transmitted to the server via the internet.

[0193] The server centrally manages the received data and uses an emotion engine to analyze emotional states in real time. The emotion engine utilizes existing image recognition software and speech analysis algorithms (e.g., Amazon Rekognition and Google® Cloud Speech-to-Text) to analyze changes in voice tone and facial expressions, for example. This allows the customer's emotional state to be identified.

[0194] Based on the analysis results, the server generates optimal product information and service suggestions for the customer. These suggestions are immediately sent to in-store display devices and staff's mobile devices. The display devices and mobile devices utilize communication technologies such as Wi-Fi and Bluetooth to transmit information to customers and staff in real time.

[0195] For example, if a customer appears restless in the store, the server, based on an analysis of their emotions, generates and displays suggestions for products that can help them relax. It also notifies staff members' terminals of customers who show interest, offering them the opportunity to try or see demonstrations of new products.

[0196] An example of a prompt message would be: "A specific customer's emotion has been identified as 'relaxed.' Please generate relaxation-related product suggestions tailored to this customer." In this way, individual customer experiences can be personalized, leading to improved service in stores.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The terminal uses cameras and microphones installed in the store to collect customer audio and visual data in real time. This input data includes the customer's facial expressions and tone of voice. The terminal transmits this data to a server via the internet. Once the collected data has arrived at the server in digital format, the next processing step begins.

[0200] Step 2:

[0201] The server receives audio and visual data transmitted from the terminal. This data is input into the emotion engine to analyze the customer's emotional state. Specifically, the emotion engine uses voice tone analysis and facial recognition algorithms (e.g., Google Cloud Speech-to-Text or Amazon Rekognition) to decode the data and outputs the customer's emotional category (e.g., interest, stress, relaxation) as the analysis result. This analysis result becomes the basis data for the next service proposal generation step.

[0202] Step 3:

[0203] The server generates product information and service suggestions tailored to the customer based on sentiment analysis results obtained from the emotion engine. Utilizing a generative AI model, it interprets the analysis results and generates prompt sentences to form specific suggestions. These suggestions, based on the prompt sentences, are optimized to improve the in-store customer experience. The generated suggestions are output digitally, and the process proceeds to the notification step.

[0204] Step 4:

[0205] The server notifies in-store displays or staff members' mobile devices of generated product information and service suggestions. Information is transmitted instantly via Wi-Fi or Bluetooth, providing appropriate information to both customers and staff. This process enables personalized service in physical stores, contributing to increased customer satisfaction. The notified content serves as an effective means of guiding customers to their next actions.

[0206] 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.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention is a personal support system realized through the cooperation of a user device and a server device. It primarily collects, analyzes, and generates advice on everyday data to support the user's daily life.

[0223] First, users record their daily activities and health information using smartphones or wearable devices (user devices). This allows for the acquisition of various data, such as steps taken, heart rate, activity history, and location information.

[0224] This user device transmits collected data to a server device via the internet. The server receives this data and securely stores it in a database. The server uses machine learning algorithms to analyze the stored data and understand the user's lifestyle, health status, hobbies, and preferences.

[0225] Next, the server generates personalized health advice and lifestyle improvement suggestions based on the analysis results. This advice might include, for example, providing an efficient exercise plan that takes into account the user's daily activity patterns, or suggesting a nutritionally balanced diet.

[0226] Once advice is generated, the server notifies the user device. The user device then assists in the execution of this advice by informing the user and, if necessary, incorporating it into the schedule.

[0227] Users can send feedback on the advice they receive through their device. The server incorporates this feedback into its analysis and adjusts the algorithm accordingly to improve the accuracy of the advice. This cyclical feedback process allows the system to evolve in a way that is more adapted to the user's needs.

[0228] For example, if a user sets weight loss as their goal, the system analyzes the user's lack of exercise and eating patterns, and proposes a meal plan based on calorie balance. Furthermore, it incorporates alerts into the schedule to encourage increased daily exercise, creating an environment that makes it easier for the user to achieve their goal.

[0229] Thus, the system of the present invention aims to function as a platform that supports the user's overall lifestyle and facilitates long-term improvement in their quality of life.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The device continuously collects data such as the user's heart rate, steps taken, and application usage information using sensors. This data is appropriately formatted according to the user's active time periods.

[0233] Step 2:

[0234] The device sends collected data to the server at regular intervals. This data includes the user's activity history and current location information.

[0235] Step 3:

[0236] The server stores the data received from the terminal in a database. Here, the data is organized into categories such as health information, location information, and activity history.

[0237] Step 4:

[0238] The server begins analyzing the accumulated data using machine learning algorithms. It primarily identifies user behavior patterns and extracts trends in specific health conditions and hobbies / preferences.

[0239] Step 5:

[0240] Based on the analysis results, the server generates personalized lifestyle improvement advice for each user. In this process, it develops plans based on past data to contribute to future lifestyle improvements and incorporates them into the advice.

[0241] Step 6:

[0242] The server sends the generated advice to the device. The device displays the advice as a notification and, if necessary, adds an action plan to its schedule.

[0243] Step 7:

[0244] Users input their thoughts and impressions about the advice they receive as feedback from their device. This feedback is then sent from the device to the server.

[0245] Step 8:

[0246] The server analyzes user feedback and uses it to further refine its analysis algorithm. This cycle ensures that subsequent advice generation becomes more accurate.

[0247] (Example 1)

[0248] 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."

[0249] With the increasing diversification of living environments in recent years, there is a growing demand for personalized lifestyle improvement advice and health support. However, traditional approaches struggle to provide adaptive and highly accurate support tailored to individual circumstances and changes, resulting in a lack of effective means to improve users' quality of life. Therefore, a system is needed that learns in real time based on individual user data and generates appropriate advice.

[0250] 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.

[0251] In this invention, the server includes means for collecting diverse activity information from a user terminal, means for transmitting the collected information to a remote server via a network and securely storing the information, and means for analyzing the stored information using a machine learning algorithm and learning the individual's lifestyle. This enables the generation of lifestyle improvement measures and health advice optimized for the user in real time, thereby improving the user's life and maintaining their health.

[0252] A "user terminal" is a device used by an individual to record and transmit activity information and health information, and this includes smartphones and wearable devices.

[0253] "Activity information" refers to data about a user's daily activities and physiological state, and includes things like steps taken, heart rate, activity history, and location information.

[0254] A "network" refers to a communication environment for data transmission, acting as a medium for exchanging information between servers and terminals via the internet.

[0255] A "remote server" refers to a centrally managed computer system used to receive, store, and analyze user activity information.

[0256] A "machine learning algorithm" refers to a series of computational methods that enable computers to automatically find patterns and rules from given data, making analysis and prediction possible.

[0257] "Lifestyle" refers to an individual's lifestyle, encompassing a comprehensive range of factors including the user's daily habits, activity patterns, health status, hobbies, and preferences.

[0258] "Lifestyle improvement measures" refer to action plans and suggestions recommended to improve the user's health and quality of life, and include exercise plans and dietary advice.

[0259] "Health advice" refers to individual recommendations aimed at maintaining or improving the user's health, and specifically includes suggestions regarding exercise and nutrition.

[0260] This invention is a personal support system realized through the cooperation of a user terminal and a server. Users use smartphones or wearable devices (user terminals) to record daily activity and health information. Specifically, these terminals have built-in pedometers and heart rate sensors to accurately capture the user's daily activities.

[0261] This information is transmitted from the device to a remote server via the internet. After receiving the information, the server securely stores it in a database and analyzes it using machine learning algorithms with generating AI models. Specifically, algorithms using programming languages ​​such as Python analyze the user's lifestyle and health status to identify trends and patterns. Based on this analysis, personalized health advice and lifestyle improvement measures are generated for the user.

[0262] For example, if a user wants to avoid weight gain due to lack of exercise, the server can suggest an exercise plan based on past data analysis. This includes how many times a week they need to run and the target distance for each session. This advice is then notified to the user's device by the server. The device then uses this notification to connect with a scheduling app and presents the user with a concrete action plan.

[0263] Furthermore, users can send feedback on the advice from their device to the server. The server uses this feedback to adapt its machine learning algorithms and improve the accuracy of the advice.

[0264] An example of a prompt message might be, "Generate effective weight loss advice based on the user's exercise data."

[0265] In this way, the entire system adapts to the user's lifestyle and health condition, providing efficient and personalized support.

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

[0267] Step 1:

[0268] Users record their daily activity and health information using smartphones and wearable devices. For example, they might launch an app to measure steps or heart rate, and acquire data using the device's built-in sensors. The input is the user's activity itself, and the output is the activity information stored in the device.

[0269] Step 2:

[0270] The device transmits recorded activity information to a remote server via the internet at regular intervals. Specifically, a dedicated application on the device runs in the background and automatically uploads the data to the server in batch processing. The input is raw data stored on the device, and the output is data securely transferred to the server via the network.

[0271] Step 3:

[0272] The server stores the received data in a database. Next, it analyzes the accumulated data using a generative AI model to understand the user's activity patterns. This process utilizes machine learning algorithms written in programming languages ​​such as Python. The input is activity information sent to the server, and the output is the analysis results regarding the user's lifestyle and health status. Specifically, trend analysis and pattern recognition are performed.

[0273] Step 4:

[0274] The server generates personalized lifestyle improvement plans and health advice based on the analysis results. A generation AI model is used, and the suggested advice includes specific activity plans and dietary recommendations. The input is the analysis results, and the output is specific advice provided to the user. For example, a one-week walking plan might be generated.

[0275] Step 5:

[0276] The server notifies the user's device of the generated advice. The device receives this notification, informs the user, and reflects it in the scheduling app. The input is the generated advice, and the output is the alerts and reminders displayed on the user's device. Specifically, notifications such as "Today's exercise goal: 30 minutes of walking" are sent.

[0277] Step 6:

[0278] After implementing the advice, the user sends feedback to the server via their device. This feedback provides information about the effectiveness of the advice. The server uses this feedback to refine its machine learning algorithm and improve the accuracy of future advice. The input is the user's feedback data, and the output is the improved advice generated by the updated analysis algorithm.

[0279] (Application Example 1)

[0280] 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."

[0281] In factory operations, there is a lack of concrete support to properly manage workers' health and fatigue levels, and to improve work efficiency while maintaining productivity. Furthermore, optimizing worker performance through appropriate advice and schedule adjustments for the work environment is a challenge.

[0282] 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.

[0283] In this invention, the server includes means for collecting physiological indicators from user devices, means for transmitting the collected physiological indicators to a storage device via a communication device and retaining them as information resources, and means for analyzing the retained information resources and learning the health status of each user. Thereby, the health status of the operator can be grasped in real time, and appropriate advice and adjustment of the work schedule can be made according to the working environment.

[0284] The "user device" is an electronic device worn or carried by an individual and is used to collect physiological indicators and behavior data.

[0285] The "physiological indicators" are a group of data indicating the physiological state of a human, such as heart rate, body temperature, and amount of movement.

[0286] The "communication device" is an electronic device equipped with an interface for transmitting data to a storage device at a remote location.

[0287] <x The "storage device" is a device that stores the received data and manages it as an information resource for later analysis.

[0288] The "information resource" is a collection of physiological indicators and behavior data used as basic data for analysis and learning.

[0289] "Analysis" is a computational process performed to derive patterns and correlations based on the accumulated data.

[0290] <x The "health status" is a collection of information indicating the physiological and psychological balance of an individual. <$

[0291] "Advice" is a specific instruction or proposal provided to the user for problem-solving and improvement.

[0292] The "working environment" refers to the place and situation where life or work is carried out, and its conditions affect the productivity and health of an individual.

[0293] "Schedule adjustment" is the process of rearranging schedules, taking into account the user's activity patterns, in order to optimize work start times and break times.

[0294] The system in this invention is designed to optimize the health status of individual workers and create an efficient work environment. This system uses smart glasses or wearable devices as user equipment to collect physiological indicators in real time. The devices record physiological indicators such as heart rate, body temperature, and movement, and transmit the data to a storage device via a communication device.

[0295] The server stores the received data in a storage device and manages it as an information resource. Based on this, machine learning algorithms are used to perform analysis and learn about each worker's health status and work performance. When performing the analysis, cloud-based analysis services (e.g., Azure Machine Learning) are utilized.

[0296] Based on the analysis results, the server generates optimal advice for each worker and notifies the user's device. For example, if it is found that concentration levels drop during a specific time period, it suggests taking a break at that time. It also adjusts the schedule according to the work environment, allowing workers to concentrate on their tasks at the optimal time.

[0297] As a concrete example, for workers who tend to expend more energy in the morning, a schedule could be proposed that includes a break in the morning and lighter tasks in the afternoon. In this way, the server can dynamically adjust workers' schedules and improve overall productivity.

[0298] An example of a prompt for a generating AI model is: "Generate optimal rest and stretching suggestions based on today's fatigue level data. These suggestions aim to improve afternoon work efficiency." Using this prompt, the server can quickly generate appropriate advice.

[0299] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0300] Step 1:

[0301] The user device acquires physiological indicators in real time using a sensor. The input is the health-related data (heart rate, body temperature, amount of movement) of the operator, which is formatted as a data packet. The output is a data set that can be transmitted to the communication device.

[0302] Step 2:

[0303] The terminal transmits the formatted data packet to the communication device via Bluetooth or Wi-Fi. At this stage, a checksum is added to prevent mistransmission of data. The input is the data packet transmitted from the user device. The output is a data packet converted into a format that can be received by the server.

[0304] Step 3:

[0305] The server records the received data packet in the storage device for analysis. Using a database engine, it performs an operation to confirm the integrity of the data. The input is the data packet from the communication device. The output is the refined information resource.

[0306] Step 4: <00OO967> The server analyzes the recorded information resource using a machine learning algorithm. The input is the physiological indicators stored in the storage device, and the output is the health status data and performance data optimized for the user as the analysis result. As a specific operation, pattern recognition is performed using Azure Machine Learning.

[0308] Step 5:

[0309] The server generates advice using a generative AI model based on the analysis results. It uses prompts to formulate suggestions for breaks and stretches during specific time periods. The input is the analysis results obtained in step 4, and the output is the proposed activity plan.

[0310] Step 6:

[0311] The server sends the generated advice to the user's device. The user can receive notifications in real time through the device. The input is the activity plan generated on the server side, and the output is specific instructions and suggestions displayed to the user. Specific notification actions include text-to-speech reading on the smart glasses display.

[0312] 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.

[0313] This invention is a system that supports the improvement of a user's lifestyle by recognizing the user's emotional state from data acquired in the user's daily life and generating personalized advice based on that. The present invention is implemented by combining a user device, a server device, and an emotion engine.

[0314] First, the user device collects data such as the user's heart rate, location, activity history, and even voice and facial expression data through their daily activities. This data contains important information that suggests the user's emotional state.

[0315] The user device transmits the collected data to the server via the internet. The server receives this data in bulk and securely stores it in a database. During this storage process, the emotional data is analyzed in real time using an emotion engine.

[0316] The emotion engine analyzes voice tone, facial expressions, and behavioral patterns to recognize the emotions the user is currently experiencing. This recognition then categorizes the user's emotional state into specific categories (e.g., stress, euphoria, depression).

[0317] The server generates personalized lifestyle improvement advice for the user based on analysis results from the emotion engine and other data. This advice includes suggestions for mental health care tailored to the user's emotional state, as well as recommended actions to improve their mood.

[0318] Furthermore, advice is sent to the user's device, and the terminal notifies the user. The user can then improve their behavior and lifestyle based on the advice and provide feedback. This feedback is sent to the server and used to further refine the algorithm.

[0319] For example, if a user is experiencing work-related stress, the emotion engine recognizes their emotions through voice and facial expression analysis, and the server generates recommendations for exercise and relaxation to alleviate stress. The device then notifies the user of these recommendations, and the user incorporates the recommended exercises into their daily routine.

[0320] In this way, the present invention aims to provide personalized lifestyle improvement advice based on emotional data and to continuously improve the user's quality of life.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The device quietly collects the user's heart rate, steps, voice data, and facial expression data using sensors and cameras. Activity data from the user's daily life is also acquired simultaneously.

[0324] Step 2:

[0325] The device transmits the collected data to a server via the internet. This data includes voice tone and facial expression changes necessary for emotion recognition.

[0326] Step 3:

[0327] The server stores the data received from the terminal in a database and performs real-time analysis using a dedicated emotion engine. This analysis recognizes the user's emotional state from their voice and facial expressions.

[0328] Step 4:

[0329] The server utilizes emotional data obtained by the emotion engine and combines it with other stored data to evaluate the user's current mental state. Based on this, it generates appropriate advice and improvement measures.

[0330] Step 5:

[0331] The advice generated by the server includes mental health care suggestions tailored to the user's emotional state, as well as action plans for improving mood. This advice is then sent to the device.

[0332] Step 6:

[0333] The device displays advice sent from the server to the user as a notification. The user can then incorporate this information into their daily routine and take concrete action.

[0334] Step 7:

[0335] Users input the results and their impressions of actions taken based on the suggested advice into their device and send this feedback to the server. This feedback will be reflected in subsequent interactions.

[0336] Step 8:

[0337] The server analyzes user feedback and adjusts its sentiment recognition algorithms and advice generation models accordingly. This allows for the provision of more refined and helpful advice.

[0338] (Example 2)

[0339] 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".

[0340] In modern society, personalized advice tailored to each user's emotional state is needed to improve their quality of life. However, conventional methods have struggled to accurately recognize individual emotional states and provide specific and effective solutions. To address this challenge, there is a need for technology that analyzes diverse user data in real time and provides individually optimized advice.

[0341] 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.

[0342] In this invention, the server includes means for accumulating and analyzing biometric information and behavioral data transmitted from the user, means for recognizing the user's emotional state in real time using an emotion analysis engine, and means for generating lifestyle improvement advice tailored to each individual user using a generative AI model. This makes it possible to provide individually optimized advice to improve the user's quality of life.

[0343] A "user device" is a hardware device that collects biometric information from the user, such as heart rate and behavioral data, and includes smartphones and smartwatches.

[0344] A "server" is a computer system that stores and analyzes data transmitted from user devices.

[0345] A "database" is a digital storage system that efficiently and securely stores collected data.

[0346] An "emotion analysis engine" is a software component that analyzes data such as voice and facial expressions to identify the user's emotional state.

[0347] A "generative AI model" is a machine learning-based algorithm that generates individually optimized lifestyle improvement advice based on the user's emotional state.

[0348] "Feedback" refers to the evaluations and comments that users provide regarding the advice they receive, and this information is used to improve the system.

[0349] "Advice" refers to specific suggestions generated based on the user's emotional state and lifestyle data, aimed at encouraging improvements in lifestyle habits.

[0350] The system of this invention is comprised of a user device, a server, an emotion analysis engine, and a generative AI model, all designed to improve the user's quality of life. The user device consists of a smartphone or smartwatch and is responsible for collecting heart rate, location information, behavioral data, voice data, and facial expression data from the user's daily life. This data is an important indicator of the user's physiological and psychological state.

[0351] The data collected by the user's device is securely transmitted to the server via the internet. The server stores and manages the received data in an advanced database. After the data is stored, an emotion analysis engine is used to analyze voice tone, facial expression changes, and behavioral patterns to recognize the user's emotional state in real time. For example, if the user's voice tone is high, suggesting stress, the emotion analysis engine will recognize this as "stress."

[0352] The server uses a generative AI model to create personalized lifestyle improvement advice based on the recognized emotional state. This generative AI model analyzes diverse emotional data based on machine learning algorithms and provides the user with specific action suggestions. For example, it might generate a prompt message such as, "Analyze the user's emotional state based on recent behavioral history and voice tone, and generate appropriate lifestyle improvement advice." In this way, the most suitable advice for the user is created and sent to the user's device.

[0353] The user terminal relays advice notified from the server to the user, facilitating specific actions. The user improves their daily life based on the suggested advice and provides feedback to the server, allowing the system to generate more effective, user-specific advice. This feedback is also used to improve the system's analysis algorithms.

[0354] In this way, the present invention aims to provide personalized advice based on user emotional data and continuously improve the user's quality of life.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user device collects heart rate, location information, behavioral data, voice data, and facial expression data. This data is acquired in real time from the user's daily activities and temporarily stored within the device. The data input is sensor data obtained from the user's living environment, and the output is the accumulated raw data. Specifically, the user's smartwatch measures heart rate, and the smartphone's microphone records voice.

[0358] Step 2:

[0359] The user device sends the collected data to the server in batch transfer mode at regular intervals. The input is the raw data accumulated in step 1, and the output is the data securely sent to the server. Specifically, the data is encrypted using the AES encryption algorithm before being transmitted over the internet.

[0360] Step 3:

[0361] The server automatically stores received data in a database. Input is data sent from user devices, and output is structured data stored in the database. Specifically, the server standardizes the data format and stores it in the database with indexes.

[0362] Step 4:

[0363] The server analyzes the accumulated data using an emotion analysis engine. The input is structured data obtained from a database, and the output is an analysis result indicating the user's emotional state. Data processing includes real-time frequency analysis of voice tone and pattern recognition of facial expression data to classify emotional states into categories such as stress and exhilaration. Specifically, the emotion analysis engine performs emotion recognition from voice data using a machine learning model.

[0364] Step 5:

[0365] The server uses a generative AI model to generate lifestyle improvement advice for the user based on the emotion analysis results. The input is emotional state data obtained from the emotion analysis engine, and the output is specific advice sentences tailored to the user. One prompt is in the format of "Generate relaxation suggestions tailored to the user's stress level." The generative AI model compares this with past data to generate the optimal advice.

[0366] Step 6:

[0367] The server sends the generated advice to the user's device. The input is the generated advice text, and the output is a notification on the user's device. Specifically, the advice message pops up in the notification center of the user's smartphone.

[0368] Step 7:

[0369] Users improve their daily lives based on the advice they receive and send their results and opinions as feedback to the server. The input is information about the effectiveness of the advice the user felt, and the output is feedback data. Specifically, the user enters their thoughts into the feedback form and presses the submit button, which sends the information to the server.

[0370] (Application Example 2)

[0371] 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."

[0372] Traditional in-store customer service often involves providing standardized services without considering the customer's emotional state. This makes it difficult to offer appropriate services or products based on the customer's emotional state within the store environment. As a result, this may not lead to increased customer satisfaction or a greater desire to purchase. To improve this situation, there is a need to develop technologies that accurately understand the customer's emotional state and provide services and suggestions accordingly.

[0373] 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.

[0374] In this invention, the server includes means for collecting audio and visual data obtained from the user, means for determining the emotional state using an emotion engine for analyzing the collected data, and means for generating and outputting service or product information that corresponds to the customer's emotions. This makes it possible to provide personalized services based on individual emotions within the store.

[0375] "Audio and visual data obtained from users" refers to information acquired within the store, such as customers' voices and facial expressions, through cameras and recording devices.

[0376] An "emotion engine" is software or an algorithm that analyzes collected audio and visual data to identify a customer's emotions.

[0377] "Means for generating and outputting service or product information" refers to a method of creating information about products and services suitable for the customer based on sentiment analysis results and presenting it on a display device or mobile terminal within the store.

[0378] "Means of notifying in-store display devices or staff's mobile terminals" refers to hardware and communication technologies for transmitting generated information to customers and store staff in real time.

[0379] The system for implementing this invention mainly consists of a user terminal, a server, and an emotion engine. First, the user terminal installed in the store collects customer voice and visual data using a camera and microphone. This data is transmitted to the server via the internet.

[0380] The server centrally manages the received data and uses an emotion engine to analyze emotional states in real time. The emotion engine utilizes existing image recognition software and speech analysis algorithms (e.g., Amazon Rekognition and Google Cloud Speech-to-Text) to analyze changes in voice tone and facial expressions, for example. This allows the customer's emotional state to be identified.

[0381] Based on the analysis results, the server generates optimal product information and service suggestions for the customer. These suggestions are immediately sent to in-store display devices and staff's mobile devices. The display devices and mobile devices utilize communication technologies such as Wi-Fi and Bluetooth to transmit information to customers and staff in real time.

[0382] For example, if a customer appears restless in the store, the server, based on an analysis of their emotions, generates and displays suggestions for products that can help them relax. It also notifies staff members' terminals of customers who show interest, offering them the opportunity to try or see demonstrations of new products.

[0383] An example of a prompt message would be: "A specific customer's emotion has been identified as 'relaxed.' Please generate relaxation-related product suggestions tailored to this customer." In this way, individual customer experiences can be personalized, leading to improved service in stores.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The terminal uses cameras and microphones installed in the store to collect customer audio and visual data in real time. This input data includes the customer's facial expressions and tone of voice. The terminal transmits this data to a server via the internet. Once the collected data has arrived at the server in digital format, the next processing step begins.

[0387] Step 2:

[0388] The server receives audio and visual data transmitted from the terminal. This data is input into the emotion engine to analyze the customer's emotional state. Specifically, the emotion engine uses voice tone analysis and facial recognition algorithms (e.g., Google Cloud Speech-to-Text or Amazon Rekognition) to decode the data and outputs the customer's emotional category (e.g., interest, stress, relaxation) as the analysis result. This analysis result becomes the basis data for the next service proposal generation step.

[0389] Step 3:

[0390] The server generates product information and service suggestions tailored to the customer based on sentiment analysis results obtained from the emotion engine. Utilizing a generative AI model, it interprets the analysis results and generates prompt sentences to form specific suggestions. These suggestions, based on the prompt sentences, are optimized to improve the in-store customer experience. The generated suggestions are output digitally, and the process proceeds to the notification step.

[0391] Step 4:

[0392] The server notifies in-store displays or staff members' mobile devices of generated product information and service suggestions. Information is transmitted instantly via Wi-Fi or Bluetooth, providing appropriate information to both customers and staff. This process enables personalized service in physical stores, contributing to increased customer satisfaction. The notified content serves as an effective means of guiding customers to their next actions.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is a personal support system realized through the cooperation of a user device and a server device. It primarily collects, analyzes, and generates advice on everyday data to support the user's daily life.

[0410] First, users record their daily activities and health information using smartphones or wearable devices (user devices). This allows for the acquisition of various data, such as steps taken, heart rate, activity history, and location information.

[0411] This user device transmits collected data to a server device via the internet. The server receives this data and securely stores it in a database. The server uses machine learning algorithms to analyze the stored data and understand the user's lifestyle, health status, hobbies, and preferences.

[0412] Next, the server generates personalized health advice and lifestyle improvement suggestions based on the analysis results. This advice might include, for example, providing an efficient exercise plan that takes into account the user's daily activity patterns, or suggesting a nutritionally balanced diet.

[0413] Once advice is generated, the server notifies the user device. The user device then assists in the execution of this advice by informing the user and, if necessary, incorporating it into the schedule.

[0414] Users can send feedback on the advice they receive through their device. The server incorporates this feedback into its analysis and adjusts the algorithm accordingly to improve the accuracy of the advice. This cyclical feedback process allows the system to evolve in a way that is more adapted to the user's needs.

[0415] For example, if a user sets weight loss as their goal, the system analyzes the user's lack of exercise and eating patterns, and proposes a meal plan based on calorie balance. Furthermore, it incorporates alerts into the schedule to encourage increased daily exercise, creating an environment that makes it easier for the user to achieve their goal.

[0416] Thus, the system of the present invention aims to function as a platform that supports the user's overall lifestyle and facilitates long-term improvement in their quality of life.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The device continuously collects data such as the user's heart rate, steps taken, and application usage information using sensors. This data is appropriately formatted according to the user's active time periods.

[0420] Step 2:

[0421] The device sends collected data to the server at regular intervals. This data includes the user's activity history and current location information.

[0422] Step 3:

[0423] The server stores the data received from the terminal in a database. Here, the data is organized into categories such as health information, location information, and activity history.

[0424] Step 4:

[0425] The server begins analyzing the accumulated data using machine learning algorithms. It primarily identifies user behavior patterns and extracts trends in specific health conditions and hobbies / preferences.

[0426] Step 5:

[0427] Based on the analysis results, the server generates personalized lifestyle improvement advice for each user. In this process, it develops plans based on past data to contribute to future lifestyle improvements and incorporates them into the advice.

[0428] Step 6:

[0429] The server sends the generated advice to the device. The device displays the advice as a notification and, if necessary, adds an action plan to its schedule.

[0430] Step 7:

[0431] Users input their thoughts and impressions about the advice they receive as feedback from their device. This feedback is then sent from the device to the server.

[0432] Step 8:

[0433] The server analyzes user feedback and uses it to further refine its analysis algorithm. This cycle ensures that subsequent advice generation becomes more accurate.

[0434] (Example 1)

[0435] 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."

[0436] With the increasing diversification of living environments in recent years, there is a growing demand for personalized lifestyle improvement advice and health support. However, traditional approaches struggle to provide adaptive and highly accurate support tailored to individual circumstances and changes, resulting in a lack of effective means to improve users' quality of life. Therefore, a system is needed that learns in real time based on individual user data and generates appropriate advice.

[0437] 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.

[0438] In this invention, the server includes means for collecting diverse activity information from a user terminal, means for transmitting the collected information to a remote server via a network and securely storing the information, and means for analyzing the stored information using a machine learning algorithm and learning the individual's lifestyle. This enables the generation of lifestyle improvement measures and health advice optimized for the user in real time, thereby improving the user's life and maintaining their health.

[0439] A "user terminal" is a device used by an individual to record and transmit activity information and health information, and this includes smartphones and wearable devices.

[0440] "Activity information" refers to data about a user's daily activities and physiological state, and includes things like steps taken, heart rate, activity history, and location information.

[0441] A "network" refers to a communication environment for data transmission, acting as a medium for exchanging information between servers and terminals via the internet.

[0442] A "remote server" refers to a centrally managed computer system used to receive, store, and analyze user activity information.

[0443] A "machine learning algorithm" refers to a series of computational methods that enable computers to automatically find patterns and rules from given data, making analysis and prediction possible.

[0444] "Lifestyle" refers to an individual's lifestyle, encompassing a comprehensive range of factors including the user's daily habits, activity patterns, health status, hobbies, and preferences.

[0445] "Lifestyle improvement measures" refer to action plans and suggestions recommended to improve the user's health and quality of life, and include exercise plans and dietary advice.

[0446] "Health advice" refers to individual recommendations aimed at maintaining or improving the user's health, and specifically includes suggestions regarding exercise and nutrition.

[0447] This invention is a personal support system realized through the cooperation of a user terminal and a server. Users use smartphones or wearable devices (user terminals) to record daily activity and health information. Specifically, these terminals have built-in pedometers and heart rate sensors to accurately capture the user's daily activities.

[0448] This information is transmitted from the device to a remote server via the internet. After receiving the information, the server securely stores it in a database and analyzes it using machine learning algorithms with generating AI models. Specifically, algorithms using programming languages ​​such as Python analyze the user's lifestyle and health status to identify trends and patterns. Based on this analysis, personalized health advice and lifestyle improvement measures are generated for the user.

[0449] For example, if a user wants to avoid weight gain due to lack of exercise, the server can suggest an exercise plan based on past data analysis. This includes how many times a week they need to run and the target distance for each session. This advice is then notified to the user's device by the server. The device then uses this notification to connect with a scheduling app and presents the user with a concrete action plan.

[0450] Furthermore, users can send feedback on the advice from their device to the server. The server uses this feedback to adapt its machine learning algorithms and improve the accuracy of the advice.

[0451] An example of a prompt message might be, "Generate effective weight loss advice based on the user's exercise data."

[0452] In this way, the entire system adapts to the user's lifestyle and health condition, providing efficient and personalized support.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] Users record their daily activity and health information using smartphones and wearable devices. For example, they might launch an app to measure steps or heart rate, and acquire data using the device's built-in sensors. The input is the user's activity itself, and the output is the activity information stored in the device.

[0456] Step 2:

[0457] The device transmits recorded activity information to a remote server via the internet at regular intervals. Specifically, a dedicated application on the device runs in the background and automatically uploads the data to the server in batch processing. The input is raw data stored on the device, and the output is data securely transferred to the server via the network.

[0458] Step 3:

[0459] The server stores the received data in a database. Next, it analyzes the accumulated data using a generative AI model to understand the user's activity patterns. This process utilizes machine learning algorithms written in programming languages ​​such as Python. The input is activity information sent to the server, and the output is the analysis results regarding the user's lifestyle and health status. Specifically, trend analysis and pattern recognition are performed.

[0460] Step 4:

[0461] The server generates personalized lifestyle improvement plans and health advice based on the analysis results. A generation AI model is used, and the suggested advice includes specific activity plans and dietary recommendations. The input is the analysis results, and the output is specific advice provided to the user. For example, a one-week walking plan might be generated.

[0462] Step 5:

[0463] The server notifies the user's device of the generated advice. The device receives this notification, informs the user, and reflects it in the scheduling app. The input is the generated advice, and the output is the alerts and reminders displayed on the user's device. Specifically, notifications such as "Today's exercise goal: 30 minutes of walking" are sent.

[0464] Step 6:

[0465] After implementing the advice, the user sends feedback to the server via their device. This feedback provides information about the effectiveness of the advice. The server uses this feedback to refine its machine learning algorithm and improve the accuracy of future advice. The input is the user's feedback data, and the output is the improved advice generated by the updated analysis algorithm.

[0466] (Application Example 1)

[0467] 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."

[0468] In factory operations, there is a lack of concrete support to properly manage workers' health and fatigue levels, and to improve work efficiency while maintaining productivity. Furthermore, optimizing worker performance through appropriate advice and schedule adjustments for the work environment is a challenge.

[0469] 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.

[0470] In this invention, the server includes means for collecting physiological indicators from user equipment, means for transmitting the collected physiological indicators to a storage device via a communication device and storing them as information resources, and means for analyzing the stored information resources and learning the health status of each user. This makes it possible to grasp the health status of workers in real time and provide appropriate advice and adjust work schedules according to the work environment.

[0471] "User devices" are electronic devices worn or carried by individuals and used to collect physiological indicators and behavioral data.

[0472] "Physiological indicators" are a set of data that shows a person's physiological state, such as heart rate, body temperature, and amount of movement.

[0473] A "communication device" is an electronic device equipped with an interface for transmitting data to a storage device located at a remote location.

[0474] A "storage device" is a device that stores received data and manages it as an information resource for later analysis.

[0475] "Information resources" refer to a collection of physiological indicators and behavioral data used as foundational data for analysis and learning.

[0476] "Analysis" is a computational process performed to derive patterns and relationships based on accumulated data.

[0477] "Health status" is a collection of information that indicates an individual's physiological and psychological balance.

[0478] "Advice" refers to specific instructions or suggestions provided to the user for solving problems or making improvements.

[0479] "Work environment" refers to the place and conditions in which a person lives or works, and whose conditions affect an individual's productivity and health.

[0480] "Schedule adjustment" is the process of rearranging schedules, taking into account the user's activity patterns, in order to optimize work start times and break times.

[0481] The system in this invention is designed to optimize the health status of individual workers and create an efficient work environment. This system uses smart glasses or wearable devices as user equipment to collect physiological indicators in real time. The devices record physiological indicators such as heart rate, body temperature, and movement, and transmit the data to a storage device via a communication device.

[0482] The server stores the received data in a storage device and manages it as an information resource. Based on this, machine learning algorithms are used to perform analysis and learn about each worker's health status and work performance. When performing the analysis, cloud-based analysis services (e.g., Azure Machine Learning) are utilized.

[0483] Based on the analysis results, the server generates optimal advice for each worker and notifies the user's device. For example, if it is found that concentration levels drop during a specific time period, it suggests taking a break at that time. It also adjusts the schedule according to the work environment, allowing workers to concentrate on their tasks at the optimal time.

[0484] As a concrete example, for workers who tend to expend more energy in the morning, a schedule could be proposed that includes a break in the morning and lighter tasks in the afternoon. In this way, the server can dynamically adjust workers' schedules and improve overall productivity.

[0485] An example of a prompt for a generating AI model is: "Generate optimal rest and stretching suggestions based on today's fatigue level data. These suggestions aim to improve afternoon work efficiency." Using this prompt, the server can quickly generate appropriate advice.

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The user device acquires physiological indicators in real time using sensors. The input consists of worker health-related data (heart rate, body temperature, and movement), which is formatted as data packets. The output is a dataset that can be transmitted to a communication device.

[0489] Step 2:

[0490] The terminal transmits formatted data packets to the communication device via Bluetooth or Wi-Fi. At this stage, a checksum is added to prevent accidental data transmission. The input is the data packet sent from the user device. The output is the data packet converted into a format that the server can receive.

[0491] Step 3:

[0492] The server records received data packets in a storage device for analysis. A database engine is used to verify data integrity. The input is data packets from communication devices. The output is organized information resources.

[0493] Step 4:

[0494] The server analyzes recorded information resources using machine learning algorithms. The input consists of physiological indicators stored in a data storage device, and the output is optimized health status and performance data tailored to the user as a result of the analysis. Specifically, it uses Azure Machine Learning for pattern recognition.

[0495] Step 5:

[0496] The server generates advice using a generative AI model based on the analysis results. It uses prompts to formulate suggestions for breaks and stretches during specific time periods. The input is the analysis results obtained in step 4, and the output is the proposed activity plan.

[0497] Step 6:

[0498] The server sends the generated advice to the user's device. The user can receive notifications in real time through the device. The input is the activity plan generated on the server side, and the output is specific instructions and suggestions displayed to the user. Specific notification actions include text-to-speech reading on the smart glasses display.

[0499] 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.

[0500] This invention is a system that supports the improvement of a user's lifestyle by recognizing the user's emotional state from data acquired in the user's daily life and generating personalized advice based on that. The present invention is implemented by combining a user device, a server device, and an emotion engine.

[0501] First, the user device collects data such as the user's heart rate, location, activity history, and even voice and facial expression data through their daily activities. This data contains important information that suggests the user's emotional state.

[0502] The user device transmits the collected data to the server via the internet. The server receives this data in bulk and securely stores it in a database. During this storage process, the emotional data is analyzed in real time using an emotion engine.

[0503] The emotion engine analyzes voice tone, facial expressions, and behavioral patterns to recognize the emotions the user is currently experiencing. This recognition then categorizes the user's emotional state into specific categories (e.g., stress, euphoria, depression).

[0504] The server generates personalized lifestyle improvement advice for the user based on analysis results from the emotion engine and other data. This advice includes suggestions for mental health care tailored to the user's emotional state, as well as recommended actions to improve their mood.

[0505] Furthermore, advice is sent to the user's device, and the terminal notifies the user. The user can then improve their behavior and lifestyle based on the advice and provide feedback. This feedback is sent to the server and used to further refine the algorithm.

[0506] For example, if a user is experiencing work-related stress, the emotion engine recognizes their emotions through voice and facial expression analysis, and the server generates recommendations for exercise and relaxation to alleviate stress. The device then notifies the user of these recommendations, and the user incorporates the recommended exercises into their daily routine.

[0507] In this way, the present invention aims to provide personalized lifestyle improvement advice based on emotional data and to continuously improve the user's quality of life.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The device quietly collects the user's heart rate, steps, voice data, and facial expression data using sensors and cameras. Activity data from the user's daily life is also acquired simultaneously.

[0511] Step 2:

[0512] The device transmits the collected data to a server via the internet. This data includes voice tone and facial expression changes necessary for emotion recognition.

[0513] Step 3:

[0514] The server stores the data received from the terminal in a database and performs real-time analysis using a dedicated emotion engine. This analysis recognizes the user's emotional state from their voice and facial expressions.

[0515] Step 4:

[0516] The server utilizes emotional data obtained by the emotion engine and combines it with other stored data to evaluate the user's current mental state. Based on this, it generates appropriate advice and improvement measures.

[0517] Step 5:

[0518] The advice generated by the server includes mental health care suggestions tailored to the user's emotional state, as well as action plans for improving mood. This advice is then sent to the device.

[0519] Step 6:

[0520] The device displays advice sent from the server to the user as a notification. The user can then incorporate this information into their daily routine and take concrete action.

[0521] Step 7:

[0522] Users input the results and their impressions of actions taken based on the suggested advice into their device and send this feedback to the server. This feedback will be reflected in subsequent interactions.

[0523] Step 8:

[0524] The server analyzes user feedback and adjusts its sentiment recognition algorithms and advice generation models accordingly. This allows for the provision of more refined and helpful advice.

[0525] (Example 2)

[0526] 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."

[0527] In modern society, personalized advice tailored to each user's emotional state is needed to improve their quality of life. However, conventional methods have struggled to accurately recognize individual emotional states and provide specific and effective solutions. To address this challenge, there is a need for technology that analyzes diverse user data in real time and provides individually optimized advice.

[0528] 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.

[0529] In this invention, the server includes means for accumulating and analyzing biometric information and behavioral data transmitted from the user, means for recognizing the user's emotional state in real time using an emotion analysis engine, and means for generating lifestyle improvement advice tailored to each individual user using a generative AI model. This makes it possible to provide individually optimized advice to improve the user's quality of life.

[0530] A "user device" is a hardware device that collects biometric information from the user, such as heart rate and behavioral data, and includes smartphones and smartwatches.

[0531] A "server" is a computer system that stores and analyzes data transmitted from user devices.

[0532] A "database" is a digital storage system that efficiently and securely stores collected data.

[0533] An "emotion analysis engine" is a software component that analyzes data such as voice and facial expressions to identify the user's emotional state.

[0534] A "generative AI model" is a machine learning-based algorithm that generates individually optimized lifestyle improvement advice based on the user's emotional state.

[0535] "Feedback" refers to the evaluations and comments that users provide regarding the advice they receive, and this information is used to improve the system.

[0536] "Advice" refers to specific suggestions generated based on the user's emotional state and lifestyle data, aimed at encouraging improvements in lifestyle habits.

[0537] The system of this invention is comprised of a user device, a server, an emotion analysis engine, and a generative AI model, all designed to improve the user's quality of life. The user device consists of a smartphone or smartwatch and is responsible for collecting heart rate, location information, behavioral data, voice data, and facial expression data from the user's daily life. This data is an important indicator of the user's physiological and psychological state.

[0538] The data collected by the user's device is securely transmitted to the server via the internet. The server stores and manages the received data in an advanced database. After the data is stored, an emotion analysis engine is used to analyze voice tone, facial expression changes, and behavioral patterns to recognize the user's emotional state in real time. For example, if the user's voice tone is high, suggesting stress, the emotion analysis engine will recognize this as "stress."

[0539] The server uses a generative AI model to create personalized lifestyle improvement advice based on the recognized emotional state. This generative AI model analyzes diverse emotional data based on machine learning algorithms and provides the user with specific action suggestions. For example, it might generate a prompt message such as, "Analyze the user's emotional state based on recent behavioral history and voice tone, and generate appropriate lifestyle improvement advice." In this way, the most suitable advice for the user is created and sent to the user's device.

[0540] The user terminal relays advice notified from the server to the user, facilitating specific actions. The user improves their daily life based on the suggested advice and provides feedback to the server, allowing the system to generate more effective, user-specific advice. This feedback is also used to improve the system's analysis algorithms.

[0541] In this way, the present invention aims to provide personalized advice based on user emotional data and continuously improve the user's quality of life.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The user device collects heart rate, location information, behavioral data, voice data, and facial expression data. This data is acquired in real time from the user's daily activities and temporarily stored within the device. The data input is sensor data obtained from the user's living environment, and the output is the accumulated raw data. Specifically, the user's smartwatch measures heart rate, and the smartphone's microphone records voice.

[0545] Step 2:

[0546] The user device sends the collected data to the server in batch transfer mode at regular intervals. The input is the raw data accumulated in step 1, and the output is the data securely sent to the server. Specifically, the data is encrypted using the AES encryption algorithm before being transmitted over the internet.

[0547] Step 3:

[0548] The server automatically stores received data in a database. Input is data sent from user devices, and output is structured data stored in the database. Specifically, the server standardizes the data format and stores it in the database with indexes.

[0549] Step 4:

[0550] The server analyzes the accumulated data using an emotion analysis engine. The input is structured data obtained from a database, and the output is an analysis result indicating the user's emotional state. Data processing includes real-time frequency analysis of voice tone and pattern recognition of facial expression data to classify emotional states into categories such as stress and exhilaration. Specifically, the emotion analysis engine performs emotion recognition from voice data using a machine learning model.

[0551] Step 5:

[0552] The server uses a generative AI model to generate lifestyle improvement advice for the user based on the emotion analysis results. The input is emotional state data obtained from the emotion analysis engine, and the output is specific advice sentences tailored to the user. One prompt is in the format of "Generate relaxation suggestions tailored to the user's stress level." The generative AI model compares this with past data to generate the optimal advice.

[0553] Step 6:

[0554] The server sends the generated advice to the user's device. The input is the generated advice text, and the output is a notification on the user's device. Specifically, the advice message pops up in the notification center of the user's smartphone.

[0555] Step 7:

[0556] Users improve their daily lives based on the advice they receive and send their results and opinions as feedback to the server. The input is information about the effectiveness of the advice the user felt, and the output is feedback data. Specifically, the user enters their thoughts into the feedback form and presses the submit button, which sends the information to the server.

[0557] (Application Example 2)

[0558] 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."

[0559] Traditional in-store customer service often involves providing standardized services without considering the customer's emotional state. This makes it difficult to offer appropriate services or products based on the customer's emotional state within the store environment. As a result, this may not lead to increased customer satisfaction or a greater desire to purchase. To improve this situation, there is a need to develop technologies that accurately understand the customer's emotional state and provide services and suggestions accordingly.

[0560] 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.

[0561] In this invention, the server includes means for collecting audio and visual data obtained from the user, means for determining the emotional state using an emotion engine for analyzing the collected data, and means for generating and outputting service or product information that corresponds to the customer's emotions. This makes it possible to provide personalized services based on individual emotions within the store.

[0562] "Audio and visual data obtained from users" refers to information acquired within the store, such as customers' voices and facial expressions, through cameras and recording devices.

[0563] An "emotion engine" is software or an algorithm that analyzes collected audio and visual data to identify a customer's emotions.

[0564] "Means for generating and outputting service or product information" refers to a method of creating information about products and services suitable for the customer based on sentiment analysis results and presenting it on a display device or mobile terminal within the store.

[0565] "Means of notifying in-store display devices or staff's mobile terminals" refers to hardware and communication technologies for transmitting generated information to customers and store staff in real time.

[0566] The system for implementing this invention mainly consists of a user terminal, a server, and an emotion engine. First, the user terminal installed in the store collects customer voice and visual data using a camera and microphone. This data is transmitted to the server via the internet.

[0567] The server centrally manages the received data and uses an emotion engine to analyze emotional states in real time. The emotion engine utilizes existing image recognition software and speech analysis algorithms (e.g., Amazon Rekognition and Google Cloud Speech-to-Text) to analyze changes in voice tone and facial expressions, for example. This allows the customer's emotional state to be identified.

[0568] Based on the analysis results, the server generates optimal product information and service suggestions for the customer. These suggestions are immediately sent to in-store display devices and staff's mobile devices. The display devices and mobile devices utilize communication technologies such as Wi-Fi and Bluetooth to transmit information to customers and staff in real time.

[0569] For example, if a customer appears restless in the store, the server, based on an analysis of their emotions, generates and displays suggestions for products that can help them relax. It also notifies staff members' terminals of customers who show interest, offering them the opportunity to try or see demonstrations of new products.

[0570] An example of a prompt message would be: "A specific customer's emotion has been identified as 'relaxed.' Please generate relaxation-related product suggestions tailored to this customer." In this way, individual customer experiences can be personalized, leading to improved service in stores.

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The terminal uses cameras and microphones installed in the store to collect customer audio and visual data in real time. This input data includes the customer's facial expressions and tone of voice. The terminal transmits this data to a server via the internet. Once the collected data has arrived at the server in digital format, the next processing step begins.

[0574] Step 2:

[0575] The server receives audio and visual data transmitted from the terminal. This data is input into the emotion engine to analyze the customer's emotional state. Specifically, the emotion engine uses voice tone analysis and facial recognition algorithms (e.g., Google Cloud Speech-to-Text or Amazon Rekognition) to decode the data and outputs the customer's emotional category (e.g., interest, stress, relaxation) as the analysis result. This analysis result becomes the basis data for the next service proposal generation step.

[0576] Step 3:

[0577] The server generates product information and service suggestions tailored to the customer based on sentiment analysis results obtained from the emotion engine. Utilizing a generative AI model, it interprets the analysis results and generates prompt sentences to form specific suggestions. These suggestions, based on the prompt sentences, are optimized to improve the in-store customer experience. The generated suggestions are output digitally, and the process proceeds to the notification step.

[0578] Step 4:

[0579] The server notifies in-store displays or staff members' mobile devices of generated product information and service suggestions. Information is transmitted instantly via Wi-Fi or Bluetooth, providing appropriate information to both customers and staff. This process enables personalized service in physical stores, contributing to increased customer satisfaction. The notified content serves as an effective means of guiding customers to their next actions.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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".

[0597] This invention is a personal support system realized through the cooperation of a user device and a server device. It primarily collects, analyzes, and generates advice on everyday data to support the user's daily life.

[0598] First, users record their daily activities and health information using smartphones or wearable devices (user devices). This allows for the acquisition of various data, such as steps taken, heart rate, activity history, and location information.

[0599] This user device transmits collected data to a server device via the internet. The server receives this data and securely stores it in a database. The server uses machine learning algorithms to analyze the stored data and understand the user's lifestyle, health status, hobbies, and preferences.

[0600] Next, the server generates personalized health advice and lifestyle improvement suggestions based on the analysis results. This advice might include, for example, providing an efficient exercise plan that takes into account the user's daily activity patterns, or suggesting a nutritionally balanced diet.

[0601] Once advice is generated, the server notifies the user device. The user device then assists in the execution of this advice by informing the user and, if necessary, incorporating it into the schedule.

[0602] Users can send feedback on the advice they receive through their device. The server incorporates this feedback into its analysis and adjusts the algorithm accordingly to improve the accuracy of the advice. This cyclical feedback process allows the system to evolve in a way that is more adapted to the user's needs.

[0603] For example, if a user sets weight loss as their goal, the system analyzes the user's lack of exercise and eating patterns, and proposes a meal plan based on calorie balance. Furthermore, it incorporates alerts into the schedule to encourage increased daily exercise, creating an environment that makes it easier for the user to achieve their goal.

[0604] Thus, the system of the present invention aims to function as a platform that supports the user's overall lifestyle and facilitates long-term improvement in their quality of life.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The device continuously collects data such as the user's heart rate, steps taken, and application usage information using sensors. This data is appropriately formatted according to the user's active time periods.

[0608] Step 2:

[0609] The device sends collected data to the server at regular intervals. This data includes the user's activity history and current location information.

[0610] Step 3:

[0611] The server stores the data received from the terminal in a database. Here, the data is organized into categories such as health information, location information, and activity history.

[0612] Step 4:

[0613] The server begins analyzing the accumulated data using machine learning algorithms. It primarily identifies user behavior patterns and extracts trends in specific health conditions and hobbies / preferences.

[0614] Step 5:

[0615] Based on the analysis results, the server generates personalized lifestyle improvement advice for each user. In this process, it develops plans based on past data to contribute to future lifestyle improvements and incorporates them into the advice.

[0616] Step 6:

[0617] The server sends the generated advice to the device. The device displays the advice as a notification and, if necessary, adds an action plan to its schedule.

[0618] Step 7:

[0619] Users input their thoughts and impressions about the advice they receive as feedback from their device. This feedback is then sent from the device to the server.

[0620] Step 8:

[0621] The server analyzes user feedback and uses it to further refine its analysis algorithm. This cycle ensures that subsequent advice generation becomes more accurate.

[0622] (Example 1)

[0623] 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".

[0624] With the increasing diversification of living environments in recent years, there is a growing demand for personalized lifestyle improvement advice and health support. However, traditional approaches struggle to provide adaptive and highly accurate support tailored to individual circumstances and changes, resulting in a lack of effective means to improve users' quality of life. Therefore, a system is needed that learns in real time based on individual user data and generates appropriate advice.

[0625] 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.

[0626] In this invention, the server includes means for collecting diverse activity information from a user terminal, means for transmitting the collected information to a remote server via a network and securely storing the information, and means for analyzing the stored information using a machine learning algorithm and learning the individual's lifestyle. This enables the generation of lifestyle improvement measures and health advice optimized for the user in real time, thereby improving the user's life and maintaining their health.

[0627] A "user terminal" is a device used by an individual to record and transmit activity information and health information, and this includes smartphones and wearable devices.

[0628] "Activity information" refers to data about a user's daily activities and physiological state, and includes things like steps taken, heart rate, activity history, and location information.

[0629] A "network" refers to a communication environment for data transmission, acting as a medium for exchanging information between servers and terminals via the internet.

[0630] A "remote server" refers to a centrally managed computer system used to receive, store, and analyze user activity information.

[0631] A "machine learning algorithm" refers to a series of computational methods that enable computers to automatically find patterns and rules from given data, making analysis and prediction possible.

[0632] "Lifestyle" refers to an individual's lifestyle, encompassing a comprehensive range of factors including the user's daily habits, activity patterns, health status, hobbies, and preferences.

[0633] "Lifestyle improvement measures" refer to action plans and suggestions recommended to improve the user's health and quality of life, and include exercise plans and dietary advice.

[0634] "Health advice" refers to individual recommendations aimed at maintaining or improving the user's health, and specifically includes suggestions regarding exercise and nutrition.

[0635] This invention is a personal support system realized through the cooperation of a user terminal and a server. Users use smartphones or wearable devices (user terminals) to record daily activity and health information. Specifically, these terminals have built-in pedometers and heart rate sensors to accurately capture the user's daily activities.

[0636] This information is transmitted from the device to a remote server via the internet. After receiving the information, the server securely stores it in a database and analyzes it using machine learning algorithms with generating AI models. Specifically, algorithms using programming languages ​​such as Python analyze the user's lifestyle and health status to identify trends and patterns. Based on this analysis, personalized health advice and lifestyle improvement measures are generated for the user.

[0637] For example, if a user wants to avoid weight gain due to lack of exercise, the server can suggest an exercise plan based on past data analysis. This includes how many times a week they need to run and the target distance for each session. This advice is then notified to the user's device by the server. The device then uses this notification to connect with a scheduling app and presents the user with a concrete action plan.

[0638] Furthermore, users can send feedback on the advice from their device to the server. The server uses this feedback to adapt its machine learning algorithms and improve the accuracy of the advice.

[0639] An example of a prompt message might be, "Generate effective weight loss advice based on the user's exercise data."

[0640] In this way, the entire system adapts to the user's lifestyle and health condition, providing efficient and personalized support.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] Users record their daily activity and health information using smartphones and wearable devices. For example, they might launch an app to measure steps or heart rate, and acquire data using the device's built-in sensors. The input is the user's activity itself, and the output is the activity information stored in the device.

[0644] Step 2:

[0645] The device transmits recorded activity information to a remote server via the internet at regular intervals. Specifically, a dedicated application on the device runs in the background and automatically uploads the data to the server in batch processing. The input is raw data stored on the device, and the output is data securely transferred to the server via the network.

[0646] Step 3:

[0647] The server stores the received data in a database. Next, it analyzes the accumulated data using a generative AI model to understand the user's activity patterns. This process utilizes machine learning algorithms written in programming languages ​​such as Python. The input is activity information sent to the server, and the output is the analysis results regarding the user's lifestyle and health status. Specifically, trend analysis and pattern recognition are performed.

[0648] Step 4:

[0649] The server generates personalized lifestyle improvement plans and health advice based on the analysis results. A generation AI model is used, and the suggested advice includes specific activity plans and dietary recommendations. The input is the analysis results, and the output is specific advice provided to the user. For example, a one-week walking plan might be generated.

[0650] Step 5:

[0651] The server notifies the user's device of the generated advice. The device receives this notification, informs the user, and reflects it in the scheduling app. The input is the generated advice, and the output is the alerts and reminders displayed on the user's device. Specifically, notifications such as "Today's exercise goal: 30 minutes of walking" are sent.

[0652] Step 6:

[0653] After implementing the advice, the user sends feedback to the server via their device. This feedback provides information about the effectiveness of the advice. The server uses this feedback to refine its machine learning algorithm and improve the accuracy of future advice. The input is the user's feedback data, and the output is the improved advice generated by the updated analysis algorithm.

[0654] (Application Example 1)

[0655] 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".

[0656] In factory operations, there is a lack of concrete support to properly manage workers' health and fatigue levels, and to improve work efficiency while maintaining productivity. Furthermore, optimizing worker performance through appropriate advice and schedule adjustments for the work environment is a challenge.

[0657] 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.

[0658] In this invention, the server includes means for collecting physiological indicators from user equipment, means for transmitting the collected physiological indicators to a storage device via a communication device and storing them as information resources, and means for analyzing the stored information resources and learning the health status of each user. This makes it possible to grasp the health status of workers in real time and provide appropriate advice and adjust work schedules according to the work environment.

[0659] "User devices" are electronic devices worn or carried by individuals and used to collect physiological indicators and behavioral data.

[0660] "Physiological indicators" are a set of data that shows a person's physiological state, such as heart rate, body temperature, and amount of movement.

[0661] A "communication device" is an electronic device equipped with an interface for transmitting data to a storage device located at a remote location.

[0662] A "storage device" is a device that stores received data and manages it as an information resource for later analysis.

[0663] "Information resources" refer to a collection of physiological indicators and behavioral data used as foundational data for analysis and learning.

[0664] "Analysis" is a computational process performed to derive patterns and relationships based on accumulated data.

[0665] "Health status" is a collection of information that indicates an individual's physiological and psychological balance.

[0666] "Advice" refers to specific instructions or suggestions provided to the user for solving problems or making improvements.

[0667] "Work environment" refers to the place and conditions in which a person lives or works, and whose conditions affect an individual's productivity and health.

[0668] "Schedule adjustment" is the process of rearranging schedules, taking into account the user's activity patterns, in order to optimize work start times and break times.

[0669] The system in this invention is designed to optimize the health status of individual workers and create an efficient work environment. This system uses smart glasses or wearable devices as user equipment to collect physiological indicators in real time. The devices record physiological indicators such as heart rate, body temperature, and movement, and transmit the data to a storage device via a communication device.

[0670] The server stores the received data in a storage device and manages it as an information resource. Based on this, machine learning algorithms are used to perform analysis and learn about each worker's health status and work performance. When performing the analysis, cloud-based analysis services (e.g., Azure Machine Learning) are utilized.

[0671] Based on the analysis results, the server generates optimal advice for each worker and notifies the user's device. For example, if it is found that concentration levels drop during a specific time period, it suggests taking a break at that time. It also adjusts the schedule according to the work environment, allowing workers to concentrate on their tasks at the optimal time.

[0672] As a concrete example, for workers who tend to expend more energy in the morning, a schedule could be proposed that includes a break in the morning and lighter tasks in the afternoon. In this way, the server can dynamically adjust workers' schedules and improve overall productivity.

[0673] An example of a prompt for a generating AI model is: "Generate optimal rest and stretching suggestions based on today's fatigue level data. These suggestions aim to improve afternoon work efficiency." Using this prompt, the server can quickly generate appropriate advice.

[0674] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0675] Step 1:

[0676] The user device acquires physiological indicators in real time using sensors. The input consists of worker health-related data (heart rate, body temperature, and movement), which is formatted as data packets. The output is a dataset that can be transmitted to a communication device.

[0677] Step 2:

[0678] The terminal transmits formatted data packets to the communication device via Bluetooth or Wi-Fi. At this stage, a checksum is added to prevent accidental data transmission. The input is the data packet sent from the user device. The output is the data packet converted into a format that the server can receive.

[0679] Step 3:

[0680] The server records received data packets in a storage device for analysis. A database engine is used to verify data integrity. The input is data packets from communication devices. The output is organized information resources.

[0681] Step 4:

[0682] The server analyzes recorded information resources using machine learning algorithms. The input consists of physiological indicators stored in a data storage device, and the output is optimized health status and performance data tailored to the user as a result of the analysis. Specifically, it uses Azure Machine Learning for pattern recognition.

[0683] Step 5:

[0684] The server generates advice using a generative AI model based on the analysis results. It uses prompts to formulate suggestions for breaks and stretches during specific time periods. The input is the analysis results obtained in step 4, and the output is the proposed activity plan.

[0685] Step 6:

[0686] The server sends the generated advice to the user's device. The user can receive notifications in real time through the device. The input is the activity plan generated on the server side, and the output is specific instructions and suggestions displayed to the user. Specific notification actions include text-to-speech reading on the smart glasses display.

[0687] 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.

[0688] This invention is a system that supports the improvement of a user's lifestyle by recognizing the user's emotional state from data acquired in the user's daily life and generating personalized advice based on that. The present invention is implemented by combining a user device, a server device, and an emotion engine.

[0689] First, the user device collects data such as the user's heart rate, location, activity history, and even voice and facial expression data through their daily activities. This data contains important information that suggests the user's emotional state.

[0690] The user device transmits the collected data to the server via the internet. The server receives this data in bulk and securely stores it in a database. During this storage process, the emotional data is analyzed in real time using an emotion engine.

[0691] The emotion engine analyzes voice tone, facial expressions, and behavioral patterns to recognize the emotions the user is currently experiencing. This recognition then categorizes the user's emotional state into specific categories (e.g., stress, euphoria, depression).

[0692] The server generates personalized lifestyle improvement advice for the user based on analysis results from the emotion engine and other data. This advice includes suggestions for mental health care tailored to the user's emotional state, as well as recommended actions to improve their mood.

[0693] Furthermore, advice is sent to the user's device, and the terminal notifies the user. The user can then improve their behavior and lifestyle based on the advice and provide feedback. This feedback is sent to the server and used to further refine the algorithm.

[0694] For example, if a user is experiencing work-related stress, the emotion engine recognizes their emotions through voice and facial expression analysis, and the server generates recommendations for exercise and relaxation to alleviate stress. The device then notifies the user of these recommendations, and the user incorporates the recommended exercises into their daily routine.

[0695] In this way, the present invention aims to provide personalized lifestyle improvement advice based on emotional data and to continuously improve the user's quality of life.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The device quietly collects the user's heart rate, steps, voice data, and facial expression data using sensors and cameras. Activity data from the user's daily life is also acquired simultaneously.

[0699] Step 2:

[0700] The device transmits the collected data to a server via the internet. This data includes voice tone and facial expression changes necessary for emotion recognition.

[0701] Step 3:

[0702] The server stores the data received from the terminal in a database and performs real-time analysis using a dedicated emotion engine. This analysis recognizes the user's emotional state from their voice and facial expressions.

[0703] Step 4:

[0704] The server utilizes emotional data obtained by the emotion engine and combines it with other stored data to evaluate the user's current mental state. Based on this, it generates appropriate advice and improvement measures.

[0705] Step 5:

[0706] The advice generated by the server includes mental health care suggestions tailored to the user's emotional state, as well as action plans for improving mood. This advice is then sent to the device.

[0707] Step 6:

[0708] The device displays advice sent from the server to the user as a notification. The user can then incorporate this information into their daily routine and take concrete action.

[0709] Step 7:

[0710] Users input the results and their impressions of actions taken based on the suggested advice into their device and send this feedback to the server. This feedback will be reflected in subsequent interactions.

[0711] Step 8:

[0712] The server analyzes user feedback and adjusts its sentiment recognition algorithms and advice generation models accordingly. This allows for the provision of more refined and helpful advice.

[0713] (Example 2)

[0714] 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".

[0715] In modern society, personalized advice tailored to each user's emotional state is needed to improve their quality of life. However, conventional methods have struggled to accurately recognize individual emotional states and provide specific and effective solutions. To address this challenge, there is a need for technology that analyzes diverse user data in real time and provides individually optimized advice.

[0716] 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.

[0717] In this invention, the server includes means for accumulating and analyzing biometric information and behavioral data transmitted from the user, means for recognizing the user's emotional state in real time using an emotion analysis engine, and means for generating lifestyle improvement advice tailored to each individual user using a generative AI model. This makes it possible to provide individually optimized advice to improve the user's quality of life.

[0718] A "user device" is a hardware device that collects biometric information from the user, such as heart rate and behavioral data, and includes smartphones and smartwatches.

[0719] A "server" is a computer system that stores and analyzes data transmitted from user devices.

[0720] A "database" is a digital storage system that efficiently and securely stores collected data.

[0721] An "emotion analysis engine" is a software component that analyzes data such as voice and facial expressions to identify the user's emotional state.

[0722] A "generative AI model" is a machine learning-based algorithm that generates individually optimized lifestyle improvement advice based on the user's emotional state.

[0723] "Feedback" refers to the evaluations and comments that users provide regarding the advice they receive, and this information is used to improve the system.

[0724] "Advice" refers to specific suggestions generated based on the user's emotional state and lifestyle data, aimed at encouraging improvements in lifestyle habits.

[0725] The system of this invention is comprised of a user device, a server, an emotion analysis engine, and a generative AI model, all designed to improve the user's quality of life. The user device consists of a smartphone or smartwatch and is responsible for collecting heart rate, location information, behavioral data, voice data, and facial expression data from the user's daily life. This data is an important indicator of the user's physiological and psychological state.

[0726] The data collected by the user's device is securely transmitted to the server via the internet. The server stores and manages the received data in an advanced database. After the data is stored, an emotion analysis engine is used to analyze voice tone, facial expression changes, and behavioral patterns to recognize the user's emotional state in real time. For example, if the user's voice tone is high, suggesting stress, the emotion analysis engine will recognize this as "stress."

[0727] The server uses a generative AI model to create personalized lifestyle improvement advice based on the recognized emotional state. This generative AI model analyzes diverse emotional data based on machine learning algorithms and provides the user with specific action suggestions. For example, it might generate a prompt message such as, "Analyze the user's emotional state based on recent behavioral history and voice tone, and generate appropriate lifestyle improvement advice." In this way, the most suitable advice for the user is created and sent to the user's device.

[0728] The user terminal relays advice notified from the server to the user, facilitating specific actions. The user improves their daily life based on the suggested advice and provides feedback to the server, allowing the system to generate more effective, user-specific advice. This feedback is also used to improve the system's analysis algorithms.

[0729] In this way, the present invention aims to provide personalized advice based on user emotional data and continuously improve the user's quality of life.

[0730] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0731] Step 1:

[0732] The user device collects heart rate, location information, behavioral data, voice data, and facial expression data. This data is acquired in real time from the user's daily activities and temporarily stored within the device. The data input is sensor data obtained from the user's living environment, and the output is the accumulated raw data. Specifically, the user's smartwatch measures heart rate, and the smartphone's microphone records voice.

[0733] Step 2:

[0734] The user device sends the collected data to the server in batch transfer mode at regular intervals. The input is the raw data accumulated in step 1, and the output is the data securely sent to the server. Specifically, the data is encrypted using the AES encryption algorithm before being transmitted over the internet.

[0735] Step 3:

[0736] The server automatically stores received data in a database. Input is data sent from user devices, and output is structured data stored in the database. Specifically, the server standardizes the data format and stores it in the database with indexes.

[0737] Step 4:

[0738] The server analyzes the accumulated data using an emotion analysis engine. The input is structured data obtained from a database, and the output is an analysis result indicating the user's emotional state. Data processing includes real-time frequency analysis of voice tone and pattern recognition of facial expression data to classify emotional states into categories such as stress and exhilaration. Specifically, the emotion analysis engine performs emotion recognition from voice data using a machine learning model.

[0739] Step 5:

[0740] The server uses a generative AI model to generate lifestyle improvement advice for the user based on the emotion analysis results. The input is emotional state data obtained from the emotion analysis engine, and the output is specific advice sentences tailored to the user. One prompt is in the format of "Generate relaxation suggestions tailored to the user's stress level." The generative AI model compares this with past data to generate the optimal advice.

[0741] Step 6:

[0742] The server sends the generated advice to the user's device. The input is the generated advice text, and the output is a notification on the user's device. Specifically, the advice message pops up in the notification center of the user's smartphone.

[0743] Step 7:

[0744] Users improve their daily lives based on the advice they receive and send their results and opinions as feedback to the server. The input is information about the effectiveness of the advice the user felt, and the output is feedback data. Specifically, the user enters their thoughts into the feedback form and presses the submit button, which sends the information to the server.

[0745] (Application Example 2)

[0746] 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".

[0747] Traditional in-store customer service often involves providing standardized services without considering the customer's emotional state. This makes it difficult to offer appropriate services or products based on the customer's emotional state within the store environment. As a result, this may not lead to increased customer satisfaction or a greater desire to purchase. To improve this situation, there is a need to develop technologies that accurately understand the customer's emotional state and provide services and suggestions accordingly.

[0748] 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.

[0749] In this invention, the server includes means for collecting audio and visual data obtained from the user, means for determining the emotional state using an emotion engine for analyzing the collected data, and means for generating and outputting service or product information that corresponds to the customer's emotions. This makes it possible to provide personalized services based on individual emotions within the store.

[0750] "Audio and visual data obtained from users" refers to information acquired within the store, such as customers' voices and facial expressions, through cameras and recording devices.

[0751] An "emotion engine" is software or an algorithm that analyzes collected audio and visual data to identify a customer's emotions.

[0752] "Means for generating and outputting service or product information" refers to a method of creating information about products and services suitable for the customer based on sentiment analysis results and presenting it on a display device or mobile terminal within the store.

[0753] "Means of notifying in-store display devices or staff's mobile terminals" refers to hardware and communication technologies for transmitting generated information to customers and store staff in real time.

[0754] The system for implementing this invention mainly consists of a user terminal, a server, and an emotion engine. First, the user terminal installed in the store collects customer voice and visual data using a camera and microphone. This data is transmitted to the server via the internet.

[0755] The server centrally manages the received data and uses an emotion engine to analyze emotional states in real time. The emotion engine utilizes existing image recognition software and speech analysis algorithms (e.g., Amazon Rekognition and Google Cloud Speech-to-Text) to analyze changes in voice tone and facial expressions, for example. This allows the customer's emotional state to be identified.

[0756] Based on the analysis results, the server generates optimal product information and service suggestions for the customer. These suggestions are immediately sent to in-store display devices and staff's mobile devices. The display devices and mobile devices utilize communication technologies such as Wi-Fi and Bluetooth to transmit information to customers and staff in real time.

[0757] For example, if a customer appears restless in the store, the server, based on an analysis of their emotions, generates and displays suggestions for products that can help them relax. It also notifies staff members' terminals of customers who show interest, offering them the opportunity to try or see demonstrations of new products.

[0758] An example of a prompt message would be: "A specific customer's emotion has been identified as 'relaxed.' Please generate relaxation-related product suggestions tailored to this customer." In this way, individual customer experiences can be personalized, leading to improved service in stores.

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The terminal uses cameras and microphones installed in the store to collect customer audio and visual data in real time. This input data includes the customer's facial expressions and tone of voice. The terminal transmits this data to a server via the internet. Once the collected data has arrived at the server in digital format, the next processing step begins.

[0762] Step 2:

[0763] The server receives audio and visual data transmitted from the terminal. This data is input into the emotion engine to analyze the customer's emotional state. Specifically, the emotion engine uses voice tone analysis and facial recognition algorithms (e.g., Google Cloud Speech-to-Text or Amazon Rekognition) to decode the data and outputs the customer's emotional category (e.g., interest, stress, relaxation) as the analysis result. This analysis result becomes the basis data for the next service proposal generation step.

[0764] Step 3:

[0765] The server generates product information and service suggestions tailored to the customer based on sentiment analysis results obtained from the emotion engine. Utilizing a generative AI model, it interprets the analysis results and generates prompt sentences to form specific suggestions. These suggestions, based on the prompt sentences, are optimized to improve the in-store customer experience. The generated suggestions are output digitally, and the process proceeds to the notification step.

[0766] Step 4:

[0767] The server notifies in-store displays or staff members' mobile devices of generated product information and service suggestions. Information is transmitted instantly via Wi-Fi or Bluetooth, providing appropriate information to both customers and staff. This process enables personalized service in physical stores, contributing to increased customer satisfaction. The notified content serves as an effective means of guiding customers to their next actions.

[0768] 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.

[0769] 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.

[0770] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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 as being incorporated by reference.

[0789] The following is further disclosed regarding the embodiments described above.

[0790] (Claim 1)

[0791] Means for collecting personal data from user devices,

[0792] The means for transmitting the collected data to a server device and storing the data,

[0793] A means for analyzing the aforementioned accumulated data and learning each user's lifestyle habits,

[0794] A means for generating improvement measures or advice suitable for the user based on the aforementioned learning results,

[0795] A system including means for notifying the user device of the generated advice.

[0796] (Claim 2)

[0797] A means for receiving user feedback in a user device,

[0798] The system according to claim 1, further comprising means for adjusting the analysis algorithm using the received feedback.

[0799] (Claim 3)

[0800] It is equipped with means for accumulating data including health status, behavioral history, hobbies and preferences, or location information,

[0801] The system according to claim 1, comprising means for generating advice relating to finance, career, or family relationships.

[0802] "Example 1"

[0803] (Claim 1)

[0804] A means of collecting diverse activity information from user terminals,

[0805] The means for transmitting the collected information to a remote server via a network and securely storing the information,

[0806] The aforementioned accumulated information is analyzed using a machine learning algorithm, and a means is used to learn individual lifestyles.

[0807] A means for generating optimized lifestyle improvement measures or health advice based on the aforementioned learning results,

[0808] A system including means for notifying the user terminal of the generated advice and reflecting it in the schedule.

[0809] (Claim 2)

[0810] We receive feedback from users through their devices.

[0811] The system according to claim 1, further comprising means for adaptively adjusting the machine learning algorithm using the received feedback.

[0812] (Claim 3)

[0813] It is equipped with means for accumulating daily health status, behavioral history, hobbies and preferences, or location information,

[0814] The system according to claim 1, comprising means for generating personalized advice relating to health maintenance, career choices, or home life.

[0815] "Application Example 1"

[0816] (Claim 1)

[0817] A means of collecting physiological indicators from user devices,

[0818] A means for transmitting the collected physiological indicators to a storage device via a communication device and storing them as information resources,

[0819] A means for analyzing the aforementioned retained information resources and learning the health status of each user,

[0820] A means for generating control measures or advice that meet the user's requirements based on the aforementioned learning results,

[0821] Means for notifying the user device of the generated advice,

[0822] A system that includes means for adjusting work schedules to improve productivity in the work environment.

[0823] (Claim 2)

[0824] A means for receiving user responses on user devices,

[0825] The system according to claim 1, further comprising means for adjusting the analysis program using the received reaction.

[0826] (Claim 3)

[0827] The system includes means for storing information resources including health status, behavioral history, or location information,

[0828] The system according to claim 1, comprising means for generating advice relating to professional, financial, or social relations.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] A means for collecting biometric information, geographic information, behavioral data, voice data, or facial expression data from a user device,

[0832] The means for transmitting the collected data to a server and storing it in a database,

[0833] A means of recognizing the user's emotional state by analyzing accumulated data in real time using an emotion analysis engine,

[0834] A means of generating personalized lifestyle improvement advice by utilizing a generation AI model based on the recognized emotional state,

[0835] A system including means for transmitting and notifying the user device of the generated advice.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising means for receiving user feedback from a user device and using the feedback to adjust the analysis algorithm.

[0838] (Claim 3)

[0839] The system according to claim 1, comprising means for accumulating biometric information, behavioral patterns, or environmental information, and for generating advice related to psychological state.

[0840] "Application example 2 when combining with an emotional engine"

[0841] (Claim 1)

[0842] Means for collecting audio and visual data obtained from users,

[0843] A means for determining an emotional state using an emotion engine for analyzing the collected data,

[0844] A means for generating and outputting service or product information that responds to customer emotions,

[0845] Means for notifying the generated information to an in-store display device or a staff member's mobile terminal,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising means for adjusting the sentiment analysis method based on user feedback.

[0849] (Claim 3)

[0850] It is equipped with means for accumulating customer movement and behavior information within the store,

[0851] The system according to claim 1, which includes means for generating suggestions to improve the customer experience based on this data. [Explanation of symbols]

[0852] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting personal data from user devices, The means for transmitting the collected data to a server device and storing the data, A means for analyzing the aforementioned accumulated data and learning each user's lifestyle habits, A means for generating improvement measures or advice suitable for the user based on the aforementioned learning results, A system including means for notifying the user device of the generated advice.

2. A means for receiving user feedback in a user device, The system according to claim 1, further comprising means for adjusting the analysis algorithm using the received feedback.

3. It is equipped with means for accumulating data including health status, behavioral history, hobbies and preferences, or location information, The system according to claim 1, comprising means for generating advice relating to finance, career, or family relationships.

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