system

Abstract

We provide the system. [Solution] Means of collecting user activity data, A means of analyzing the user's lifestyle based on the aforementioned activity data, A means of generating suggestions tailored to the user's cultural background and life stage, A means for notifying the user terminal of the aforementioned proposal, A means of receiving user feedback and continuously learning, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] Conventional recommendation systems are limited to proposals based on short-term interests and preferences of users, and there is a problem that it is difficult to make comprehensive proposals considering changes in users' long-term lifestyles and life stages. Also, these systems often require prompts, which may increase the burden on users. Furthermore, there is also a problem that it is difficult to provide ethical proposals considering cultural backgrounds and ethical concepts.

Means for Solving the Problems

[0005] This invention is a system that automatically collects user activity data from various devices and performs data analysis in the cloud. This generates long-term, ethical suggestions tailored to the user's lifestyle and life stage, and notifies the user's device. Furthermore, it continuously improves its suggestions by receiving user feedback and learning from it. This enables comprehensive lifestyle curation that enriches the user's entire life.

[0006] A "user" refers to an individual who utilizes this system and is the entity that provides data on their lifestyle and activities.

[0007] "Activity data" refers to information that shows a user's actions and status in their daily life, and includes location information, heart rate, steps taken, and meal records.

[0008] "Lifestyle" refers to the totality of a user's way of life, daily habits, and values, and includes elements related to their health, interests, and hobbies.

[0009] "Cultural background" refers to the characteristics of the society and culture to which the user belongs, including customs, ethics, and belief systems.

[0010] "Life stage" refers to a specific phase in a user's life, including phases such as student, new graduate, child-rearing, and post-retirement.

[0011] "Suggestions" refer to specific actions and information provided by the system to enrich the user's lifestyle, and include things like meal plans and exercise schedules.

[0012] A "device" refers to an instrument used by a user to collect activity data, and includes smartphones and wearable devices.

[0013] "Feedback" refers to the reactions and evaluations that users provide to a system, and is used to improve suggestions.

[0014] "Continuous learning" refers to the process by which a system improves itself based on user activity and feedback in order to enhance the accuracy of its suggestions.

[0015] "Ethical" means being ethical, indicating that the proposed content takes into consideration the user's values ​​and ethical perspectives. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] 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 Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

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

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

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

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a comprehensive lifestyle curation system designed to enrich the entire lives of users. Specifically, it generates and provides personalized suggestions based on activity data collected from each user, tailored to their individual lifestyle.

[0038] First, users use smartphones or wearable devices to record various activity information from their daily lives. This includes recording steps, location information, heart rate data, and meal records. The device sends this information to a cloud server, where it is stored as user activity data.

[0039] The server stores the received data while maintaining its integrity and analyzes it using machine learning algorithms. The server understands the user's activity patterns and health indicators and generates personalized recommendations. These recommendations take into account the user's life stage, cultural background, and ethical values, and are provided in a way that aligns with the user's values.

[0040] For example, if a user is a new employee who is busy with work during the week and lacks exercise, the server can use that activity data to suggest short exercises and healthy meals. These suggestions are sent to the user's smartphone and used as reference information to improve their daily activities.

[0041] Furthermore, users can provide feedback on suggestions via their devices. This feedback is sent to the server and used as training data to improve the accuracy of the suggestions. This feedback allows for suggestions tailored to the user's preferences, ultimately improving their quality of life in the long term.

[0042] In this way, the suggestion system based on user activity data functions as an effective means of improving users' quality of life and realizing a richer lifestyle. The coordination of servers, terminals, and users enables individualized responses tailored to each user's needs.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device records activity data from the user's daily life. This data includes steps taken, heart rate, location information, and details of meals eaten. This data is sent from the device to a cloud server as it is collected.

[0046] Step 2:

[0047] The server receives activity data sent from the terminal and stores it in the database. It verifies the integrity of the data and preprocesses any incomplete data or outliers.

[0048] Step 3:

[0049] The server analyzes the user's lifestyle based on pre-processed data. Machine learning algorithms are used to evaluate the user's behavioral patterns and health indicators.

[0050] Step 4:

[0051] Based on the analysis results, the server generates lifestyle suggestions that take into account the user's life stage, cultural background, and ethical values. The suggestions cover a wide range of topics, including health management, exercise, nutritional balance, and relaxation methods.

[0052] Step 5:

[0053] The server notifies the user of the generated suggestions on their smartphone or wearable device. The suggestions are presented in a format that is easy for the user to immediately incorporate into their daily life.

[0054] Step 6:

[0055] Users implement the suggested actions and provide feedback on the results and their impressions through their device. This feedback serves as important data for evaluating how well users accepted the suggestions.

[0056] Step 7:

[0057] The server receives user feedback and stores it in a database. This data is used for continuous learning to improve the quality of suggestions. This allows for adjustments to make future suggestions more personalized.

[0058] (Example 1)

[0059] 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."

[0060] In today's world, improving the health and lifestyle quality of busy users is a crucial challenge. However, conventional methods fail to adequately address the individual user's background and preferences, making it difficult to provide effective lifestyle improvement solutions. In particular, the inability to fully utilize individual user activity data hinders the provision of optimal recommendations.

[0061] 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.

[0062] In this invention, the server includes means for acquiring information related to the user's activities, means for analyzing the user's lifestyle based on the information, and means for producing advice tailored to the user's social background and life stage using the generated AI model. This makes it possible to provide optimal suggestions for improving each user's life based on their individual needs.

[0063] "Information related to user activities" refers to data that reflects the user's activities in their daily life, such as location information, physiological state, exercise level, and dietary intake.

[0064] "Means of analyzing lifestyles" refers to a process or technology that uses information obtained from users to understand their unique behavioral patterns and health status, and to conduct analyses that are useful for improving their lifestyles.

[0065] A "generated AI model" is a model constructed using machine learning or artificial intelligence algorithms, which is used to analyze user data and generate individually optimized suggestions.

[0066] "Means of producing advice" refers to the process or technology for creating and providing suggestions to users based on their analysis results, tailored to their social background and life stage.

[0067] "Means of receiving responses and learning to continuously improve" refers to machine learning or data update processes that take user feedback and use it to improve the accuracy and appropriateness of suggestions.

[0068] This invention is a comprehensive lifestyle curation system designed to enrich the user's entire life. The system functions as a three-part system consisting of a server, terminals, and the user.

[0069] First, users use devices such as smartphones or wearable devices to record information related to their daily activities. This information includes steps taken, location data, heart rate, and food and drink intake. The devices send this information to a cloud server, where it is stored as the user's activity data.

[0070] The server analyzes the user's lifestyle based on the received data. Using machine learning frameworks such as Python and Tensorflow®, the server leverages the generated AI model to analyze the user's activity patterns. This process makes it possible to produce advice that takes into account the user's social background and life stage.

[0071] As a concrete example, consider a user who has recently started a new job, is busy, and is not getting enough exercise. In this situation, the server would suggest exercise programs that can be completed in a short time, as well as healthy, time-saving meal plans. These suggestions would be notified to the user's device, and the user could receive them and use them in their daily life.

[0072] Furthermore, users can send feedback on suggestions to the server via their device. The server uses this feedback to learn and further improve the accuracy of its suggestions. The goal is to continuously improve the quality of advice in response to user preferences and changes.

[0073] An example of a prompt might be, "Please provide suggestions for improving exercise for a user who is a new member of the workforce." This system inputs such prompts into an AI model and builds a mechanism to generate appropriate suggestions. In this way, a suggestion system based on user activity data functions as an effective means of improving the user's quality of life and realizing a fulfilling lifestyle.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] Users record information related to their daily activities using devices such as smartphones and wearable devices. Specific actions include measuring steps, obtaining location information using GPS, monitoring heart rate, and manually entering or photographing meal details. This data is temporarily stored on the device. Input is user activity data, and output is data saved to the device's local storage.

[0077] Step 2:

[0078] The device periodically sends the collected activity data to a cloud server. This operation is performed automatically, for example, every hour, using HTTPS. The input is raw data stored on the device, and the output is user-specific activity data stored on the server.

[0079] Step 3:

[0080] The server checks the integrity of the received activity data and performs data cleansing before saving it to the database. Specifically, it uses a Python script to impute missing values ​​and remove outliers. The input is data sent from the terminal, and the output is data formatted for analysis.

[0081] Step 4:

[0082] The server analyzes the formatted data using a generation AI model. This AI model utilizes TensorFlow and analyzes the user's activity patterns and health status to generate suggestions tailored to the user's life stage and cultural background. The input is cleansed data, and the output is user-optimized suggestions.

[0083] Step 5:

[0084] The server sends the generated suggestions to the user's device. The device notifies the user of the received suggestions in the form of a pop-up notification or similar. The input is the suggestions generated by the AI ​​model, and the output is the information notified to the user.

[0085] Step 6:

[0086] Users send feedback on received suggestions to the server via their device. This feedback includes evaluations based on user preferences and the effectiveness of the suggestions. The input is the user's feedback, and the output is the training data sent to the server. The server uses this data to learn and improve the accuracy of future suggestions.

[0087] (Application Example 1)

[0088] 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."

[0089] Modern consumers have diverse lifestyles and individual needs, but general product and service offerings are not adequately addressing them. Furthermore, there is a lack of systems that effectively utilize individual activity data to suggest the most suitable products and services to users in real time. Therefore, there is a need for specific technologies to further enrich the consumer experience.

[0090] 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.

[0091] In this invention, the server includes means for collecting user behavior data, means for analyzing the user's lifestyle based on the behavior data, means for generating suggestions tailored to the user's life stage and cultural background, and means for suggesting products and services based on the behavior data. This makes it possible to quickly and accurately suggest products and services suitable for each user, thereby improving the consumer experience.

[0092] "Behavioral data" refers to information about a user's activities, including location, physical information, distance traveled, and records of eating and drinking.

[0093] "Lifestyle" refers to a user's daily habits and behavioral patterns, encompassing their entire way of life based on their individual values ​​and cultural background.

[0094] "Life stages" refer to different phases in a user's life, such as being a new employee, having a family, or being retired.

[0095] "Suggestions" refer to specific recommendations and suggestions about products and services that are generated based on users' lifestyle and behavioral data.

[0096] "User device" refers to a terminal operated by a user, and includes smartphones, wearable devices, or other interfaces.

[0097] "Responses" refer to feedback from users regarding their opinions and actions in response to suggestions, and this information is used to improve the system.

[0098] "Methods for proposing products and services" refers to technologies that analyze user behavior data to select the most suitable products and services and propose them appropriately to users.

[0099] This invention provides a system to enrich the user's lifestyle, and its specific embodiments are described below.

[0100] First, users collect daily activity data using smartphones or wearable devices (hereinafter referred to as "user devices"). User devices are equipped with sensors to record location information, distance traveled, body measurements, and records of eating and drinking. This data is transmitted to a cloud server in real time.

[0101] The server uses the received behavioral data to analyze the user's lifestyle using specialized data analysis software (such as machine learning frameworks like TensorFlow or PyTorch). This analysis makes it possible to generate suggestions that take into account the user's life stage and cultural background. For example, it can suggest an appropriate exercise program to a user who is not getting enough exercise, and these suggestions can include information on suitable products and events.

[0102] The user device is equipped with an interface that allows the user to easily provide feedback on suggestions received from the server. This feedback is sent to the server and used as data to improve the accuracy of the suggestions.

[0103] For example, if the system detects through analysis that a user is not getting enough exercise, it will generate a prompt such as, "How about a 15-minute yoga set that fits your busy weekday schedule?" and suggest it to the user. This prompt will be displayed on the user's smart glasses and provided as reference information for daily health management.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The user device uses built-in sensors to collect behavioral data in real time, including location information, distance traveled, body measurements, and records of eating and drinking. This data is primarily obtained from accelerometers, GPS sensors, and heart rate sensors. The collected data is temporarily stored in the device's memory.

[0107] Step 2:

[0108] The user device transmits the collected behavioral data to the cloud server. To protect user privacy, the data is securely transferred via an encryption protocol. The input is the user's behavioral data, and the output is the raw data uploaded to the server.

[0109] Step 3:

[0110] The server stores the received behavioral data in a database. During this process, data integrity is ensured, and data is managed separately for each user. Next, machine learning frameworks such as TensorFlow and PyTorch are used to analyze the data. The input is raw behavioral data, and the output is the analysis results regarding the user's lifestyle.

[0111] Step 4:

[0112] The server generates product suggestions that take into account the user's lifestyle and cultural background, based on the analysis results. This generation utilizes natural language processing technology and a generative AI model as its platform. In this example, prompts such as "How about a 15-minute yoga set suitable for your busy weekday schedule?" are generated. The input is the analysis results, and the output is the suggested prompt.

[0113] Step 5:

[0114] The user device notifies the user of the prompt message received from the server. In doing so, smart glasses or smartphone notification functions are used to ensure the user receives the suggestions appropriately. The input is the suggestion prompt message, and the output is the notification to the user.

[0115] Step 6:

[0116] Users provide feedback on the received suggestions through their device. This feedback is easily done using the terminal's input interface and sent to the server. The input is the user's feedback, and the output is data that helps improve the suggestions.

[0117] 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.

[0118] This invention is a comprehensive system that analyzes a user's activity data and emotional state to provide suggestions for improving the user's lifestyle. The system collects and analyzes data from the user's daily life using their smartphone or wearable device.

[0119] The device records activity data such as the user's location, heart rate, steps taken, and meal records. Furthermore, by combining it with an emotion engine, it analyzes the user's emotions through voice tone, facial recognition, and biosignals, and sends that data to a cloud server.

[0120] The server stores received activity and emotional data in a database and first performs data preprocessing. Then, it analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. Through this analysis, lifestyle suggestions are generated. These suggestions are customized according to the user's cultural background and life stage, and include content aimed at stress management and mental health improvement based on their emotional state.

[0121] For example, if the server suspects a user is experiencing high stress levels, it will suggest relaxation methods or meditation apps that address that emotional state. Furthermore, suggestions for meals and exercise are adjusted according to the user's emotional state on any given day and presented at the most beneficial time for them.

[0122] The suggestions are notified to the user's smartphone and presented in an actionable format. The user provides feedback, and the server uses this feedback to learn from the system and improve the suggestions. This allows the user to receive more personalized lifestyle advice.

[0123] This system allows for a real-time understanding of users' daily lives and emotional states, enabling appropriate lifestyle improvements. The integration of the server and terminals provides solutions tailored to diverse user needs.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The device records the user's daily activity data and emotional data. This includes location information, heart rate, steps, and meal records obtained from the user's smartphone or wearable device, as well as emotional data such as voice tone, facial expressions, and heart rate variability obtained using an emotion engine.

[0127] Step 2:

[0128] The device transmits collected activity and emotion data to a cloud server. Data transmission occurs in real time or at specified intervals, while maintaining data integrity.

[0129] Step 3:

[0130] The server receives data sent from the terminal and stores it in the database. It detects missing or abnormal values ​​in the data and performs preprocessing such as imputation or removal as needed.

[0131] Step 4:

[0132] The server applies machine learning algorithms based on pre-processed data to analyze user behavior and emotions. At this stage, it extracts user lifestyle patterns and emotional trends, and evaluates stress levels, happiness levels, and other factors.

[0133] Step 5:

[0134] Based on the analysis results, the server generates suggestions tailored to the user's current lifestyle and emotional state. These suggestions include health management, exercise, dietary improvements, stress reduction methods, and relaxation and mental health support.

[0135] Step 6:

[0136] The server notifies the user's device of the generated suggestions. The suggestions are provided in an easy-to-follow format and may include, for example, immediately actionable exercise videos, recommended recipes, or the use of meditation apps.

[0137] Step 7:

[0138] Users receive notifications and act accordingly. They can also record feedback on the suggestions and their effects through their device.

[0139] Step 8:

[0140] The server collects feedback from users and stores it in a database. This data is used to update the machine learning model, continuously improving the quality and accuracy of suggestions.

[0141] This flow allows users to improve their quality of life by consistently monitoring and caring for their lifestyle and emotions.

[0142] (Example 2)

[0143] 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".

[0144] In modern society, personalized health management and stress management are in demand. However, existing systems struggle to effectively integrate and analyze activity information and emotional states to propose lifestyles optimized for individuals. Furthermore, there is a lack of adequate methods for using user feedback to improve the accuracy of these suggestions and train the systems accordingly. In this situation, there is a need for a system that can provide detailed support tailored to the diverse needs of users.

[0145] 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.

[0146] In this invention, the server includes means for collecting user activity information, means for analyzing emotional states, and means for creating and notifying personalized suggestions. This enables the integrated analysis of user activity information and emotional states to provide personalized lifestyle suggestions. Furthermore, by incorporating user feedback, the system can continuously learn and provide highly accurate suggestions.

[0147] A "user" is a person who uses the system and receives suggestions based on the analysis of their activity information and emotional state.

[0148] "Activity information" refers to data generated by users in their daily lives, such as location information, pulse rate, distance traveled, and meal history.

[0149] "Emotional state" refers to the user's psychological state as determined through analysis of voice tone and facial expressions.

[0150] "Suggestions" refer to information created based on the user's activity data and emotional state, aimed at improving their lifestyle, such as meal plans, exercise plans, and relaxation methods.

[0151] "Feedback" refers to the evaluations and opinions that users provide to the system regarding the acceptance and implementation of their suggestions.

[0152] "Means of continuous learning and improvement" refers to the process by which the system uses user feedback to update its algorithms and suggestions, enabling it to make more accurate suggestions.

[0153] "User device" refers to a device used by a user to receive suggestions, and includes smartphones and wearable devices.

[0154] "Lifestyle" refers to how a user spends their daily life, according to their cultural environment and stage of life, as well as the habits and behaviors that support it.

[0155] This invention is a system for providing personalized lifestyle improvement suggestions by comprehensively analyzing a user's activity information and emotional state. Specific embodiments are described below.

[0156] First, the device uses hardware such as smartphones and wearable devices to collect activity information in real time, including location data, pulse rate, distance traveled, and meal history, generated during the user's daily life. Regarding emotional states, the device's built-in microphone and camera are used to analyze voice tone and facial expressions. For this purpose, facial recognition software and voice analysis software are employed. All of this data is transmitted to a server in a cloud environment.

[0157] The server stores the received activity and emotion data in a database and then performs preprocessing. This involves noise reduction and correction of anomalous data to create a clean dataset. Next, machine learning algorithms and generative AI models are used to analyze the user's lifestyle and emotional state. Based on the analysis results, lifestyle suggestions tailored to the user are generated. These suggestions are created taking into account the user's cultural environment and life stage, and are then sent back to the device via the cloud.

[0158] For example, if the server detects high stress levels based on the user's emotional state, it will suggest relaxation methods or the use of a meditation app. Such suggestions will be notified to the user's device in the form of, for example, "Please open a meditation app to practice deep breathing." An example of a prompt message might be, "Based on the user's activity information and emotional state, please generate specific suggestions to alleviate stress."

[0159] Users try out actions based on the suggestions they receive and provide feedback to the system. Based on this feedback, the server updates its learning algorithm to further improve the accuracy of its suggestions. Through this cycle, the suggestions provided to users become more personalized and effective over time.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The device collects user activity information. Inputs include location data, pulse rate, distance traveled, and meal history obtained from smartphones and wearable devices. This data is acquired using sensors and applications, and processed in real time. The output is processed into a format that can be sent to a cloud server.

[0163] Step 2:

[0164] The device analyzes the user's emotional state. Inputs include voice tone and facial expression data acquired through the device's built-in microphone and camera. This data is processed by emotion analysis software to understand the user's emotional state. Output is analyzed emotion data, which is also sent to a cloud server.

[0165] Step 3:

[0166] The server receives collected activity and sentiment data. All data from the terminal is received as input and stored in the database. The server preprocesses the received data, removing noise and imputing missing values ​​to generate a clean dataset. The output is processed data suitable for analysis by machine learning algorithms.

[0167] Step 4:

[0168] The server performs lifestyle analysis based on a generative AI model using pre-processed data. The input consists of pre-processed activity information and emotional data. The server analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. The output generates specific suggestions that should be provided to the user.

[0169] Step 5:

[0170] The server customizes the generated suggestions according to the user's cultural environment and life stage. The input consists of the generated suggestions and the user's basic profile information. Based on this, it creates personalized lifestyle suggestions. The output is the customized suggestions.

[0171] Step 6:

[0172] The server sends customized suggestions to the device. The input is the customized suggestions, which are then sent to the user's smartphone in an appropriate notification format. The output is the completion of sending the suggestions in a format the user can receive.

[0173] Step 7:

[0174] The user attempts to take specific actions based on the suggestions they receive. The input is a suggestion notified to their smartphone. The user then engages in specific activities, such as "open a meditation app and practice deep breathing for 5 minutes." The output is prepared to return feedback to the system, including the user's own impressions and the effects of these activities.

[0175] Step 8:

[0176] The server receives feedback from users and continuously improves its content. The input is user-provided feedback. The server analyzes this feedback, evaluates the effectiveness of suggestions and user reactions, and uses it as training data. The output is reflected in future suggestions, allowing the system to constantly evolve.

[0177] (Application Example 2)

[0178] 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".

[0179] In modern commercial facilities, there is a demand for personalized products and services that cater to the diverse emotional states and circumstances of customers. However, conventional systems struggle to accurately grasp a customer's emotional state and provide meaningful suggestions instantly. Therefore, to increase customer satisfaction, flexible service provision tailored to individual situations is necessary.

[0180] 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.

[0181] In this invention, the server includes means for acquiring user activity data and emotional information, means for analyzing the user's participation patterns based on the acquired data, and means for dynamically presenting products and services according to the user's situation and emotions. This enables the provision of personalized services to improve the customer experience and increase satisfaction within commercial facilities.

[0182] "User activity data" refers to information that indicates an individual's behavior and lifestyle, and includes geographical and biometric information.

[0183] "Emotional information" refers to data that indicates an individual's current psychological state, and is analyzed through factors such as voice tone and facial expressions.

[0184] A "participation pattern" is a characteristic behavioral model based on the user's behavioral tendencies and emotional changes.

[0185] "Dynamically presenting products and services" refers to a method of instantly displaying optimized product information and service guidance based on real-time data analysis results from users.

[0186] An "information display device" is a device that visually displays information about presented products or services, and includes monitors and displays.

[0187] "Response data" refers to data that records users' actions and opinions in response to suggestions, and can be used to improve future suggestions.

[0188] The system for implementing this invention acquires and analyzes user activity data and emotional information, and dynamically proposes products and services relevant to the user. The system utilizes hardware such as smart devices and robots placed in stores. These devices have the function of acquiring activity data and emotional information from users and transmitting it to a cloud server.

[0189] The server integrates with a data analysis engine to process the received data. AWS® Rekognition is used for facial recognition, and Google® Cloud Speech-to-Text is used for analyzing audio data. Based on these analysis results, the server understands the user's emotional state and participation patterns.

[0190] The server implements algorithms to accurately present products and services based on the user's real-time situation and emotional information. It uses a generative AI model to construct prompts and determine the information to be presented. Finally, the data is presented to the user through an information display device. It also collects user response data and feeds it back into the system's learning process.

[0191] As a concrete example, when implementing the system in a shopping mall, if it is determined that a customer is experiencing stress, the robot will generate and suggest a prompt such as, "We can show you a relaxing space here. Would you like to use it?" The AI ​​model used for generating prompts would look something like this:

[0192] Example prompt: "Your expression is relaxed and you are smiling. I will guide you to the outdoor equipment section."

[0193] In this way, the system can provide a personalized experience tailored to each customer, thereby improving user satisfaction.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The device collects user activity data. Using sensors and cameras, it acquires geographical and biometric information and transmits this data to a cloud server. The input is geographical and biometric information acquired from the user, and the output is the raw data transmitted to the cloud server.

[0197] Step 2:

[0198] The server analyzes the received data. It uses AWS Rekognition to analyze facial expression data and Google Cloud Speech-to-Text to convert speech data into text. This process allows the server to understand the user's emotional state. The input is raw data sent from the device, and the output is the analyzed emotional data.

[0199] Step 3:

[0200] The server determines the user's participation pattern based on the analyzed sentiment data. Using machine learning algorithms, it analyzes the user's past behavioral tendencies and emotional changes to generate the current participation pattern. The input is the analyzed sentiment data, and the output is the user's participation pattern.

[0201] Step 4:

[0202] The server generates prompt messages based on the user's situation using a generative AI model. These prompt messages are designed to suggest the most suitable products or services based on the user's current participation patterns and emotional state. The input is the user's participation patterns, and the output is the generated prompt message.

[0203] Step 5:

[0204] The terminal displays prompt messages to the user via an information display device. The suggested information is presented in a way that appeals to the user's sight and hearing. The input is the prompt message sent from the server, and the output is the presented information that the user views.

[0205] Step 6:

[0206] The user inputs response data to the suggestions via their device. This response data is recorded as the user's selections and feedback. The input is user feedback, and the output is the response data sent to the server.

[0207] Step 7:

[0208] The server continuously learns the system based on the collected response data. Based on the feedback, it adjusts the algorithm to improve prompt generation and suggestion accuracy for future sessions. The input is the user's response data, and the output is the learned and updated system.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention is a comprehensive lifestyle curation system designed to enrich the entire lives of users. Specifically, it generates and provides personalized suggestions based on activity data collected from each user, tailored to their individual lifestyle.

[0226] First, users use smartphones or wearable devices to record various activity information from their daily lives. This includes recording steps, location information, heart rate data, and meal records. The device sends this information to a cloud server, where it is stored as user activity data.

[0227] The server stores the received data while maintaining its integrity and analyzes it using machine learning algorithms. The server understands the user's activity patterns and health indicators and generates personalized recommendations. These recommendations take into account the user's life stage, cultural background, and ethical values, and are provided in a way that aligns with the user's values.

[0228] For example, if a user is a new employee who is busy with work during the week and lacks exercise, the server can use that activity data to suggest short exercises and healthy meals. These suggestions are sent to the user's smartphone and used as reference information to improve their daily activities.

[0229] Furthermore, users can provide feedback on suggestions via their devices. This feedback is sent to the server and used as training data to improve the accuracy of the suggestions. This feedback allows for suggestions tailored to the user's preferences, ultimately improving their quality of life in the long term.

[0230] In this way, the suggestion system based on user activity data functions as an effective means of improving users' quality of life and realizing a richer lifestyle. The coordination of servers, terminals, and users enables individualized responses tailored to each user's needs.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The device records activity data from the user's daily life. This data includes steps taken, heart rate, location information, and details of meals eaten. This data is sent from the device to a cloud server as it is collected.

[0234] Step 2:

[0235] The server receives activity data sent from the terminal and stores it in the database. It verifies the integrity of the data and preprocesses any incomplete data or outliers.

[0236] Step 3:

[0237] The server analyzes the user's lifestyle based on pre-processed data. Machine learning algorithms are used to evaluate the user's behavioral patterns and health indicators.

[0238] Step 4:

[0239] Based on the analysis results, the server generates lifestyle suggestions that take into account the user's life stage, cultural background, and ethical values. The suggestions cover a wide range of topics, including health management, exercise, nutritional balance, and relaxation methods.

[0240] Step 5:

[0241] The server notifies the user of the generated suggestions on their smartphone or wearable device. The suggestions are presented in a format that is easy for the user to immediately incorporate into their daily life.

[0242] Step 6:

[0243] Users implement the suggested actions and provide feedback on the results and their impressions through their device. This feedback serves as important data for evaluating how well users accepted the suggestions.

[0244] Step 7:

[0245] The server receives user feedback and stores it in a database. This data is used for continuous learning to improve the quality of suggestions. This allows for adjustments to make future suggestions more personalized.

[0246] (Example 1)

[0247] 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."

[0248] In today's world, improving the health and lifestyle quality of busy users is a crucial challenge. However, conventional methods fail to adequately address the individual user's background and preferences, making it difficult to provide effective lifestyle improvement solutions. In particular, the inability to fully utilize individual user activity data hinders the provision of optimal recommendations.

[0249] 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.

[0250] In this invention, the server includes means for acquiring information related to the user's activities, means for analyzing the user's lifestyle based on the information, and means for producing advice tailored to the user's social background and life stage using the generated AI model. This makes it possible to provide optimal suggestions for improving each user's life based on their individual needs.

[0251] "Information related to user activities" refers to data that reflects the user's activities in their daily life, such as location information, physiological state, exercise level, and dietary intake.

[0252] "Means of analyzing lifestyles" refers to a process or technology that uses information obtained from users to understand their unique behavioral patterns and health status, and to conduct analyses that are useful for improving their lifestyles.

[0253] A "generated AI model" is a model constructed using machine learning or artificial intelligence algorithms, which is used to analyze user data and generate individually optimized suggestions.

[0254] "Means of producing advice" refers to the process or technology for creating and providing suggestions to users based on their analysis results, tailored to their social background and life stage.

[0255] "Means of receiving responses and learning to continuously improve" refers to machine learning or data update processes that take user feedback and use it to improve the accuracy and appropriateness of suggestions.

[0256] This invention is a comprehensive lifestyle curation system designed to enrich the user's entire life. The system functions as a three-part system consisting of a server, terminals, and the user.

[0257] First, users use devices such as smartphones or wearable devices to record information related to their daily activities. This information includes steps taken, location data, heart rate, and food and drink intake. The devices send this information to a cloud server, where it is stored as the user's activity data.

[0258] The server analyzes the user's lifestyle based on the received data. Using machine learning frameworks such as Python and TensorFlow, the server leverages the generated AI model to analyze the user's activity patterns. This process makes it possible to produce advice that takes into account the user's social background and life stage.

[0259] As a concrete example, consider a user who has recently started a new job, is busy, and is not getting enough exercise. In this situation, the server would suggest exercise programs that can be completed in a short time, as well as healthy, time-saving meal plans. These suggestions would be notified to the user's device, and the user could receive them and use them in their daily life.

[0260] Furthermore, users can send feedback on suggestions to the server via their device. The server uses this feedback to learn and further improve the accuracy of its suggestions. The goal is to continuously improve the quality of advice in response to user preferences and changes.

[0261] An example of a prompt might be, "Please provide suggestions for improving exercise for a user who is a new member of the workforce." This system inputs such prompts into an AI model and builds a mechanism to generate appropriate suggestions. In this way, a suggestion system based on user activity data functions as an effective means of improving the user's quality of life and realizing a fulfilling lifestyle.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] Users record information related to their daily activities using devices such as smartphones and wearable devices. Specific actions include measuring steps, obtaining location information using GPS, monitoring heart rate, and manually entering or photographing meal details. This data is temporarily stored on the device. Input is user activity data, and output is data saved to the device's local storage.

[0265] Step 2:

[0266] The device periodically sends the collected activity data to a cloud server. This operation is performed automatically, for example, every hour, using HTTPS. The input is raw data stored on the device, and the output is user-specific activity data stored on the server.

[0267] Step 3:

[0268] The server checks the integrity of the received activity data and performs data cleansing before saving it to the database. Specifically, it uses a Python script to impute missing values ​​and remove outliers. The input is data sent from the terminal, and the output is data formatted for analysis.

[0269] Step 4:

[0270] The server analyzes the formatted data using a generation AI model. This AI model utilizes TensorFlow and analyzes the user's activity patterns and health status to generate suggestions tailored to the user's life stage and cultural background. The input is cleansed data, and the output is user-optimized suggestions.

[0271] Step 5:

[0272] The server sends the generated suggestions to the user's device. The device notifies the user of the received suggestions in the form of a pop-up notification or similar. The input is the suggestions generated by the AI ​​model, and the output is the information notified to the user.

[0273] Step 6:

[0274] Users send feedback on received suggestions to the server via their device. This feedback includes evaluations based on user preferences and the effectiveness of the suggestions. The input is the user's feedback, and the output is the training data sent to the server. The server uses this data to learn and improve the accuracy of future suggestions.

[0275] (Application Example 1)

[0276] 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."

[0277] Modern consumers have diverse lifestyles and individual needs, but general product and service offerings do not adequately meet them. Additionally, there is a lack of a mechanism to effectively utilize individual activity data to propose optimal products and services to users in real time. Therefore, specific technologies are required to further enrich the consumer experience.

[0278] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means. <000​​​​​​​​​​​​​​​​​​​​​​​​​"Reaction" refers to the feedback of opinions and actions shown by the user in response to a proposal, and this information is used to improve the system.

[0286] "Means of proposing products or services" refers to the technology that analyzes the user's behavioral data to select the optimal products or services and appropriately propose them to the user.

[0287] This invention provides a system for enriching the user's lifestyle, and its specific embodiments will be described below.

[0288] First, the user uses a smartphone or a wearable device (hereinafter referred to as "user device") to collect daily behavioral data. The user device is equipped with sensors for recording location information, distance traveled, body measurement information, and records of food and drink. This data is transmitted to the cloud server in real time.

[0289] The server uses the received behavioral data to analyze the user's lifestyle by leveraging dedicated data analysis software (such as machine learning frameworks like TensorFlow or PyTorch). Through this analysis, it is possible to generate proposals considering the user's life stage and cultural background. As a result, for example, appropriate exercise programs can be proposed to users with insufficient exercise, and the proposals further include information on appropriate products and event participation.

[0290] The user device is equipped with an interface that allows the user to easily provide feedback on the reaction to the proposal received from the server. This reaction is transmitted to the server and used as data to improve the accuracy of the proposal.

[0291] For example, if the system detects through analysis that a user is not getting enough exercise, it will generate a prompt such as, "How about a 15-minute yoga set that fits your busy weekday schedule?" and suggest it to the user. This prompt will be displayed on the user's smart glasses and provided as reference information for daily health management.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The user device uses built-in sensors to collect behavioral data in real time, including location information, distance traveled, body measurements, and records of eating and drinking. This data is primarily obtained from accelerometers, GPS sensors, and heart rate sensors. The collected data is temporarily stored in the device's memory.

[0295] Step 2:

[0296] The user device transmits the collected behavioral data to the cloud server. To protect user privacy, the data is securely transferred via an encryption protocol. The input is the user's behavioral data, and the output is the raw data uploaded to the server.

[0297] Step 3:

[0298] The server stores the received behavioral data in a database. During this process, data integrity is ensured, and data is managed separately for each user. Next, machine learning frameworks such as TensorFlow and PyTorch are used to analyze the data. The input is raw behavioral data, and the output is the analysis results regarding the user's lifestyle.

[0299] Step 4:

[0300] The server generates product suggestions that take into account the user's lifestyle and cultural background, based on the analysis results. This generation utilizes natural language processing technology and a generative AI model as its platform. In this example, prompts such as "How about a 15-minute yoga set suitable for your busy weekday schedule?" are generated. The input is the analysis results, and the output is the suggested prompt.

[0301] Step 5:

[0302] The user device notifies the user of the prompt message received from the server. In doing so, smart glasses or smartphone notification functions are used to ensure the user receives the suggestions appropriately. The input is the suggestion prompt message, and the output is the notification to the user.

[0303] Step 6:

[0304] Users provide feedback on the received suggestions through their device. This feedback is easily done using the terminal's input interface and sent to the server. The input is the user's feedback, and the output is data that helps improve the suggestions.

[0305] 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.

[0306] This invention is a comprehensive system that analyzes a user's activity data and emotional state to provide suggestions for improving the user's lifestyle. The system collects and analyzes data from the user's daily life using their smartphone or wearable device.

[0307] The terminal records activity data such as the user's location information, heart rate, number of steps, and meal records. Furthermore, by combining an emotion engine, it analyzes the user's emotions through voice tone, facial expression recognition, and biometric signals, and transmits the data to the cloud server.

[0308] The server stores the received activity data and emotion data in a database and first performs preprocessing of the data. Then, it analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. Through this analysis, proposals regarding the user's lifestyle are generated. The proposals are customized according to the user's cultural background and life stage, and also include content aimed at stress management and improvement of mental health based on the emotional state.

[0309] As a specific example, when the user is presumed to be in a stressful situation, the server proposes relaxation methods or the use of meditation apps corresponding to that emotional state. Also, proposals for diet content and exercise are adjusted according to the emotional state of the day and are proposed at the timing that is considered most beneficial for the user.

[0310] The proposals are notified to the user's smartphone and presented in an executable form. The user provides feedback on this, and the server uses this feedback to conduct system learning and improvement of the proposed content. As a result, the user can receive more personalized lifestyle advice.

[0311] With this system, it becomes possible to understand the user's daily life and emotional situation in real time and to improve the appropriate lifestyle. Through the cooperation between the server and the terminal, solutions corresponding to the various needs of the user are provided.

[0312] The following explains the processing flow.

[0313] Step 1:

[0314] The device records the user's daily activity data and emotional data. This includes location information, heart rate, steps, and meal records obtained from the user's smartphone or wearable device, as well as emotional data such as voice tone, facial expressions, and heart rate variability obtained using an emotion engine.

[0315] Step 2:

[0316] The device transmits collected activity and emotion data to a cloud server. Data transmission occurs in real time or at specified intervals, while maintaining data integrity.

[0317] Step 3:

[0318] The server receives data sent from the terminal and stores it in the database. It detects missing or abnormal values ​​in the data and performs preprocessing such as imputation or removal as needed.

[0319] Step 4:

[0320] The server applies machine learning algorithms based on pre-processed data to analyze user behavior and emotions. At this stage, it extracts user lifestyle patterns and emotional trends, and evaluates stress levels, happiness levels, and other factors.

[0321] Step 5:

[0322] Based on the analysis results, the server generates suggestions tailored to the user's current lifestyle and emotional state. These suggestions include health management, exercise, dietary improvements, stress reduction methods, and relaxation and mental health support.

[0323] Step 6:

[0324] The server notifies the user's device of the generated suggestions. The suggestions are provided in an easy-to-follow format and may include, for example, immediately actionable exercise videos, recommended recipes, or the use of meditation apps.

[0325] Step 7:

[0326] Users receive notifications and act accordingly. They can also record feedback on the suggestions and their effects through their device.

[0327] Step 8:

[0328] The server collects feedback from users and stores it in a database. This data is used to update the machine learning model, continuously improving the quality and accuracy of suggestions.

[0329] This flow allows users to improve their quality of life by consistently monitoring and caring for their lifestyle and emotions.

[0330] (Example 2)

[0331] 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".

[0332] In modern society, personalized health management and stress management are in demand. However, existing systems struggle to effectively integrate and analyze activity information and emotional states to propose lifestyles optimized for individuals. Furthermore, there is a lack of adequate methods for using user feedback to improve the accuracy of these suggestions and train the systems accordingly. In this situation, there is a need for a system that can provide detailed support tailored to the diverse needs of users.

[0333] 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.

[0334] In this invention, the server includes means for collecting user activity information, means for analyzing emotional states, and means for creating and notifying personalized suggestions. This enables the integrated analysis of user activity information and emotional states to provide personalized lifestyle suggestions. Furthermore, by incorporating user feedback, the system can continuously learn and provide highly accurate suggestions.

[0335] A "user" is a person who uses the system and receives suggestions based on the analysis of their activity information and emotional state.

[0336] "Activity information" refers to data generated by users in their daily lives, such as location information, pulse rate, distance traveled, and meal history.

[0337] "Emotional state" refers to the user's psychological state as determined through analysis of voice tone and facial expressions.

[0338] "Suggestions" refer to information created based on the user's activity data and emotional state, aimed at improving their lifestyle, such as meal plans, exercise plans, and relaxation methods.

[0339] "Feedback" refers to the evaluations and opinions that users provide to the system regarding the acceptance and implementation of their suggestions.

[0340] "Means of continuous learning and improvement" refers to the process by which the system uses user feedback to update its algorithms and suggestions, enabling it to make more accurate suggestions.

[0341] "User device" refers to a device used by a user to receive suggestions, and includes smartphones and wearable devices.

[0342] "Lifestyle" refers to how a user spends their daily life, according to their cultural environment and stage of life, as well as the habits and behaviors that support it.

[0343] This invention is a system for providing personalized lifestyle improvement suggestions by comprehensively analyzing a user's activity information and emotional state. Specific embodiments are described below.

[0344] First, the device uses hardware such as smartphones and wearable devices to collect activity information in real time, including location data, pulse rate, distance traveled, and meal history, generated during the user's daily life. Regarding emotional states, the device's built-in microphone and camera are used to analyze voice tone and facial expressions. For this purpose, facial recognition software and voice analysis software are employed. All of this data is transmitted to a server in a cloud environment.

[0345] The server stores the received activity and emotion data in a database and then performs preprocessing. This involves noise reduction and correction of anomalous data to create a clean dataset. Next, machine learning algorithms and generative AI models are used to analyze the user's lifestyle and emotional state. Based on the analysis results, lifestyle suggestions tailored to the user are generated. These suggestions are created taking into account the user's cultural environment and life stage, and are then sent back to the device via the cloud.

[0346] For example, if the server detects high stress levels based on the user's emotional state, it will suggest relaxation methods or the use of a meditation app. Such suggestions will be notified to the user's device in the form of, for example, "Please open a meditation app to practice deep breathing." An example of a prompt message might be, "Based on the user's activity information and emotional state, please generate specific suggestions to alleviate stress."

[0347] Users try out actions based on the suggestions they receive and provide feedback to the system. Based on this feedback, the server updates its learning algorithm to further improve the accuracy of its suggestions. Through this cycle, the suggestions provided to users become more personalized and effective over time.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The device collects user activity information. Inputs include location data, pulse rate, distance traveled, and meal history obtained from smartphones and wearable devices. This data is acquired using sensors and applications, and processed in real time. The output is processed into a format that can be sent to a cloud server.

[0351] Step 2:

[0352] The device analyzes the user's emotional state. Inputs include voice tone and facial expression data acquired through the device's built-in microphone and camera. This data is processed by emotion analysis software to understand the user's emotional state. Output is analyzed emotion data, which is also sent to a cloud server.

[0353] Step 3:

[0354] The server receives collected activity and sentiment data. All data from the terminal is received as input and stored in the database. The server preprocesses the received data, removing noise and imputing missing values ​​to generate a clean dataset. The output is processed data suitable for analysis by machine learning algorithms.

[0355] Step 4:

[0356] The server performs lifestyle analysis based on a generative AI model using pre-processed data. The input consists of pre-processed activity information and emotional data. The server analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. The output generates specific suggestions that should be provided to the user.

[0357] Step 5:

[0358] The server customizes the generated suggestions according to the user's cultural environment and life stage. The input consists of the generated suggestions and the user's basic profile information. Based on this, it creates personalized lifestyle suggestions. The output is the customized suggestions.

[0359] Step 6:

[0360] The server sends customized suggestions to the device. The input is the customized suggestions, which are then sent to the user's smartphone in an appropriate notification format. The output is the completion of sending the suggestions in a format the user can receive.

[0361] Step 7:

[0362] The user attempts to take specific actions based on the suggestions they receive. The input is a suggestion notified to their smartphone. The user then engages in specific activities, such as "open a meditation app and practice deep breathing for 5 minutes." The output is prepared to return feedback to the system, including the user's own impressions and the effects of these activities.

[0363] Step 8:

[0364] The server receives feedback from users and continuously improves its content. The input is user-provided feedback. The server analyzes this feedback, evaluates the effectiveness of suggestions and user reactions, and uses it as training data. The output is reflected in future suggestions, allowing the system to constantly evolve.

[0365] (Application Example 2)

[0366] 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."

[0367] In modern commercial facilities, there is a demand for personalized products and services that cater to the diverse emotional states and circumstances of customers. However, conventional systems struggle to accurately grasp a customer's emotional state and provide meaningful suggestions instantly. Therefore, to increase customer satisfaction, flexible service provision tailored to individual situations is necessary.

[0368] 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.

[0369] In this invention, the server includes means for acquiring user activity data and emotional information, means for analyzing the user's participation patterns based on the acquired data, and means for dynamically presenting products and services according to the user's situation and emotions. This enables the provision of personalized services to improve the customer experience and increase satisfaction within commercial facilities.

[0370] "User activity data" refers to information that indicates an individual's behavior and lifestyle, and includes geographical and biometric information.

[0371] "Emotional information" refers to data that indicates an individual's current psychological state, and is analyzed through factors such as voice tone and facial expressions.

[0372] A "participation pattern" is a characteristic behavioral model based on the user's behavioral tendencies and emotional changes.

[0373] "Dynamically presenting products and services" refers to a method of instantly displaying optimized product information and service guidance based on real-time data analysis results from users.

[0374] An "information display device" is a device that visually displays information about presented products or services, and includes monitors and displays.

[0375] "Response data" refers to data that records users' actions and opinions in response to suggestions, and can be used to improve future suggestions.

[0376] The system for implementing this invention acquires and analyzes user activity data and emotional information, and dynamically proposes products and services relevant to the user. The system utilizes hardware such as smart devices and robots placed in stores. These devices have the function of acquiring activity data and emotional information from users and transmitting it to a cloud server.

[0377] The server integrates with a data analysis engine to process the received data. AWS Rekognition is used for facial recognition, and Google Cloud Speech-to-Text is used for analyzing audio data. Based on these analysis results, the server understands the user's emotional state and participation patterns.

[0378] The server implements algorithms to accurately present products and services based on the user's real-time situation and emotional information. It uses a generative AI model to construct prompts and determine the information to be presented. Finally, the data is presented to the user through an information display device. It also collects user response data and feeds it back into the system's learning process.

[0379] As a concrete example, when implementing the system in a shopping mall, if it is determined that a customer is experiencing stress, the robot will generate and suggest a prompt such as, "We can show you a relaxing space here. Would you like to use it?" The AI ​​model used for generating prompts would look something like this:

[0380] Example prompt: "Your expression is relaxed and you are smiling. I will guide you to the outdoor equipment section."

[0381] In this way, the system can provide a personalized experience tailored to each customer, thereby improving user satisfaction.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The device collects user activity data. Using sensors and cameras, it acquires geographical and biometric information and transmits this data to a cloud server. The input is geographical and biometric information acquired from the user, and the output is the raw data transmitted to the cloud server.

[0385] Step 2:

[0386] The server analyzes the received data. It uses AWS Rekognition to analyze facial expression data and Google Cloud Speech-to-Text to convert speech data into text. This process allows the server to understand the user's emotional state. The input is raw data sent from the device, and the output is the analyzed emotional data.

[0387] Step 3:

[0388] The server determines the user's participation pattern based on the analyzed sentiment data. Using machine learning algorithms, it analyzes the user's past behavioral tendencies and emotional changes to generate the current participation pattern. The input is the analyzed sentiment data, and the output is the user's participation pattern.

[0389] Step 4:

[0390] The server generates prompt messages based on the user's situation using a generative AI model. These prompt messages are designed to suggest the most suitable products or services based on the user's current participation patterns and emotional state. The input is the user's participation patterns, and the output is the generated prompt message.

[0391] Step 5:

[0392] The terminal displays prompt messages to the user via an information display device. The suggested information is presented in a way that appeals to the user's sight and hearing. The input is the prompt message sent from the server, and the output is the presented information that the user views.

[0393] Step 6:

[0394] The user inputs response data to the suggestions via their device. This response data is recorded as the user's selections and feedback. The input is user feedback, and the output is the response data sent to the server.

[0395] Step 7:

[0396] The server continuously learns the system based on the collected response data. Based on the feedback, it adjusts the algorithm to improve prompt generation and suggestion accuracy for future sessions. The input is the user's response data, and the output is the learned and updated system.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention is a comprehensive lifestyle curation system designed to enrich the entire lives of users. Specifically, it generates and provides personalized suggestions based on activity data collected from each user, tailored to their individual lifestyle.

[0414] First, users use smartphones or wearable devices to record various activity information from their daily lives. This includes recording steps, location information, heart rate data, and meal records. The device sends this information to a cloud server, where it is stored as user activity data.

[0415] The server stores the received data while maintaining its integrity and analyzes it using machine learning algorithms. The server understands the user's activity patterns and health indicators and generates personalized recommendations. These recommendations take into account the user's life stage, cultural background, and ethical values, and are provided in a way that aligns with the user's values.

[0416] For example, if a user is a new employee who is busy with work during the week and lacks exercise, the server can use that activity data to suggest short exercises and healthy meals. These suggestions are sent to the user's smartphone and used as reference information to improve their daily activities.

[0417] Furthermore, users can provide feedback on suggestions via their devices. This feedback is sent to the server and used as training data to improve the accuracy of the suggestions. This feedback allows for suggestions tailored to the user's preferences, ultimately improving their quality of life in the long term.

[0418] In this way, the suggestion system based on user activity data functions as an effective means of improving users' quality of life and realizing a richer lifestyle. The coordination of servers, terminals, and users enables individualized responses tailored to each user's needs.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The device records activity data from the user's daily life. This data includes steps taken, heart rate, location information, and details of meals eaten. This data is sent from the device to a cloud server as it is collected.

[0422] Step 2:

[0423] The server receives activity data sent from the terminal and stores it in the database. It verifies the integrity of the data and preprocesses any incomplete data or outliers.

[0424] Step 3:

[0425] The server analyzes the user's lifestyle based on pre-processed data. It uses machine learning algorithms to evaluate the user's behavioral patterns and health indicators.

[0426] Step 4:

[0427] Based on the analysis results, the server generates lifestyle suggestions that take into account the user's life stage, cultural background, and ethical values. The suggestions cover a wide range of topics, including health management, exercise, nutritional balance, and relaxation methods.

[0428] Step 5:

[0429] The server notifies the user of the generated suggestions on their smartphone or wearable device. The suggestions are presented in a format that is easy for the user to immediately incorporate into their daily life.

[0430] Step 6:

[0431] Users act on the suggested actions and provide feedback on the results and their impressions through their device. This feedback serves as important data for evaluating how well users accepted the suggestions.

[0432] Step 7:

[0433] The server receives user feedback and stores it in a database. This data is used for continuous learning to improve the quality of suggestions. This allows for adjustments to make future suggestions more personalized.

[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] In today's world, improving the health and lifestyle quality of busy users is a crucial challenge. However, conventional methods fail to adequately address the individual user's background and preferences, making it difficult to provide effective lifestyle improvement solutions. In particular, the inability to fully utilize individual user activity data hinders the provision of optimal recommendations.

[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 acquiring information related to the user's activities, means for analyzing the user's lifestyle based on the information, and means for producing advice tailored to the user's social background and life stage using the generated AI model. This makes it possible to provide optimal suggestions for improving each user's life based on their individual needs.

[0439] "Information related to user activities" refers to data that reflects the user's activities in their daily life, such as location information, physiological state, exercise level, and dietary intake.

[0440] "Means of analyzing lifestyles" refers to a process or technology that uses information obtained from users to understand their unique behavioral patterns and health status, and to conduct analyses that are useful for improving their lifestyles.

[0441] A "generated AI model" is a model constructed using machine learning or artificial intelligence algorithms, which is used to analyze user data and generate individually optimized suggestions.

[0442] "Means of producing advice" refers to the process or technology for creating and providing suggestions to users based on their analysis results, tailored to their social background and life stage.

[0443] "Means of receiving responses and learning to continuously improve" refers to machine learning or data update processes that take user feedback and use it to improve the accuracy and appropriateness of suggestions.

[0444] This invention is a comprehensive lifestyle curation system designed to enrich the user's entire life. The system functions as a three-part system consisting of a server, terminals, and the user.

[0445] First, users use devices such as smartphones or wearable devices to record information related to their daily activities. This information includes steps taken, location data, heart rate, and food and drink intake. The devices send this information to a cloud server, where it is stored as the user's activity data.

[0446] The server analyzes the user's lifestyle based on the received data. Using machine learning frameworks such as Python and TensorFlow, the server leverages the generated AI model to analyze the user's activity patterns. This process makes it possible to produce advice that takes into account the user's social background and life stage.

[0447] As a concrete example, consider a user who has recently started a new job, is busy, and is not getting enough exercise. In this situation, the server would suggest exercise programs that can be completed in a short time, as well as healthy, time-saving meal plans. These suggestions would be notified to the user's device, and the user could receive them and use them in their daily life.

[0448] Furthermore, users can send feedback on suggestions to the server via their device. The server uses this feedback to learn and further improve the accuracy of its suggestions. The goal is to continuously improve the quality of advice in response to user preferences and changes.

[0449] An example of a prompt might be, "Please provide suggestions for improving exercise for a user who is a new member of the workforce." This system inputs such prompts into an AI model and builds a mechanism to generate appropriate suggestions. In this way, a suggestion system based on user activity data functions as an effective means of improving the user's quality of life and realizing a fulfilling lifestyle.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] Users record information related to their daily activities using devices such as smartphones and wearable devices. Specific actions include measuring steps, obtaining location information using GPS, monitoring heart rate, and manually entering or photographing meal details. This data is temporarily stored on the device. Input is user activity data, and output is data saved to the device's local storage.

[0453] Step 2:

[0454] The device periodically sends the collected activity data to a cloud server. This operation is performed automatically, for example, every hour, using HTTPS. The input is raw data stored on the device, and the output is user-specific activity data stored on the server.

[0455] Step 3:

[0456] The server checks the integrity of the received activity data and performs data cleansing before saving it to the database. Specifically, it uses a Python script to impute missing values ​​and remove outliers. The input is data sent from the terminal, and the output is data formatted for analysis.

[0457] Step 4:

[0458] The server analyzes the formatted data using a generation AI model. This AI model utilizes TensorFlow and analyzes the user's activity patterns and health status to generate suggestions tailored to the user's life stage and cultural background. The input is cleansed data, and the output is user-optimized suggestions.

[0459] Step 5:

[0460] The server sends the generated suggestions to the user's device. The device notifies the user of the received suggestions in the form of a pop-up notification or similar. The input is the suggestions generated by the AI ​​model, and the output is the information notified to the user.

[0461] Step 6:

[0462] Users send feedback on received suggestions to the server via their device. This feedback includes evaluations based on user preferences and the effectiveness of the suggestions. The input is the user's feedback, and the output is the training data sent to the server. The server uses this data to learn and improve the accuracy of future suggestions.

[0463] (Application Example 1)

[0464] 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."

[0465] Modern consumers have diverse lifestyles and individual needs, but general product and service offerings are not adequately addressing them. Furthermore, there is a lack of systems that effectively utilize individual activity data to suggest the most suitable products and services to users in real time. Therefore, there is a need for specific technologies to further enrich the consumer experience.

[0466] 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.

[0467] In this invention, the server includes means for collecting user behavior data, means for analyzing the user's lifestyle based on the behavior data, means for generating suggestions tailored to the user's life stage and cultural background, and means for suggesting products and services based on the behavior data. This makes it possible to quickly and accurately suggest products and services suitable for each user, thereby improving the consumer experience.

[0468] "Behavioral data" refers to information about a user's activities, including location, physical information, distance traveled, and records of eating and drinking.

[0469] "Lifestyle" refers to a user's daily habits and behavioral patterns, encompassing their entire way of life based on their individual values ​​and cultural background.

[0470] "Life stages" refer to different phases in a user's life, such as being a new employee, having a family, or being retired.

[0471] "Suggestions" refer to specific recommendations and suggestions about products and services that are generated based on users' lifestyle and behavioral data.

[0472] "User device" refers to a terminal operated by a user, and includes smartphones, wearable devices, or other interfaces.

[0473] "Responses" refer to feedback from users regarding their opinions and actions in response to suggestions, and this information is used to improve the system.

[0474] "Methods for proposing products and services" refers to technologies that analyze user behavior data to select the most suitable products and services and propose them appropriately to users.

[0475] This invention provides a system to enrich the user's lifestyle, and its specific embodiments are described below.

[0476] First, users collect daily activity data using smartphones or wearable devices (hereinafter referred to as "user devices"). User devices are equipped with sensors to record location information, distance traveled, body measurements, and records of eating and drinking. This data is transmitted to a cloud server in real time.

[0477] The server uses the received behavioral data to analyze the user's lifestyle using specialized data analysis software (such as machine learning frameworks like TensorFlow or PyTorch). This analysis makes it possible to generate suggestions that take into account the user's life stage and cultural background. For example, it can suggest an appropriate exercise program to a user who is not getting enough exercise, and these suggestions can include information on suitable products and events.

[0478] The user device is equipped with an interface that allows the user to easily provide feedback on suggestions received from the server. This feedback is sent to the server and used as data to improve the accuracy of the suggestions.

[0479] For example, if the system detects through analysis that a user is not getting enough exercise, it will generate a prompt such as, "How about a 15-minute yoga set that fits your busy weekday schedule?" and suggest it to the user. This prompt will be displayed on the user's smart glasses and provided as reference information for daily health management.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The user device uses built-in sensors to collect behavioral data in real time, including location information, distance traveled, body measurements, and records of eating and drinking. This data is primarily obtained from accelerometers, GPS sensors, and heart rate sensors. The collected data is temporarily stored in the device's memory.

[0483] Step 2:

[0484] The user device transmits the collected behavioral data to the cloud server. To protect user privacy, the data is securely transferred via an encryption protocol. The input is the user's behavioral data, and the output is the raw data uploaded to the server.

[0485] Step 3:

[0486] The server stores the received behavioral data in a database. During this process, data integrity is ensured, and data is managed separately for each user. Next, machine learning frameworks such as TensorFlow and PyTorch are used to analyze the data. The input is raw behavioral data, and the output is the analysis results regarding the user's lifestyle.

[0487] Step 4:

[0488] The server generates product suggestions that take into account the user's lifestyle and cultural background, based on the analysis results. This generation utilizes natural language processing technology and a generative AI model as its platform. In this example, prompts such as "How about a 15-minute yoga set suitable for your busy weekday schedule?" are generated. The input is the analysis results, and the output is the suggested prompt.

[0489] Step 5:

[0490] The user device notifies the user of the prompt message received from the server. In doing so, smart glasses or smartphone notification functions are used to ensure the user receives the suggestions appropriately. The input is the suggestion prompt message, and the output is the notification to the user.

[0491] Step 6:

[0492] Users provide feedback on the received suggestions through their device. This feedback is easily done using the terminal's input interface and sent to the server. The input is the user's feedback, and the output is data that helps improve the suggestions.

[0493] 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.

[0494] This invention is a comprehensive system that analyzes a user's activity data and emotional state to provide suggestions for improving the user's lifestyle. The system collects and analyzes data from the user's daily life using their smartphone or wearable device.

[0495] The device records activity data such as the user's location, heart rate, steps taken, and meal records. Furthermore, by combining it with an emotion engine, it analyzes the user's emotions through voice tone, facial recognition, and biosignals, and sends that data to a cloud server.

[0496] The server stores received activity and emotional data in a database and first performs data preprocessing. Then, it analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. Through this analysis, lifestyle suggestions are generated. These suggestions are customized according to the user's cultural background and life stage, and include content aimed at stress management and mental health improvement based on their emotional state.

[0497] For example, if the server suspects a user is experiencing high stress levels, it will suggest relaxation methods or meditation apps that address that emotional state. Furthermore, suggestions for meals and exercise are adjusted according to the user's emotional state on any given day and presented at the most beneficial time for them.

[0498] The suggestions are notified to the user's smartphone and presented in an actionable format. The user provides feedback, and the server uses this feedback to learn from the system and improve the suggestions. This allows the user to receive more personalized lifestyle advice.

[0499] This system allows for a real-time understanding of users' daily lives and emotional states, enabling appropriate lifestyle improvements. The integration of the server and terminals provides solutions tailored to diverse user needs.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The device records the user's daily activity data and emotional data. This includes location information, heart rate, steps, and meal records obtained from the user's smartphone or wearable device, as well as emotional data such as voice tone, facial expressions, and heart rate variability obtained using an emotion engine.

[0503] Step 2:

[0504] The device transmits collected activity and emotion data to a cloud server. Data transmission occurs in real time or at specified intervals, while maintaining data integrity.

[0505] Step 3:

[0506] The server receives data sent from the terminal and stores it in the database. It detects missing or abnormal values ​​in the data and performs preprocessing such as imputation or removal as needed.

[0507] Step 4:

[0508] The server applies machine learning algorithms based on pre-processed data to analyze user behavior and emotions. At this stage, it extracts user lifestyle patterns and emotional trends, and evaluates stress levels, happiness levels, and other factors.

[0509] Step 5:

[0510] Based on the analysis results, the server generates suggestions tailored to the user's current lifestyle and emotional state. These suggestions include health management, exercise, dietary improvements, stress reduction methods, and relaxation and mental health support.

[0511] Step 6:

[0512] The server notifies the user's device of the generated suggestions. The suggestions are provided in an easy-to-follow format and may include, for example, immediately actionable exercise videos, recommended recipes, or the use of meditation apps.

[0513] Step 7:

[0514] Users receive notifications and act accordingly. They can also record feedback on the suggestions and their effects through their device.

[0515] Step 8:

[0516] The server collects feedback from users and stores it in a database. This data is used to update the machine learning model, continuously improving the quality and accuracy of suggestions.

[0517] This flow allows users to improve their quality of life by consistently monitoring and caring for their lifestyle and emotions.

[0518] (Example 2)

[0519] 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."

[0520] In modern society, personalized health management and stress management are in demand. However, existing systems struggle to effectively integrate and analyze activity information and emotional states to propose lifestyles optimized for individuals. Furthermore, there is a lack of adequate methods for using user feedback to improve the accuracy of these suggestions and train the systems accordingly. In this situation, there is a need for a system that can provide detailed support tailored to the diverse needs of users.

[0521] 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.

[0522] In this invention, the server includes means for collecting user activity information, means for analyzing emotional states, and means for creating and notifying personalized suggestions. This enables the integrated analysis of user activity information and emotional states to provide personalized lifestyle suggestions. Furthermore, by incorporating user feedback, the system can continuously learn and provide highly accurate suggestions.

[0523] A "user" is a person who uses the system and receives suggestions based on the analysis of their activity information and emotional state.

[0524] "Activity information" refers to data generated by users in their daily lives, such as location information, pulse rate, distance traveled, and meal history.

[0525] "Emotional state" refers to the user's psychological state as determined through analysis of voice tone and facial expressions.

[0526] "Suggestions" refer to information created based on the user's activity data and emotional state, aimed at improving their lifestyle, such as meal plans, exercise plans, and relaxation methods.

[0527] "Feedback" refers to the evaluations and opinions that users provide to the system regarding the acceptance and implementation of their suggestions.

[0528] "Means of continuous learning and improvement" refers to the process by which the system uses user feedback to update its algorithms and suggestions, enabling it to make more accurate suggestions.

[0529] "User device" refers to a device used by a user to receive suggestions, and includes smartphones and wearable devices.

[0530] "Lifestyle" refers to how a user spends their daily life, according to their cultural environment and stage of life, as well as the habits and behaviors that support it.

[0531] This invention is a system for providing personalized lifestyle improvement suggestions by comprehensively analyzing a user's activity information and emotional state. Specific embodiments are described below.

[0532] First, the device uses hardware such as smartphones and wearable devices to collect activity information in real time, including location data, pulse rate, distance traveled, and meal history, generated during the user's daily life. Regarding emotional states, the device's built-in microphone and camera are used to analyze voice tone and facial expressions. For this purpose, facial recognition software and voice analysis software are employed. All of this data is transmitted to a server in a cloud environment.

[0533] The server stores the received activity and emotion data in a database and then performs preprocessing. This involves noise reduction and correction of anomalous data to create a clean dataset. Next, machine learning algorithms and generative AI models are used to analyze the user's lifestyle and emotional state. Based on the analysis results, lifestyle suggestions tailored to the user are generated. These suggestions are created taking into account the user's cultural environment and life stage, and are then sent back to the device via the cloud.

[0534] For example, if the server detects high stress levels based on the user's emotional state, it will suggest relaxation methods or the use of a meditation app. Such suggestions will be notified to the user's device in the form of, for example, "Please open a meditation app to practice deep breathing." An example of a prompt message might be, "Based on the user's activity information and emotional state, please generate specific suggestions to alleviate stress."

[0535] Users try out actions based on the suggestions they receive and provide feedback to the system. Based on this feedback, the server updates its learning algorithm to further improve the accuracy of its suggestions. Through this cycle, the suggestions provided to users become more personalized and effective over time.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] The device collects user activity information. Inputs include location data, pulse rate, distance traveled, and meal history obtained from smartphones and wearable devices. This data is acquired using sensors and applications, and processed in real time. The output is processed into a format that can be sent to a cloud server.

[0539] Step 2:

[0540] The device analyzes the user's emotional state. Inputs include voice tone and facial expression data acquired through the device's built-in microphone and camera. This data is processed by emotion analysis software to understand the user's emotional state. Output is analyzed emotion data, which is also sent to a cloud server.

[0541] Step 3:

[0542] The server receives collected activity and sentiment data. All data from the terminal is received as input and stored in the database. The server preprocesses the received data, removing noise and imputing missing values ​​to generate a clean dataset. The output is processed data suitable for analysis by machine learning algorithms.

[0543] Step 4:

[0544] The server performs lifestyle analysis based on a generative AI model using pre-processed data. The input consists of pre-processed activity information and emotional data. The server analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. The output generates specific suggestions that should be provided to the user.

[0545] Step 5:

[0546] The server customizes the generated suggestions according to the user's cultural environment and life stage. The input consists of the generated suggestions and the user's basic profile information. Based on this, it creates personalized lifestyle suggestions. The output is the customized suggestions.

[0547] Step 6:

[0548] The server sends customized suggestions to the device. The input is the customized suggestions, which are then sent to the user's smartphone in an appropriate notification format. The output is the completion of sending the suggestions in a format the user can receive.

[0549] Step 7:

[0550] The user attempts to take specific actions based on the suggestions they receive. The input is a suggestion notified to their smartphone. The user then engages in specific activities, such as "open a meditation app and practice deep breathing for 5 minutes." The output is prepared to return feedback to the system, including the user's own impressions and the effects of these activities.

[0551] Step 8:

[0552] The server receives feedback from users and continuously improves its content. The input is user-provided feedback. The server analyzes this feedback, evaluates the effectiveness of suggestions and user reactions, and uses it as training data. The output is reflected in future suggestions, allowing the system to constantly evolve.

[0553] (Application Example 2)

[0554] 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."

[0555] In modern commercial facilities, there is a demand for personalized products and services that cater to the diverse emotional states and circumstances of customers. However, conventional systems struggle to accurately grasp a customer's emotional state and provide meaningful suggestions instantly. Therefore, to increase customer satisfaction, flexible service provision tailored to individual situations is necessary.

[0556] 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.

[0557] In this invention, the server includes means for acquiring user activity data and emotional information, means for analyzing the user's participation patterns based on the acquired data, and means for dynamically presenting products and services according to the user's situation and emotions. This enables the provision of personalized services to improve the customer experience and increase satisfaction within commercial facilities.

[0558] "User activity data" refers to information that indicates an individual's behavior and lifestyle, and includes geographical and biometric information.

[0559] "Emotional information" refers to data that indicates an individual's current psychological state, and is analyzed through factors such as voice tone and facial expressions.

[0560] A "participation pattern" is a characteristic behavioral model based on the user's behavioral tendencies and emotional changes.

[0561] "Dynamically presenting products and services" refers to a method of instantly displaying optimized product information and service guidance based on real-time data analysis results from users.

[0562] An "information display device" is a device that visually displays information about presented products or services, and includes monitors and displays.

[0563] "Response data" refers to data that records users' actions and opinions in response to suggestions, and can be used to improve future suggestions.

[0564] The system for implementing this invention acquires and analyzes user activity data and emotional information, and dynamically proposes products and services relevant to the user. The system utilizes hardware such as smart devices and robots placed in stores. These devices have the function of acquiring activity data and emotional information from users and transmitting it to a cloud server.

[0565] The server integrates with a data analysis engine to process the received data. AWS Rekognition is used for facial recognition, and Google Cloud Speech-to-Text is used for analyzing audio data. Based on these analysis results, the server understands the user's emotional state and participation patterns.

[0566] The server implements algorithms to accurately present products and services based on the user's real-time situation and emotional information. It uses a generative AI model to construct prompts and determine the information to be presented. Finally, the data is presented to the user through an information display device. It also collects user response data and feeds it back into the system's learning process.

[0567] As a concrete example, when implementing the system in a shopping mall, if it is determined that a customer is experiencing stress, the robot will generate and suggest a prompt such as, "We can show you a relaxing space here. Would you like to use it?" The AI ​​model used for generating prompts would look something like this:

[0568] Example prompt: "Your expression is relaxed and you are smiling. I will guide you to the outdoor equipment section."

[0569] In this way, the system can provide a personalized experience tailored to each customer, thereby improving user satisfaction.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The device collects user activity data. Using sensors and cameras, it acquires geographical and biometric information and transmits this data to a cloud server. The input is geographical and biometric information acquired from the user, and the output is the raw data transmitted to the cloud server.

[0573] Step 2:

[0574] The server analyzes the received data. It uses AWS Rekognition to analyze facial expression data and Google Cloud Speech-to-Text to convert speech data into text. This process allows the server to understand the user's emotional state. The input is raw data sent from the device, and the output is the analyzed emotional data.

[0575] Step 3:

[0576] The server determines the user's participation pattern based on the analyzed sentiment data. Using machine learning algorithms, it analyzes the user's past behavioral tendencies and emotional changes to generate the current participation pattern. The input is the analyzed sentiment data, and the output is the user's participation pattern.

[0577] Step 4:

[0578] The server generates prompt messages based on the user's situation using a generative AI model. These prompt messages are designed to suggest the most suitable products or services based on the user's current participation patterns and emotional state. The input is the user's participation patterns, and the output is the generated prompt message.

[0579] Step 5:

[0580] The terminal displays prompt messages to the user via an information display device. The suggested information is presented in a way that appeals to the user's sight and hearing. The input is the prompt message sent from the server, and the output is the presented information that the user views.

[0581] Step 6:

[0582] The user inputs response data to the suggestions via their device. This response data is recorded as the user's selections and feedback. The input is user feedback, and the output is the response data sent to the server.

[0583] Step 7:

[0584] The server continuously learns the system based on the collected response data. Based on the feedback, it adjusts the algorithm to improve prompt generation and suggestion accuracy for future sessions. The input is the user's response data, and the output is the learned and updated system.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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".

[0602] This invention is a comprehensive lifestyle curation system designed to enrich the entire lives of users. Specifically, it generates and provides personalized suggestions based on activity data collected from each user, tailored to their individual lifestyle.

[0603] First, users use smartphones or wearable devices to record various activity information from their daily lives. This includes recording steps, location information, heart rate data, and meal records. The device sends this information to a cloud server, where it is stored as user activity data.

[0604] The server stores the received data while maintaining its integrity and analyzes it using machine learning algorithms. The server understands the user's activity patterns and health indicators and generates personalized recommendations. These recommendations take into account the user's life stage, cultural background, and ethical values, and are provided in a way that aligns with the user's values.

[0605] For example, if a user is a new employee who is busy with work during the week and lacks exercise, the server can use that activity data to suggest short exercises and healthy meals. These suggestions are sent to the user's smartphone and used as reference information to improve their daily activities.

[0606] Furthermore, users can provide feedback on suggestions via their devices. This feedback is sent to the server and used as training data to improve the accuracy of the suggestions. This feedback allows for suggestions tailored to the user's preferences, ultimately improving their quality of life in the long term.

[0607] In this way, the suggestion system based on user activity data functions as an effective means of improving users' quality of life and realizing a richer lifestyle. The coordination of servers, terminals, and users enables individualized responses tailored to each user's needs.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The device records activity data from the user's daily life. This data includes steps taken, heart rate, location information, and details of meals eaten. This data is sent from the device to a cloud server as it is collected.

[0611] Step 2:

[0612] The server receives activity data sent from the terminal and stores it in the database. It verifies the integrity of the data and preprocesses any incomplete data or outliers.

[0613] Step 3:

[0614] The server analyzes the user's lifestyle based on pre-processed data. Machine learning algorithms are used to evaluate the user's behavioral patterns and health indicators.

[0615] Step 4:

[0616] Based on the analysis results, the server generates lifestyle suggestions that take into account the user's life stage, cultural background, and ethical values. The suggestions cover a wide range of topics, including health management, exercise, nutritional balance, and relaxation methods.

[0617] Step 5:

[0618] The server notifies the user of the generated suggestions on their smartphone or wearable device. The suggestions are presented in a format that is easy for the user to immediately incorporate into their daily life.

[0619] Step 6:

[0620] Users implement the suggested actions and provide feedback on the results and their impressions through their device. This feedback serves as important data for evaluating how well users accepted the suggestions.

[0621] Step 7:

[0622] The server receives user feedback and stores it in a database. This data is used for continuous learning to improve the quality of suggestions. This allows for adjustments to make future suggestions more personalized.

[0623] (Example 1)

[0624] 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".

[0625] In today's world, improving the health and lifestyle quality of busy users is a crucial challenge. However, conventional methods fail to adequately address the individual user's background and preferences, making it difficult to provide effective lifestyle improvement solutions. In particular, the inability to fully utilize individual user activity data hinders the provision of optimal recommendations.

[0626] 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.

[0627] In this invention, the server includes means for acquiring information related to the user's activities, means for analyzing the user's lifestyle based on the information, and means for producing advice tailored to the user's social background and life stage using the generated AI model. This makes it possible to provide optimal suggestions for improving each user's life based on their individual needs.

[0628] "Information related to user activities" refers to data that reflects the user's activities in their daily life, such as location information, physiological state, exercise level, and dietary intake.

[0629] "Means of analyzing lifestyles" refers to a process or technology that uses information obtained from users to understand their unique behavioral patterns and health status, and to conduct analyses that are useful for improving their lifestyles.

[0630] A "generated AI model" is a model constructed using machine learning or artificial intelligence algorithms, which is used to analyze user data and generate individually optimized suggestions.

[0631] "Means of producing advice" refers to the process or technology for creating and providing suggestions to users based on their analysis results, tailored to their social background and life stage.

[0632] "Means of receiving responses and learning to continuously improve" refers to machine learning or data update processes that take user feedback and use it to improve the accuracy and appropriateness of suggestions.

[0633] This invention is a comprehensive lifestyle curation system designed to enrich the user's entire life. The system functions as a three-part system consisting of a server, terminals, and the user.

[0634] First, users use devices such as smartphones or wearable devices to record information related to their daily activities. This information includes steps taken, location data, heart rate, and food and drink intake. The devices send this information to a cloud server, where it is stored as the user's activity data.

[0635] The server analyzes the user's lifestyle based on the received data. Using machine learning frameworks such as Python and TensorFlow, the server leverages the generated AI model to analyze the user's activity patterns. This process makes it possible to produce advice that takes into account the user's social background and life stage.

[0636] As a concrete example, consider a user who has recently started a new job, is busy, and is not getting enough exercise. In this situation, the server would suggest exercise programs that can be completed in a short time, as well as healthy, time-saving meal plans. These suggestions would be notified to the user's device, and the user could receive them and use them in their daily life.

[0637] Furthermore, users can send feedback on suggestions to the server via their device. The server uses this feedback to learn and further improve the accuracy of its suggestions. The goal is to continuously improve the quality of advice in response to user preferences and changes.

[0638] An example of a prompt might be, "Please provide suggestions for improving exercise for a user who is a new member of the workforce." This system inputs such prompts into an AI model and builds a mechanism to generate appropriate suggestions. In this way, a suggestion system based on user activity data functions as an effective means of improving the user's quality of life and realizing a fulfilling lifestyle.

[0639] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0640] Step 1:

[0641] Users record information related to their daily activities using devices such as smartphones and wearable devices. Specific actions include measuring steps, obtaining location information using GPS, monitoring heart rate, and manually entering or photographing meal details. This data is temporarily stored on the device. Input is user activity data, and output is data saved to the device's local storage.

[0642] Step 2:

[0643] The device periodically sends the collected activity data to a cloud server. This operation is performed automatically, for example, every hour, using HTTPS. The input is raw data stored on the device, and the output is user-specific activity data stored on the server.

[0644] Step 3:

[0645] The server checks the integrity of the received activity data and performs data cleansing before saving it to the database. Specifically, it uses a Python script to impute missing values ​​and remove outliers. The input is data sent from the terminal, and the output is data formatted for analysis.

[0646] Step 4:

[0647] The server analyzes the formatted data using a generation AI model. This AI model utilizes TensorFlow and analyzes the user's activity patterns and health status to generate suggestions tailored to the user's life stage and cultural background. The input is cleansed data, and the output is user-optimized suggestions.

[0648] Step 5:

[0649] The server sends the generated suggestions to the user's device. The device notifies the user of the received suggestions in the form of a pop-up notification or similar. The input is the suggestions generated by the AI ​​model, and the output is the information notified to the user.

[0650] Step 6:

[0651] Users send feedback on received suggestions to the server via their device. This feedback includes evaluations based on user preferences and the effectiveness of the suggestions. The input is the user's feedback, and the output is the training data sent to the server. The server uses this data to learn and improve the accuracy of future suggestions.

[0652] (Application Example 1)

[0653] 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".

[0654] Modern consumers have diverse lifestyles and individual needs, but general product and service offerings are not adequately addressing them. Furthermore, there is a lack of systems that effectively utilize individual activity data to suggest the most suitable products and services to users in real time. Therefore, there is a need for specific technologies to further enrich the consumer experience.

[0655] 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.

[0656] In this invention, the server includes means for collecting user behavior data, means for analyzing the user's lifestyle based on the behavior data, means for generating suggestions tailored to the user's life stage and cultural background, and means for suggesting products and services based on the behavior data. This makes it possible to quickly and accurately suggest products and services suitable for each user, thereby improving the consumer experience.

[0657] "Behavioral data" refers to information about a user's activities, including location, physical information, distance traveled, and records of eating and drinking.

[0658] "Lifestyle" refers to a user's daily habits and behavioral patterns, encompassing their entire way of life based on their individual values ​​and cultural background.

[0659] "Life stages" refer to different phases in a user's life, such as being a new employee, having a family, or being retired.

[0660] "Suggestions" refer to specific recommendations and suggestions about products and services that are generated based on users' lifestyle and behavioral data.

[0661] "User device" refers to a terminal operated by a user, and includes smartphones, wearable devices, or other interfaces.

[0662] "Responses" refer to feedback from users regarding their opinions and actions in response to suggestions, and this information is used to improve the system.

[0663] "Methods for proposing products and services" refers to technologies that analyze user behavior data to select the most suitable products and services and propose them appropriately to users.

[0664] This invention provides a system to enrich the user's lifestyle, and its specific embodiments are described below.

[0665] First, users collect daily activity data using smartphones or wearable devices (hereinafter referred to as "user devices"). User devices are equipped with sensors to record location information, distance traveled, body measurements, and records of eating and drinking. This data is transmitted to a cloud server in real time.

[0666] The server uses the received behavioral data to analyze the user's lifestyle using specialized data analysis software (such as machine learning frameworks like TensorFlow or PyTorch). This analysis makes it possible to generate suggestions that take into account the user's life stage and cultural background. For example, it can suggest an appropriate exercise program to a user who is not getting enough exercise, and these suggestions can include information on suitable products and events.

[0667] The user device is equipped with an interface that allows the user to easily provide feedback on suggestions received from the server. This feedback is sent to the server and used as data to improve the accuracy of the suggestions.

[0668] For example, if the system detects through analysis that a user is not getting enough exercise, it will generate a prompt such as, "How about a 15-minute yoga set that fits your busy weekday schedule?" and suggest it to the user. This prompt will be displayed on the user's smart glasses and provided as reference information for daily health management.

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The user device uses built-in sensors to collect behavioral data in real time, including location information, distance traveled, body measurements, and records of eating and drinking. This data is primarily obtained from accelerometers, GPS sensors, and heart rate sensors. The collected data is temporarily stored in the device's memory.

[0672] Step 2:

[0673] The user device transmits the collected behavioral data to the cloud server. To protect user privacy, the data is securely transferred via an encryption protocol. The input is the user's behavioral data, and the output is the raw data uploaded to the server.

[0674] Step 3:

[0675] The server stores the received behavioral data in a database. During this process, data integrity is ensured, and data is managed separately for each user. Next, machine learning frameworks such as TensorFlow and PyTorch are used to analyze the data. The input is raw behavioral data, and the output is the analysis results regarding the user's lifestyle.

[0676] Step 4:

[0677] The server generates product suggestions that take into account the user's lifestyle and cultural background, based on the analysis results. This generation utilizes natural language processing technology and a generative AI model as its platform. In this example, prompts such as "How about a 15-minute yoga set suitable for your busy weekday schedule?" are generated. The input is the analysis results, and the output is the suggested prompt.

[0678] Step 5:

[0679] The user device notifies the user of the prompt message received from the server. In doing so, smart glasses or smartphone notification functions are used to ensure the user receives the suggestions appropriately. The input is the suggestion prompt message, and the output is the notification to the user.

[0680] Step 6:

[0681] Users provide feedback on the received suggestions through their device. This feedback is easily done using the terminal's input interface and sent to the server. The input is the user's feedback, and the output is data that helps improve the suggestions.

[0682] 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.

[0683] This invention is a comprehensive system that analyzes a user's activity data and emotional state to provide suggestions for improving the user's lifestyle. The system collects and analyzes data from the user's daily life using their smartphone or wearable device.

[0684] The device records activity data such as the user's location, heart rate, steps taken, and meal records. Furthermore, by combining it with an emotion engine, it analyzes the user's emotions through voice tone, facial recognition, and biosignals, and sends that data to a cloud server.

[0685] The server stores received activity and emotional data in a database and first performs data preprocessing. Then, it analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. Through this analysis, lifestyle suggestions are generated. These suggestions are customized according to the user's cultural background and life stage, and include content aimed at stress management and mental health improvement based on their emotional state.

[0686] For example, if the server suspects a user is experiencing high stress levels, it will suggest relaxation methods or meditation apps that address that emotional state. Furthermore, suggestions for meals and exercise are adjusted according to the user's emotional state on any given day and presented at the most beneficial time for them.

[0687] The suggestions are notified to the user's smartphone and presented in an actionable format. The user provides feedback, and the server uses this feedback to learn from the system and improve the suggestions. This allows the user to receive more personalized lifestyle advice.

[0688] This system allows for a real-time understanding of users' daily lives and emotional states, enabling appropriate lifestyle improvements. The integration of the server and terminals provides solutions tailored to diverse user needs.

[0689] The following describes the processing flow.

[0690] Step 1:

[0691] The device records the user's daily activity data and emotional data. This includes location information, heart rate, steps, and meal records obtained from the user's smartphone or wearable device, as well as emotional data such as voice tone, facial expressions, and heart rate variability obtained using an emotion engine.

[0692] Step 2:

[0693] The device transmits collected activity and emotion data to a cloud server. Data transmission occurs in real time or at specified intervals, while maintaining data integrity.

[0694] Step 3:

[0695] The server receives data sent from the terminal and stores it in the database. It detects missing or abnormal values ​​in the data and performs preprocessing such as imputation or removal as needed.

[0696] Step 4:

[0697] The server applies machine learning algorithms based on pre-processed data to analyze user behavior and emotions. At this stage, it extracts user lifestyle patterns and emotional trends, and evaluates stress levels, happiness levels, and other factors.

[0698] Step 5:

[0699] Based on the analysis results, the server generates suggestions tailored to the user's current lifestyle and emotional state. These suggestions include health management, exercise, dietary improvements, stress reduction methods, and relaxation and mental health support.

[0700] Step 6:

[0701] The server notifies the user's device of the generated suggestions. The suggestions are provided in an easy-to-follow format and may include, for example, immediately actionable exercise videos, recommended recipes, or the use of meditation apps.

[0702] Step 7:

[0703] Users receive notifications and act accordingly. They can also record feedback on the suggestions and their effects through their device.

[0704] Step 8:

[0705] The server collects feedback from users and stores it in a database. This data is used to update the machine learning model, continuously improving the quality and accuracy of suggestions.

[0706] This flow allows users to improve their quality of life by consistently monitoring and caring for their lifestyle and emotions.

[0707] (Example 2)

[0708] 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".

[0709] In modern society, personalized health management and stress management are in demand. However, existing systems struggle to effectively integrate and analyze activity information and emotional states to propose lifestyles optimized for individuals. Furthermore, there is a lack of adequate methods for using user feedback to improve the accuracy of these suggestions and train the systems accordingly. In this situation, there is a need for a system that can provide detailed support tailored to the diverse needs of users.

[0710] 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.

[0711] In this invention, the server includes means for collecting user activity information, means for analyzing emotional states, and means for creating and notifying personalized suggestions. This enables the integrated analysis of user activity information and emotional states to provide personalized lifestyle suggestions. Furthermore, by incorporating user feedback, the system can continuously learn and provide highly accurate suggestions.

[0712] A "user" is a person who uses the system and receives suggestions based on the analysis of their activity information and emotional state.

[0713] "Activity information" refers to data generated by users in their daily lives, such as location information, pulse rate, distance traveled, and meal history.

[0714] "Emotional state" refers to the user's psychological state as determined through analysis of voice tone and facial expressions.

[0715] "Suggestions" refer to information created based on the user's activity data and emotional state, aimed at improving their lifestyle, such as meal plans, exercise plans, and relaxation methods.

[0716] "Feedback" refers to the evaluations and opinions that users provide to the system regarding the acceptance and implementation of their suggestions.

[0717] "Means of continuous learning and improvement" refers to the process by which the system uses user feedback to update its algorithms and suggestions, enabling it to make more accurate suggestions.

[0718] "User device" refers to a device used by a user to receive suggestions, and includes smartphones and wearable devices.

[0719] "Lifestyle" refers to how a user spends their daily life, according to their cultural environment and stage of life, as well as the habits and behaviors that support it.

[0720] This invention is a system for providing personalized lifestyle improvement suggestions by comprehensively analyzing a user's activity information and emotional state. Specific embodiments are described below.

[0721] First, the device uses hardware such as smartphones and wearable devices to collect activity information in real time, including location data, pulse rate, distance traveled, and meal history, generated during the user's daily life. Regarding emotional states, the device's built-in microphone and camera are used to analyze voice tone and facial expressions. For this purpose, facial recognition software and voice analysis software are employed. All of this data is transmitted to a server in a cloud environment.

[0722] The server stores the received activity and emotion data in a database and then performs preprocessing. This involves noise reduction and correction of anomalous data to create a clean dataset. Next, machine learning algorithms and generative AI models are used to analyze the user's lifestyle and emotional state. Based on the analysis results, lifestyle suggestions tailored to the user are generated. These suggestions are created taking into account the user's cultural environment and life stage, and are then sent back to the device via the cloud.

[0723] For example, if the server detects high stress levels based on the user's emotional state, it will suggest relaxation methods or the use of a meditation app. Such suggestions will be notified to the user's device in the form of, for example, "Please open a meditation app to practice deep breathing." An example of a prompt message might be, "Based on the user's activity information and emotional state, please generate specific suggestions to alleviate stress."

[0724] Users try out actions based on the suggestions they receive and provide feedback to the system. Based on this feedback, the server updates its learning algorithm to further improve the accuracy of its suggestions. Through this cycle, the suggestions provided to users become more personalized and effective over time.

[0725] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0726] Step 1:

[0727] The device collects user activity information. Inputs include location data, pulse rate, distance traveled, and meal history obtained from smartphones and wearable devices. This data is acquired using sensors and applications, and processed in real time. The output is processed into a format that can be sent to a cloud server.

[0728] Step 2:

[0729] The device analyzes the user's emotional state. Inputs include voice tone and facial expression data acquired through the device's built-in microphone and camera. This data is processed by emotion analysis software to understand the user's emotional state. Output is analyzed emotion data, which is also sent to a cloud server.

[0730] Step 3:

[0731] The server receives collected activity and sentiment data. All data from the terminal is received as input and stored in the database. The server preprocesses the received data, removing noise and imputing missing values ​​to generate a clean dataset. The output is processed data suitable for analysis by machine learning algorithms.

[0732] Step 4:

[0733] The server performs lifestyle analysis based on a generative AI model using pre-processed data. The input consists of pre-processed activity information and emotional data. The server analyzes the data using machine learning algorithms to gain insights into the user's lifestyle and emotional state. The output generates specific suggestions that should be provided to the user.

[0734] Step 5:

[0735] The server customizes the generated suggestions according to the user's cultural environment and life stage. The input consists of the generated suggestions and the user's basic profile information. Based on this, it creates personalized lifestyle suggestions. The output is the customized suggestions.

[0736] Step 6:

[0737] The server sends customized suggestions to the device. The input is the customized suggestions, which are then sent to the user's smartphone in an appropriate notification format. The output is the completion of sending the suggestions in a format the user can receive.

[0738] Step 7:

[0739] The user attempts to take specific actions based on the suggestions they receive. The input is a suggestion notified to their smartphone. The user then engages in specific activities, such as "open a meditation app and practice deep breathing for 5 minutes." The output is prepared to return feedback to the system, including the user's own impressions and the effects of these activities.

[0740] Step 8:

[0741] The server receives feedback from users and continuously improves its content. The input is user-provided feedback. The server analyzes this feedback, evaluates the effectiveness of suggestions and user reactions, and uses it as training data. The output is reflected in future suggestions, allowing the system to constantly evolve.

[0742] (Application Example 2)

[0743] 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".

[0744] In modern commercial facilities, there is a demand for personalized products and services that cater to the diverse emotional states and circumstances of customers. However, conventional systems struggle to accurately grasp a customer's emotional state and provide meaningful suggestions instantly. Therefore, to increase customer satisfaction, flexible service provision tailored to individual situations is necessary.

[0745] 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.

[0746] In this invention, the server includes means for acquiring user activity data and emotional information, means for analyzing the user's participation patterns based on the acquired data, and means for dynamically presenting products and services according to the user's situation and emotions. This enables the provision of personalized services to improve the customer experience and increase satisfaction within commercial facilities.

[0747] "User activity data" refers to information that indicates an individual's behavior and lifestyle, and includes geographical and biometric information.

[0748] "Emotional information" refers to data that indicates an individual's current psychological state, and is analyzed through factors such as voice tone and facial expressions.

[0749] A "participation pattern" is a characteristic behavioral model based on the user's behavioral tendencies and emotional changes.

[0750] "Dynamically presenting products and services" refers to a method of instantly displaying optimized product information and service guidance based on real-time data analysis results from users.

[0751] An "information display device" is a device that visually displays information about presented products or services, and includes monitors and displays.

[0752] "Response data" refers to data that records users' actions and opinions in response to suggestions, and can be used to improve future suggestions.

[0753] The system for implementing this invention acquires and analyzes user activity data and emotional information, and dynamically proposes products and services relevant to the user. The system utilizes hardware such as smart devices and robots placed in stores. These devices have the function of acquiring activity data and emotional information from users and transmitting it to a cloud server.

[0754] The server integrates with a data analysis engine to process the received data. AWS Rekognition is used for facial recognition, and Google Cloud Speech-to-Text is used for analyzing audio data. Based on these analysis results, the server understands the user's emotional state and participation patterns.

[0755] The server implements algorithms to accurately present products and services based on the user's real-time situation and emotional information. It uses a generative AI model to construct prompts and determine the information to be presented. Finally, the data is presented to the user through an information display device. It also collects user response data and feeds it back into the system's learning process.

[0756] As a concrete example, when implementing the system in a shopping mall, if it is determined that a customer is experiencing stress, the robot will generate and suggest a prompt such as, "We can show you a relaxing space here. Would you like to use it?" The AI ​​model used for generating prompts would look something like this:

[0757] Example prompt: "Your expression is relaxed and you are smiling. I will guide you to the outdoor equipment section."

[0758] In this way, the system can provide a personalized experience tailored to each customer, thereby improving user satisfaction.

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

[0760] Step 1:

[0761] The device collects user activity data. Using sensors and cameras, it acquires geographical and biometric information and transmits this data to a cloud server. The input is geographical and biometric information acquired from the user, and the output is the raw data transmitted to the cloud server.

[0762] Step 2:

[0763] The server analyzes the received data. It uses AWS Rekognition to analyze facial expression data and Google Cloud Speech-to-Text to convert speech data into text. This process allows the server to understand the user's emotional state. The input is raw data sent from the device, and the output is the analyzed emotional data.

[0764] Step 3:

[0765] The server determines the user's participation pattern based on the analyzed sentiment data. Using machine learning algorithms, it analyzes the user's past behavioral tendencies and emotional changes to generate the current participation pattern. The input is the analyzed sentiment data, and the output is the user's participation pattern.

[0766] Step 4:

[0767] The server generates prompt messages based on the user's situation using a generative AI model. These prompt messages are designed to suggest the most suitable products or services based on the user's current participation patterns and emotional state. The input is the user's participation patterns, and the output is the generated prompt message.

[0768] Step 5:

[0769] The terminal displays prompt messages to the user via an information display device. The suggested information is presented in a way that appeals to the user's sight and hearing. The input is the prompt message sent from the server, and the output is the presented information that the user views.

[0770] Step 6:

[0771] The user inputs response data to the suggestions via their device. This response data is recorded as the user's selections and feedback. The input is user feedback, and the output is the response data sent to the server.

[0772] Step 7:

[0773] The server continuously learns the system based on the collected response data. Based on the feedback, it adjusts the algorithm to improve prompt generation and suggestion accuracy for future sessions. The input is the user's response data, and the output is the learned and updated system.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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."

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] Means of collecting user activity data,

[0798] A means of analyzing the user's lifestyle based on the aforementioned activity data,

[0799] A means of generating suggestions tailored to the user's cultural background and life stage,

[0800] A means for notifying the user terminal of the aforementioned proposal,

[0801] A means of receiving user feedback and continuously learning,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, characterized in that the activity data includes location information, heart rate, steps taken, and a record of meals.

[0805] (Claim 3)

[0806] The system according to claim 1, characterized in that the proposal includes information regarding meal plans, exercise schedules, and event participation.

[0807] "Example 1"

[0808] (Claim 1)

[0809] Means of obtaining information related to user activity,

[0810] A means of analyzing the user's lifestyle based on the aforementioned information,

[0811] A means of producing advice tailored to the user's social background and life stage using the generated AI model,

[0812] Means for notifying the user's device of the aforementioned advice,

[0813] A means of receiving user feedback and learning to continuously improve,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, characterized in that the information related to the activity includes location data, data indicating physiological state, quantity of movement, and records of eating and drinking.

[0817] (Claim 3)

[0818] The system according to claim 1, characterized in that the advice includes information regarding nutritional management plans, exercise plans, and activity participation.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] Means of collecting user behavior data,

[0822] A means for analyzing the user's lifestyle based on the aforementioned behavioral data,

[0823] A means of generating suggestions tailored to the user's life stage and cultural background,

[0824] Means for notifying the user device of the aforementioned proposal,

[0825] A means of receiving user feedback and making improvements,

[0826] A means of proposing products and services based on the aforementioned behavioral data,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, characterized in that the behavioral data includes location information, body measurement information, distance traveled, and records of meals.

[0830] (Claim 3)

[0831] The system according to claim 1, characterized in that the proposal includes information regarding meal plans, exercise schedules, and participation in events.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] Means of collecting user activity information,

[0835] A means for analyzing the user's lifestyle based on the aforementioned activity information,

[0836] A means of creating suggestions according to the user's cultural environment and life stage,

[0837] Means for notifying the user device of the aforementioned proposal,

[0838] A means of analyzing voice tone and facial expressions to determine emotional state,

[0839] A means of proposing relaxation methods based on the user's emotional state,

[0840] A means of obtaining user feedback, continuously learning, and improving,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, characterized in that the activity information includes location information, pulse rate, distance traveled, and meal history.

[0844] (Claim 3)

[0845] The system according to claim 1, characterized in that the proposal includes a meal plan, an exercise plan, and a relaxation method.

[0846] "Application example 2 when combining with an emotional engine"

[0847] (Claim 1)

[0848] Means for obtaining user activity data and sentiment information,

[0849] A means for analyzing user participation patterns based on the acquired data,

[0850] A means of dynamically presenting products and services according to the user's situation and emotions,

[0851] Means for displaying the above proposal on an information display device,

[0852] A means of collecting user response data and continuously learning from it,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, characterized in that the activity data includes geographical information and biometric information.

[0856] (Claim 3)

[0857] The system according to claim 1, characterized in that the proposal includes product recommendations, service usage instructions, and rest area introductions. [Explanation of Symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting user activity data, A means of analyzing the user's lifestyle based on the aforementioned activity data, A means of generating suggestions tailored to the user's cultural background and life stage, A means for notifying the user terminal of the aforementioned proposal, A means of receiving user feedback and continuously learning, A system that includes this.

2. The system according to claim 1, characterized in that the activity data includes location information, heart rate, steps taken, and a record of meals.

3. The system according to claim 1, characterized in that the proposal includes information regarding meal plans, exercise schedules, and event participation.

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