system

Abstract

We provide the system. [Solution] A means for collecting user activity data using sensors and transmitting said activity data to a processing device, A means for accumulating the activity data and calculating a health index based on the activity data, A means for detecting abnormalities by comparing the health index with a reference value and generating improvement suggestions, A means of notifying the user terminal of the improvement suggestion and providing advertising information, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is intended to solve the problem that there is a lack of technology to assist individuals in accurately and continuously grasping their own health conditions and taking appropriate actions for health maintenance and improvement. In particular, in an aging society, the importance of early health management is increasing, and an effective system for individuals to independently utilize health data for self-management is required.

Means for Solving the Problems

[0005] This invention provides a system that continuously monitors a user's health status by collecting user activity data using sensors and transmitting this data to a server. Furthermore, the server calculates a health index based on the activity data and compares it to a baseline value. If an abnormality is detected based on this comparison, it generates personalized improvement suggestions and notifies the user's terminal. In addition, it supports the user's motivation to maintain their health by providing relevant advertising information.

[0006] "Activity data" refers to data that indicates a user's physical activity status, and includes information such as steps taken, location information, and heart rate.

[0007] "Detecting" means finding anomalies or characteristics by comparing collected data with specific conditions or criteria.

[0008] "Improvement suggestions" are instructions and advice generated based on the user's health status and activity data, and include specific action plans to maintain or improve health.

[0009] A "health index" is a numerical indicator that quantifies a user's health status, and is calculated based on user activity data such as BMI and calorie consumption.

[0010] A "sensor" is a device that detects user activity and changes in the environment, and measures things like steps taken, heart rate, and location information.

[0011] A "server" is a computer system that receives, processes, and stores data via a network, and then provides calculation results and notifications.

[0012] A "user terminal" is an information processing device used by a user, and includes devices such as smartphones and tablets. This terminal has the function of receiving and displaying notifications and advertisements from a server. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for supporting user health management, collecting user activity data, evaluating health status, and notifying users of improvement suggestions. This system mainly consists of a server, a user terminal, and a series of sensor devices.

[0035] User terminal operation

[0036] The user device typically functions as a smartphone, constantly receiving data from sensors and sending it to the server. The device then notifies the user of improvement suggestions and advertisements in an appropriate format. For example, the user can see in real time how many steps they take in a day and how their heart rate has changed over time.

[0037] Server operation

[0038] The server receives activity data sent by the user and stores it in a database. The server analyzes the stored data, calculates a health index, and compares it to a baseline value. For example, it calculates the user's BMI and calorie consumption to determine if the user is within a healthy range. If an anomaly is detected, the server uses a generative AI model to construct improvement suggestions based on the user's activity data and health status.

[0039] Specific examples of user usage scenarios

[0040] Users carry their smartphones with them in their daily lives. For example, based on step count and heart rate data collected through daily commutes and exercise, the server generates personalized health reports. Users can track their daily and weekly health trends on their smartphones and adjust their diet and exercise as needed.

[0041] The system notifies users' mobile devices with suggestions for improving their health. For example, it might send a notification such as, "You've reached your step goal for today. We recommend adding stretching next," providing the necessary motivation.

[0042] This system allows users to monitor changes in their lifestyle and proactively work towards maintaining their health.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device collects user activity data from sensors, including steps taken, location information, and heart rate. The acquired data is temporarily stored within the device.

[0046] Step 2:

[0047] The device sends collected activity data to the server at regular intervals. Because the data is sent compactly, transmission is performed with minimal burden.

[0048] Step 3:

[0049] The server stores the received data in the database. New data is added in real time and managed on a per-user basis.

[0050] Step 4:

[0051] The server analyzes activity data stored in the database and calculates a health index. The calculation results are compared to general health standards.

[0052] Step 5:

[0053] If the server detects an anomaly using the comparison results, it will use a generated AI model to create improvement suggestions tailored to the user. These suggestions will include specific actions and points to note.

[0054] Step 6:

[0055] The server sends the generated improvement suggestions and related advertising information to the user's terminal. This transmission is performed using a secure communication protocol.

[0056] Step 7:

[0057] The device notifies the user of improvement suggestions and advertising information received from the server. Notifications are sent via push notifications and in-app messages.

[0058] Step 8:

[0059] Users refer to the improvement suggestions they receive and take action. They can check their progress and plan actions based on the suggestions through the app.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In modern times, there is a need to efficiently collect and analyze individual activity information for health management and generate appropriate health improvement suggestions. However, conventional technologies have difficulty fully utilizing user activity information to provide effective health management, and generating personalized improvement suggestions is a particular challenge.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for collecting user activity information using a measuring device, means for transmitting said activity information to a computing device, and means for storing said activity information in a storage device and calculating health indicators based on said activity information. This makes it possible to provide users with personalized health improvement suggestions and support their health management.

[0065] "User activity information" refers to information about an individual's actions and biological state in their daily life, including step count, location data, and biometric measurements.

[0066] A "measuring device" is a device used to collect user activity information and has the function of measuring activity status using sensors.

[0067] A "computational device" is a device that receives collected user activity information and performs necessary calculations, and functions as a server.

[0068] A "memory device" is a device used by a computing device to store user activity information received from the device, making the data available for subsequent processing.

[0069] A "health indicator" is a numerical value calculated based on a user's activity information, serving as a standard for evaluating their physical condition and health status.

[0070] A "reference value" is a value that indicates a target or acceptable range when evaluating health indicators, and is used as a standard for determining a normal state of health.

[0071] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and generate output such as improvement suggestions.

[0072] "Public information" refers to general information and notices provided to users, including health advice and product information.

[0073] This invention provides a system for efficiently supporting users' health management. This system mainly consists of a server, a user terminal, and a measuring device.

[0074] User terminal functions

[0075] The user terminal typically functions as a smartphone and is carried daily. This terminal has the function of receiving data from the measuring device and transmitting it to the server in real time. Specifically, it receives activity information such as steps taken and heart rate collected by the measuring device when the user is going about their daily life or exercising. Based on this information, the user terminal provides health-related notifications. For example, the user may receive a notification in real time saying, "Your heart rate is stable today."

[0076] Server Functions

[0077] The server receives activity information transmitted from the user's terminal and stores the data in its internal storage. Furthermore, it analyzes this data using a generative AI model and calculates health indicators. These health indicators are compared to baseline values, and if an abnormality is detected, the generative AI technology is used to generate specific improvement suggestions tailored to the user's health condition. These suggestions provide the user with specific, goal-oriented motivation, such as, "You achieved 7,000 steps today. Next, try an hour of yoga on the weekend."

[0078] Examples of specific cases and prompt statements

[0079] For example, if a user runs five days a week, their activity data is recorded by a measuring device and analyzed on a server. As a result, the generating AI model can compare this data with past data to create suggestions for a new running schedule. An example of a prompt in this case would be the text, "Analyze the user's running data for this week and generate exercise suggestions for next week."

[0080] This system allows users to easily understand their health status and work towards maintaining or improving their health based on personalized improvement suggestions.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user terminal collects activity information from the measuring device. This information includes steps taken, heart rate, and location data. The input is the user's real-time activity, and this data is taken into the terminal as output. Specifically, when the user is walking or exercising, the terminal connects to the measuring device via Bluetooth or Wi-Fi and acquires data.

[0084] Step 2:

[0085] The user terminal transmits collected activity information to the server. The input is the acquired user activity data, which is then transmitted to the server as output. The terminal ensures the security of the data being transmitted by using a secure communication protocol. Specifically, data transfer is performed automatically at regular intervals.

[0086] Step 3:

[0087] The server stores activity information sent from user terminals in its storage device. The input is activity data sent from the terminal, and the output is storage in a database in an organized format. Specifically, the server classifies the data by date and time and activity type, and stores it in a way that allows for quick access.

[0088] Step 4:

[0089] The server analyzes data stored in its storage device. The input is saved user activity information, and the output is health indicators and anomaly detection results. Using a generative AI model, it converts each data point into health indicators and compares them to baseline values ​​to check for abnormalities. Specifically, it sends prompt messages to the generative AI model to flexibly perform analysis based on the data.

[0090] Step 5:

[0091] The server uses AI-generated technology based on health indicators to create improvement suggestions. The input is the health indicators and abnormality detection results obtained from the analysis, and the output is specific suggestions for improving the user's health. For example, based on the user's recent activity level, it may generate suggestions such as, "We recommend you walk more."

[0092] Step 6:

[0093] The server notifies the user terminal of the generated improvement suggestions. The input is the generated health improvement suggestions, and the output is the notification to the user. The user terminal receives the suggestions and displays them as push notifications, making it easy for the user to check the notification content. Specifically, the timing of sending notifications is optimized to match the user's daily routine.

[0094] (Application Example 1)

[0095] 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."

[0096] Maintaining people's health is a crucial issue in modern society. However, many people are unable to adequately understand their own health status due to their busy daily lives, and therefore do not receive proper health management. To address this issue, there is a need for a system that provides personalized health support based on the user's living environment and behavior.

[0097] 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.

[0098] In this invention, the server includes means for collecting user behavior data using measuring means and transmitting said behavior data to a computing device, means for accumulating said behavior data and calculating health indicators based on said behavior data, and means for detecting abnormalities by comparing said health indicators with reference values ​​and generating improvement suggestions. As a result, users can understand their own health status in real time and use healthy products and services that correspond to their movements in physical stores.

[0099] A "user" is an individual who uses the system to collect activity data and receive health management support.

[0100] "Behavioral data" refers to information about a user's activities, such as distance traveled, location information, and heart rate.

[0101] "Measurement means" refers to hardware including sensor devices for acquiring user behavior data.

[0102] A "computational processing unit" refers to a computer system used to process and analyze collected behavioral data.

[0103] "Health indicators" are numerical values ​​or information that indicate a user's health status, calculated based on behavioral data.

[0104] A "reference value" refers to a numerical value or range that serves as a standard when evaluating health indicators.

[0105] "Abnormal" refers to a state where health indicators deviate from the reference value, indicating a health problem or a condition requiring improvement.

[0106] "Improvement suggestions" are suggestions and advice provided to users based on health indicators, aimed at improving their health status.

[0107] "User terminal" refers to portable information devices such as smartphones and tablets that users carry with them.

[0108] "Information" includes data on improvement suggestions and recommended products within stores.

[0109] "Inside the store" refers to the physical retail location or facility that a user visits.

[0110] "Products" refer to the products and services offered within a store.

[0111] "Events" refer to events and special programs held within the store.

[0112] The programs necessary to implement this system are installed on user devices such as smartphones and tablets, as well as on servers running in the cloud.

[0113] The server receives behavioral data sent by users and stores it in a cloud-based database. This process utilizes database services such as Google Cloud BigQuery and Amazon Relational Database Service (RDS).

[0114] The accumulated data is analyzed using generative AI models such as "TENSORFLOW®" and "PyTorch." This analysis calculates user health indicators and compares them to reference values. If an anomaly is detected, improvement suggestions are generated based on the generative AI model. These improvement suggestions include behavioral guidelines within physical stores or recommendations for healthy products and services.

[0115] The user's device acquires behavioral data in real time using the smartphone's built-in sensors (accelerometer and GPS) and sends it to the server. It also notifies the user of generated improvement suggestions and product recommendations. For this purpose, push notification systems on iOS and Android® devices are used.

[0116] For example, if a user moves around the store for more than 20 minutes and the system determines that they haven't gotten enough exercise after lunch, it will send a notification such as, "We recommend using the cardio machines located in specific areas. Please also try our low-calorie menu on your next visit."

[0117] An example of a prompt message might be: "The user's location information and heart rate were found to be significantly below normal levels. Please generate suggestions to encourage the use of cardio machines in the store."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user collects behavioral data (distance traveled, location information, heart rate) in real time using sensors in their smartphone. The input is the smartphone's accelerometer and GPS data, and the output is a behavioral data stream combining this data.

[0121] Step 2:

[0122] The user terminal sends the collected behavioral data to the server. The input is the behavioral data obtained in step 1, and the output is the latest behavioral information obtained through data transfer to the server.

[0123] Step 3:

[0124] The server stores the received behavioral data in a cloud database. The input is the behavioral data sent from the terminal, and the output is the updated behavioral data record in the database.

[0125] Step 4:

[0126] The server analyzes the accumulated behavioral data using generative AI models such as TensorFlow. At this stage, data processing is performed for calculating health indicators; the input is behavioral data from the database, and the output is the calculated health indicators.

[0127] Step 5:

[0128] The server compares the calculated health indicators to reference values ​​and uses a generative AI model to generate improvement suggestions. The input to this process is the health indicators obtained in step 4, and the output is appropriate improvement suggestions for the user.

[0129] Step 6:

[0130] The server notifies the user terminal of the improvement suggestions. The input is the improvement suggestions generated in step 5, and the output is the information displayed as a push notification on the user terminal.

[0131] Step 7:

[0132] Users check notifications displayed on their devices and adjust their in-store behavior accordingly. The input is the notification information pushed to the device, and the output is changes in user behavior or promotion of the target product.

[0133] 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.

[0134] This invention is a system equipped with emotion recognition capabilities to further enhance user health management. In addition to collecting user activity data, it can comprehensively manage the user's mental health state by utilizing an emotion engine.

[0135] User terminal operation

[0136] The user terminal is equipped with sensors that collect activity data, as well as sensors that can detect voice input and changes in heart rate. This allows the system to analyze the user's voice tone and heart rate patterns to infer their emotions in real time. It also includes a mechanism to capture heart rate trends when the user is exercising or relaxing and transmit this information to the emotion engine.

[0137] Server operation

[0138] The server integrates activity and emotional data transmitted from user terminals, stores it in a database, and analyzes it from multiple perspectives. Specifically, it uses an emotion engine to analyze patterns in the data and incorporates emotional balance as additional information to the user's health index. For example, it estimates the user's stress level and, if necessary, suggests relaxing sports or mental health care.

[0139] Specific examples of user usage scenarios

[0140] Based on data collected through the user's daily activities (commuting, exercise, work, etc.), the server generates a daily health and emotional state report. This report can also indicate periods of high stress, providing valuable information for the user to take appropriate action.

[0141] For example, if a user exhibits a high stress level during work, the device will send a notification recommending relaxation activities or short breaks. Similarly, if a positive emotional state is detected after exercise, this can be used to inform future exercise plans.

[0142] The introduction of this system will enable users to achieve a balance between their physical and mental health in their daily lives.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The device uses sensors to collect user activity and voice data. This includes steps taken, location information, heart rate, and voice tone. This data is temporarily stored on the device.

[0146] Step 2:

[0147] The device infers the user's emotions from collected activity and voice data and processes this as emotion data. The emotion engine handles this process and analyzes the data.

[0148] Step 3:

[0149] The device sends collected activity and emotion data to the server. This transmission is conducted via a secure protocol, protecting user privacy.

[0150] Step 4:

[0151] The server integrates the received activity data and emotion data and stores it in a database. The stored data is managed on a per-user basis.

[0152] Step 5:

[0153] The server analyzes the integrated data and calculates health and emotional indices. The calculated indices are then compared to baseline values.

[0154] Step 6:

[0155] The server generates personalized improvement suggestions based on health and emotional indices. These suggestions include recommendations for stress-reducing activities and relaxation techniques.

[0156] Step 7:

[0157] The server sends the generated improvement suggestions to the user's terminal. The notification also includes personalized advice tailored to the user's emotional state.

[0158] Step 8:

[0159] The device notifies the user of improvement suggestions received from the server. The notification is displayed as a push notification, and the user can tap it to view details.

[0160] Step 9:

[0161] Based on the information they receive, users can check their health and emotional state and adjust their behavior as needed. They can also use the app to refer to past data and understand trends.

[0162] (Example 2)

[0163] 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".

[0164] Traditional health management systems primarily calculate physical health indicators based on user activity data, often neglecting to consider mental health information such as emotional state and stress levels. As a result, users often struggle to grasp their overall health status.

[0165] 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.

[0166] In this invention, the server includes means for preprocessing user activity information and biometric data, performing noise reduction and outlier filtering; means for generating analysis results that incorporate emotional balance into a health index using an emotion engine; and means for generating improvement suggestions in accordance with emotional changes and stress levels, and notifying the user terminal. This enables the user to achieve harmony between physical and mental health.

[0167] A "sensor" is a device used to collect user activity information and biometric data, and has the function of measuring voice tone, heart rate, exercise patterns, etc.

[0168] A "processing device" is a machine or software that receives data collected from sensors, performs preprocessing, noise reduction, and filtering, and prepares the data for analysis.

[0169] An "emotion engine" is software or an algorithm that performs analysis based on a user's activity data and biometric data to incorporate emotional balance into health indices.

[0170] A "health index" is a numerical indicator that quantifies the overall state of physical and mental health, and is calculated based on activity data and emotional data.

[0171] "Improvement suggestions" are specific suggestions for improving behavior, style, or environment, generated based on the user's health index and emotional data, and are notified to the user's device.

[0172] This invention is a technology for comprehensively managing the physical and mental health of users. The system is primarily implemented using user terminals and servers.

[0173] The user terminal is equipped with sensors to collect activity information and biometric data. Specifically, it can measure voice tone, heart rate, and exercise patterns. For example, when a user is running, it can detect heart rate, exercise rhythm, and speech tone in real time. The collected data is preprocessed within the terminal, including noise reduction and outlier filtering.

[0174] The server receives data sent from the user's terminal. The received data is stored in a database and analyzed using an emotion engine. The emotion engine implements a generative AI model and performs advanced analysis to incorporate emotional balance into a health index. This analysis makes it possible to identify the user's stress level and emotional changes.

[0175] The analysis results are generated as improvement suggestions and notified to the user's device. For example, if a user shows a high stress level after a meeting, a notification is sent recommending taking a short break or engaging in relaxing activities. This allows users to live healthier lives based on actionable improvement measures.

[0176] For example, if a user exercises regularly and then positive emotions are detected, the system can provide feedback indicating that the exercise was effective, which can then be used to inform future exercise plans.

[0177] Examples of prompt messages include the following:

[0178] "Please explain how to analyze individual emotional states in real time using user activity data and heart rate. Show how this data can be used to provide users with relaxation recommendations."

[0179] "I would like more detailed information about the process of generating a daily emotional balance report using data obtained through users' daily activities. In particular, please explain how to identify times of day when stress levels are high and suggest countermeasures."

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user terminal uses sensors to collect user activity information and biometric data. Inputs include the user's voice tone, heart rate, and exercise patterns. The terminal acquires this raw data in real time and uses it as input data for subsequent processing. Specifically, it records heart rate during running and voice tone during everyday conversation.

[0183] Step 2:

[0184] The device performs noise reduction and outlier filtering on the collected data. The input is raw sensor data, and by performing data cleaning based on this, it outputs data with improved accuracy. For example, it removes background noise from loud voices in audio data, and corrects sudden fluctuations in heart rate detected as noise during exercise.

[0185] Step 3:

[0186] The terminal periodically sends pre-processed data to the server. The input is pre-processed data, which is then communicated as output to the server. If a significant change or anomaly is detected, the data is immediately reported to the server. This enables real-time feedback.

[0187] Step 4:

[0188] The server stores the received data in a database and prepares it for analysis. The input consists of various data sent from terminals, which are accumulated through registration in the database. This enables long-term trend analysis and management of the data.

[0189] Step 5:

[0190] The server uses an emotion engine to analyze data and calculate health indices and emotional balance. The input is accumulated data, and a generative AI model is used for pattern recognition and emotional state estimation. This generates the user's current stress level and overall emotional balance as output.

[0191] Step 6:

[0192] The server generates specific improvement suggestions based on the analysis results and notifies the user's terminal. The input is the results of the health index and emotional balance, and it outputs helpful feedback and suggestions for action for the user. For example, it might send a notification recommending relaxation techniques to reduce stress.

[0193] Step 7:

[0194] Users utilize suggestions received from their devices to manage their own health. Input consists of notifications from the device, which they can use to adjust their daily actions and lifestyle habits. Specifically, they practice relaxation techniques according to the suggestions they receive.

[0195] (Application Example 2)

[0196] 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".

[0197] Many brick-and-mortar stores struggle to understand customers' mental health and emotions and provide customized services based on that understanding. Therefore, there is a need for means to improve the customer experience and increase satisfaction. In particular, there is a lack of real-time systems to appropriately respond to customers experiencing stress.

[0198] 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.

[0199] In this invention, the server includes means for analyzing biometric data and voice data, means for determining the mental health state using an emotion recognition function, and means for generating improvement suggestions and notifying the user terminal. This makes it possible to grasp the emotions of customers in real time in physical stores and provide individualized responses and services based on that.

[0200] "Biometric data" refers to information that quantifies a user's physical condition, such as heart rate and heart rate variability.

[0201] "Voice data" refers to digital information that records a user's speech and tone of voice, and is used to analyze their emotional state.

[0202] An "analysis device" is a computer system that processes collected data and evaluates mental and physical health.

[0203] "Emotion recognition functionality" is a technology that analyzes a user's biometric data and voice data to infer the user's emotional state.

[0204] "Mental health status" refers to a psychological state that comprehensively evaluates the user's emotions and stress levels.

[0205] "Improvement suggestions" are recommendations aimed at improving the user's health by suggesting specific actions or activities based on collected and analyzed data.

[0206] A "user terminal" refers to a mobile device or wearable device used to receive information and notify the user.

[0207] A "physical store" refers to a retail business that conducts sales activities or provides services in a physical location.

[0208] The system that realizes this application example is a technology that grasps the emotional state of customers in real time and provides individualized responses based on that. Here, it is implemented using smart devices and a server system.

[0209] The device takes the form of smart glasses and is equipped with sensors for collecting biometric data and a microphone for analyzing voice. The smart glasses use a generative AI model to analyze changes in voice tone and heart rate to infer the customer's emotions. After the customer's emotional state is determined, the data is transmitted to a server using a wireless communication module.

[0210] The server performs complex data processing based on the received data. This uses an integrated data analysis system (for example, leveraging Python's NumPy and Pandas libraries). Emotion recognition capabilities evaluate the customer's stress level and mental health status from the analyzed data and generate improvement suggestions as needed. These suggestions are sent to terminals in real time and notified to store staff. This allows for the suggestion of relaxation activities and services tailored to each customer.

[0211] For example, when a customer entering a store is deemed to need relaxation, an alert is sent via smart glasses to store staff recommending a hot drink. In this way, retail services can be customized to meet customer needs.

[0212] Examples of prompts include, "Please suggest how to provide service in situations where a customer has shown positive emotions," and "Please advise on how to handle situations where a customer is experiencing high stress levels." By using these prompts, the generative AI model can derive appropriate responses based on the situation.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The device collects the customer's biometric and voice data through smart glasses. Sensors are used to acquire information such as heart rate and voice tone, which is then recorded as digital data.

[0216] Step 2:

[0217] The device uses a generated AI model to analyze the customer's emotional state from digital data. This analysis uses acquired heart rate and voice tone as input data and applies an emotion recognition algorithm to infer the customer's emotional state. The output is emotional information such as the customer's stress level and relaxation state.

[0218] Step 3:

[0219] The terminal sends emotional information to the server. Using a wireless communication module, it sends inferred emotional data to the server. This transmitted data includes emotional information necessary for subsequent processing.

[0220] Step 4:

[0221] Based on the emotional information received by the server, data analysis is performed. Here, Python libraries (such as NumPy and Pandas) are used to collect and integrate data, making it possible to evaluate the overall mental health of customers.

[0222] Step 5:

[0223] The server generates improvement suggestions. Based on the analysis results, it creates recommendations for what kind of relaxation activities and services should be provided, especially for customers with high stress levels, and uses a generative AI model to derive specific suggestions.

[0224] Step 6:

[0225] The server sends the generated improvement suggestions to the terminal. Using the suggested information, the terminal sends notifications to store staff in real time. This allows staff to quickly provide appropriate service to customers.

[0226] Step 7:

[0227] Users receive the services provided. The in-store customer experience is improved, and individually customized services are delivered based on the customer's emotional and mental well-being.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention is a system for supporting user health management, collecting user activity data, evaluating health status, and notifying users of improvement suggestions. This system mainly consists of a server, a user terminal, and a series of sensor devices.

[0245] User terminal operation

[0246] The user device typically functions as a smartphone, constantly receiving data from sensors and sending it to the server. The device then notifies the user of improvement suggestions and advertisements in an appropriate format. For example, the user can see in real time how many steps they take in a day and how their heart rate has changed over time.

[0247] Server operation

[0248] The server receives activity data sent by the user and stores it in a database. The server analyzes the stored data, calculates a health index, and compares it to a baseline value. For example, it calculates the user's BMI and calorie consumption to determine if the user is within a healthy range. If an anomaly is detected, the server uses a generative AI model to construct improvement suggestions based on the user's activity data and health status.

[0249] Specific examples of user usage scenarios

[0250] Users carry their smartphones with them in their daily lives. For example, based on step count and heart rate data collected through daily commutes and exercise, the server generates personalized health reports. Users can track their daily and weekly health trends on their smartphones and adjust their diet and exercise as needed.

[0251] The system notifies users' mobile devices with suggestions for improving their health. For example, it might send a notification such as, "You've reached your step goal for today. We recommend adding stretching next," providing the necessary motivation.

[0252] This system allows users to monitor changes in their lifestyle and proactively work towards maintaining their health.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The device collects user activity data from sensors, including steps taken, location information, and heart rate. The acquired data is temporarily stored within the device.

[0256] Step 2:

[0257] The device sends collected activity data to the server at regular intervals. Because the data is sent compactly, transmission is performed with minimal burden.

[0258] Step 3:

[0259] The server stores the received data in the database. New data is added in real time and managed on a per-user basis.

[0260] Step 4:

[0261] The server analyzes activity data stored in the database and calculates a health index. The calculation results are compared to general health standards.

[0262] Step 5:

[0263] If the server detects an anomaly using the comparison results, it will use a generated AI model to create improvement suggestions tailored to the user. These suggestions will include specific actions and points to note.

[0264] Step 6:

[0265] The server sends the generated improvement suggestions and related advertising information to the user's terminal. This transmission is performed using a secure communication protocol.

[0266] Step 7:

[0267] The device notifies the user of improvement suggestions and advertising information received from the server. Notifications are sent via push notifications and in-app messages.

[0268] Step 8:

[0269] Users refer to the improvement suggestions they receive and take action. They can check their progress and plan actions based on the suggestions through the app.

[0270] (Example 1)

[0271] 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."

[0272] In modern times, there is a need to efficiently collect and analyze individual activity information for health management and generate appropriate health improvement suggestions. However, conventional technologies have difficulty fully utilizing user activity information to provide effective health management, and generating personalized improvement suggestions is a particular challenge.

[0273] 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.

[0274] In this invention, the server includes means for collecting user activity information using a measuring device, means for transmitting said activity information to a computing device, and means for storing said activity information in a storage device and calculating health indicators based on said activity information. This makes it possible to provide users with personalized health improvement suggestions and support their health management.

[0275] "User activity information" refers to information about an individual's actions and biological state in their daily life, including step count, location data, and biometric measurements.

[0276] A "measuring device" is a device used to collect user activity information and has the function of measuring activity status using sensors.

[0277] A "computational device" is a device that receives collected user activity information and performs necessary calculations, and functions as a server.

[0278] A "memory device" is a device used by a computing device to store user activity information received from the device, making the data available for subsequent processing.

[0279] A "health indicator" is an indicator calculated based on a user's activity information and is a numerical value serving as a criterion for evaluating physical condition and health status.

[0280] A "reference value" is a value indicating a goal or tolerance range when evaluating a health indicator and is used as a criterion for judging a normal health state.

[0281] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and generate outputs such as improvement suggestions.

[0282] "Well-known information" is general information or notifications provided to users and includes health advice, product information, etc.

[0283] The present invention provides a system for efficiently supporting a user's health management. This system mainly consists of a server, a user terminal, and a measuring device.

[0284] Functions of the User Terminal

[0285] The user terminal usually operates as a smartphone and is carried daily. This terminal has the function of receiving data from the measuring device and transmitting it to the server in real time. Specifically, when the user conducts daily life or exercise, it receives activity information such as the number of steps and heart rate collected by the measuring device. The user terminal provides health-related notifications based on this information. For example, it can receive in real time a notification such as "Today's heart rate is stable."

[0286] Functions of the Server

[0287] The server receives activity information transmitted from the user's terminal and stores the data in its internal storage. Furthermore, it analyzes this data using a generative AI model and calculates health indicators. These health indicators are compared to baseline values, and if an abnormality is detected, the generative AI technology is used to generate specific improvement suggestions tailored to the user's health condition. These suggestions provide the user with specific, goal-oriented motivation, such as, "You achieved 7,000 steps today. Next, try an hour of yoga on the weekend."

[0288] Examples of specific cases and prompt statements

[0289] For example, if a user runs five days a week, their activity data is recorded by a measuring device and analyzed on a server. As a result, the generating AI model can compare this data with past data to create suggestions for a new running schedule. An example of a prompt in this case would be the text, "Analyze the user's running data for this week and generate exercise suggestions for next week."

[0290] This system allows users to easily understand their health status and work towards maintaining or improving their health based on personalized improvement suggestions.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] The user terminal collects activity information from the measuring device. This information includes steps taken, heart rate, and location data. The input is the user's real-time activity, and this data is taken into the terminal as output. Specifically, when the user is walking or exercising, the terminal connects to the measuring device via Bluetooth or Wi-Fi and acquires data.

[0294] Step 2:

[0295] The user terminal transmits collected activity information to the server. The input is the acquired user activity data, which is then transmitted to the server as output. The terminal ensures the security of the data being transmitted by using a secure communication protocol. Specifically, data transfer is performed automatically at regular intervals.

[0296] Step 3:

[0297] The server stores activity information sent from user terminals in its storage device. The input is activity data sent from the terminal, and the output is storage in a database in an organized format. Specifically, the server classifies the data by date and time and activity type, and stores it in a way that allows for quick access.

[0298] Step 4:

[0299] The server analyzes data stored in its storage device. The input is saved user activity information, and the output is health indicators and anomaly detection results. Using a generative AI model, it converts each data point into health indicators and compares them to baseline values ​​to check for abnormalities. Specifically, it sends prompt messages to the generative AI model to flexibly perform analysis based on the data.

[0300] Step 5:

[0301] The server uses AI-generated technology based on health indicators to create improvement suggestions. The input is the health indicators and abnormality detection results obtained from the analysis, and the output is specific suggestions for improving the user's health. For example, based on the user's recent activity level, it may generate suggestions such as, "We recommend you walk more."

[0302] Step 6:

[0303] The server notifies the user terminal of the generated improvement suggestions. The input is the generated health improvement suggestions, and the output is the notification to the user. The user terminal enables the user to easily check the notification content by receiving the suggestions and displaying them as push notifications. As a specific operation, the notification transmission timing is optimized according to the user's life rhythm.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] In modern society, maintaining people's health is an important issue. However, many people cannot fully grasp their own health status in their busy daily lives, and appropriate health management is not carried out. To address this issue, a system that provides individual health support based on the user's living environment and behavior is required.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] In this invention, the server includes means for collecting the user's behavior data by measuring means and transmitting the behavior data to the arithmetic device, means for accumulating the behavior data and calculating health indicators based on the behavior data, and means for detecting abnormalities by comparing the health indicators with reference values and generating improvement suggestions. As a result, the user can grasp their own health status in real time and can use healthy products and services according to their movements in the physical store.

[0309] A "user" is an individual who collects activity data using the system and receives health management support.

[0310] "Behavior data" refers to information related to activities such as the user's moving distance, location information, and heart rate.

[0311] "Measurement means" refers to hardware including sensor devices for acquiring user behavior data.

[0312] A "computational processing unit" refers to a computer system used to process and analyze collected behavioral data.

[0313] "Health indicators" are numerical values ​​or information that indicate a user's health status, calculated based on behavioral data.

[0314] A "reference value" refers to a numerical value or range that serves as a standard when evaluating health indicators.

[0315] "Abnormal" refers to a state where health indicators deviate from the reference value, indicating a health problem or a condition requiring improvement.

[0316] "Improvement suggestions" are suggestions and advice provided to users based on health indicators, aimed at improving their health status.

[0317] "User terminal" refers to portable information devices such as smartphones and tablets that users carry with them.

[0318] "Information" includes data on improvement suggestions and recommended products within stores.

[0319] "Inside the store" refers to the physical retail location or facility that a user visits.

[0320] "Products" refer to the products and services offered within a store.

[0321] "Events" refer to events and special programs held within the store.

[0322] The programs necessary to implement this system are installed on user devices such as smartphones and tablets, as well as on servers running in the cloud.

[0323] The server receives behavioral data sent by users and stores it in a cloud-based database. This process utilizes database services such as Google Cloud BigQuery and Amazon Relational Database Service (RDS).

[0324] The accumulated data is analyzed using generative AI models such as "TensorFlow" and "PyTorch." This analysis calculates user health indicators and compares them to reference values. If an anomaly is detected, improvement suggestions are generated based on the generative AI model. These improvement suggestions include behavioral guidelines within physical stores or recommendations for healthy products and services.

[0325] The user's device uses the smartphone's built-in sensors (accelerometer and GPS) to acquire behavioral data in real time and transmit it to the server. The system also notifies the user of generated improvement suggestions and product recommendations. Push notification systems on iOS and Android devices are used as the appropriate technology for this purpose.

[0326] For example, if a user moves around the store for more than 20 minutes and the system determines that they haven't gotten enough exercise after lunch, it will send a notification such as, "We recommend using the cardio machines located in specific areas. Please also try our low-calorie menu on your next visit."

[0327] An example of a prompt message might be: "The user's location information and heart rate were found to be significantly below normal levels. Please generate suggestions to encourage the use of cardio machines in the store."

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

[0329] Step 1:

[0330] The user collects behavioral data (distance traveled, location information, heart rate) in real time using sensors in their smartphone. The input is the smartphone's accelerometer and GPS data, and the output is a behavioral data stream combining this data.

[0331] Step 2:

[0332] The user terminal sends the collected behavioral data to the server. The input is the behavioral data obtained in step 1, and the output is the latest behavioral information obtained through data transfer to the server.

[0333] Step 3:

[0334] The server stores the received behavioral data in a cloud database. The input is the behavioral data sent from the terminal, and the output is the updated behavioral data record in the database.

[0335] Step 4:

[0336] The server analyzes the accumulated behavioral data using generative AI models such as TensorFlow. At this stage, data processing is performed for calculating health indicators; the input is behavioral data from the database, and the output is the calculated health indicators.

[0337] Step 5:

[0338] The server compares the calculated health indicators to reference values ​​and uses a generative AI model to generate improvement suggestions. The input to this process is the health indicators obtained in step 4, and the output is appropriate improvement suggestions for the user.

[0339] Step 6:

[0340] The server notifies the user terminal of the improvement suggestions. The input is the improvement suggestions generated in step 5, and the output is the information displayed as a push notification on the user terminal.

[0341] Step 7:

[0342] Users check notifications displayed on their devices and adjust their in-store behavior accordingly. The input is the notification information pushed to the device, and the output is changes in user behavior or promotion of the target product.

[0343] 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.

[0344] This invention is a system equipped with emotion recognition capabilities to further enhance user health management. In addition to collecting user activity data, it can comprehensively manage the user's mental health state by utilizing an emotion engine.

[0345] User terminal operation

[0346] The user terminal is equipped with sensors that collect activity data, as well as sensors that can detect voice input and changes in heart rate. This allows the system to analyze the user's voice tone and heart rate patterns to infer their emotions in real time. It also includes a mechanism to capture heart rate trends when the user is exercising or relaxing and transmit this information to the emotion engine.

[0347] Server operation

[0348] The server integrates activity and emotional data transmitted from user terminals, stores it in a database, and analyzes it from multiple perspectives. Specifically, it uses an emotion engine to analyze patterns in the data and incorporates emotional balance as additional information to the user's health index. For example, it estimates the user's stress level and, if necessary, suggests relaxing sports or mental health care.

[0349] Specific examples of user usage scenarios

[0350] Based on data collected through the user's daily activities (commuting, exercise, work, etc.), the server generates a daily health and emotional state report. This report can also indicate periods of high stress, providing valuable information for the user to take appropriate action.

[0351] For example, if a user exhibits a high stress level during work, the device will send a notification recommending relaxation activities or short breaks. Similarly, if a positive emotional state is detected after exercise, this can be used to inform future exercise plans.

[0352] The introduction of this system will enable users to achieve a balance between their physical and mental health in their daily lives.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The device uses sensors to collect user activity and voice data. This includes steps taken, location information, heart rate, and voice tone. This data is temporarily stored on the device.

[0356] Step 2:

[0357] The device infers the user's emotions from collected activity and voice data and processes this as emotion data. The emotion engine handles this process and analyzes the data.

[0358] Step 3:

[0359] The device sends collected activity and emotion data to the server. This transmission is conducted via a secure protocol, protecting user privacy.

[0360] Step 4:

[0361] The server integrates the received activity data and emotion data and stores it in a database. The stored data is managed on a per-user basis.

[0362] Step 5:

[0363] The server analyzes the integrated data and calculates health and emotional indices. The calculated indices are then compared to baseline values.

[0364] Step 6:

[0365] The server generates personalized improvement suggestions based on health and emotional indices. These suggestions include recommendations for stress-reducing activities and relaxation techniques.

[0366] Step 7:

[0367] The server sends the generated improvement suggestions to the user's terminal. The notification also includes personalized advice tailored to the user's emotional state.

[0368] Step 8:

[0369] The device notifies the user of improvement suggestions received from the server. The notification is displayed as a push notification, and the user can tap it to view details.

[0370] Step 9:

[0371] Based on the information they receive, users can check their health and emotional state and adjust their behavior as needed. They can also use the app to refer to past data and understand trends.

[0372] (Example 2)

[0373] 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".

[0374] Traditional health management systems primarily calculate physical health indicators based on user activity data, often neglecting to consider mental health information such as emotional state and stress levels. As a result, users often struggle to grasp their overall health status.

[0375] 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.

[0376] In this invention, the server includes means for preprocessing user activity information and biometric data, performing noise reduction and outlier filtering; means for generating analysis results that incorporate emotional balance into a health index using an emotion engine; and means for generating improvement suggestions in accordance with emotional changes and stress levels, and notifying the user terminal. This enables the user to achieve harmony between physical and mental health.

[0377] A "sensor" is a device used to collect user activity information and biometric data, and has the function of measuring voice tone, heart rate, exercise patterns, etc.

[0378] A "processing device" is a machine or software that receives data collected from sensors, performs preprocessing, noise reduction, and filtering, and prepares the data for analysis.

[0379] An "emotion engine" is software or an algorithm that performs analysis based on a user's activity data and biometric data to incorporate emotional balance into health indices.

[0380] A "health index" is a numerical indicator that quantifies the overall state of physical and mental health, and is calculated based on activity data and emotional data.

[0381] "Improvement suggestions" are specific suggestions for improving behavior, style, or environment, generated based on the user's health index and emotional data, and are notified to the user's device.

[0382] This invention is a technology for comprehensively managing the physical and mental health of users. The system is primarily implemented using user terminals and servers.

[0383] The user terminal is equipped with sensors to collect activity information and biometric data. Specifically, it can measure voice tone, heart rate, and exercise patterns. For example, when a user is running, it can detect heart rate, exercise rhythm, and speech tone in real time. The collected data is preprocessed within the terminal, including noise reduction and outlier filtering.

[0384] The server receives data sent from the user's terminal. The received data is stored in a database and analyzed using an emotion engine. The emotion engine implements a generative AI model and performs advanced analysis to incorporate emotional balance into a health index. This analysis makes it possible to identify the user's stress level and emotional changes.

[0385] The analysis results are generated as improvement suggestions and notified to the user's device. For example, if a user shows a high stress level after a meeting, a notification is sent recommending taking a short break or engaging in relaxing activities. This allows users to live healthier lives based on actionable improvement measures.

[0386] For example, if a user exercises regularly and then positive emotions are detected, the system can provide feedback indicating that the exercise was effective, which can then be used to inform future exercise plans.

[0387] Examples of prompt messages include the following:

[0388] "Please explain how to analyze individual emotional states in real time using user activity data and heart rate. Show how this data can be used to provide users with relaxation recommendations."

[0389] "I would like more detailed information about the process of generating a daily emotional balance report using data obtained through users' daily activities. In particular, please explain how to identify times of day when stress levels are high and suggest countermeasures."

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] The user terminal uses sensors to collect user activity information and biometric data. Inputs include the user's voice tone, heart rate, and exercise patterns. The terminal acquires this raw data in real time and uses it as input data for subsequent processing. Specifically, it records heart rate during running and voice tone during everyday conversation.

[0393] Step 2:

[0394] The device performs noise reduction and outlier filtering on the collected data. The input is raw sensor data, and by performing data cleaning based on this, it outputs data with improved accuracy. For example, it removes background noise from loud voices in audio data, and corrects sudden fluctuations in heart rate detected as noise during exercise.

[0395] Step 3:

[0396] The terminal periodically sends pre-processed data to the server. The input is pre-processed data, which is then communicated as output to the server. If a significant change or anomaly is detected, the data is immediately reported to the server. This enables real-time feedback.

[0397] Step 4:

[0398] The server stores the received data in a database and prepares it for analysis. The input consists of various data sent from terminals, which are accumulated through registration in the database. This enables long-term trend analysis and management of the data.

[0399] Step 5:

[0400] The server uses an emotion engine to analyze data and calculate health indices and emotional balance. The input is accumulated data, and a generative AI model is used for pattern recognition and emotional state estimation. This generates the user's current stress level and overall emotional balance as output.

[0401] Step 6:

[0402] The server generates specific improvement suggestions based on the analysis results and notifies the user's terminal. The input is the results of the health index and emotional balance, and it outputs helpful feedback and suggestions for action for the user. For example, it might send a notification recommending relaxation techniques to reduce stress.

[0403] Step 7:

[0404] Users utilize suggestions received from their devices to manage their own health. Input consists of notifications from the device, which they can use to adjust their daily actions and lifestyle habits. Specifically, they practice relaxation techniques according to the suggestions they receive.

[0405] (Application Example 2)

[0406] 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."

[0407] Many brick-and-mortar stores struggle to understand customers' mental health and emotions and provide customized services based on that understanding. Therefore, there is a need for means to improve the customer experience and increase satisfaction. In particular, there is a lack of real-time systems to appropriately respond to customers experiencing stress.

[0408] 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.

[0409] In this invention, the server includes means for analyzing biometric data and voice data, means for determining the mental health state using an emotion recognition function, and means for generating improvement suggestions and notifying the user terminal. This makes it possible to grasp the emotions of customers in real time in physical stores and provide individualized responses and services based on that.

[0410] "Biometric data" refers to information that quantifies a user's physical condition, such as heart rate and heart rate variability.

[0411] "Voice data" refers to digital information that records a user's speech and tone of voice, and is used to analyze their emotional state.

[0412] An "analysis device" is a computer system that processes collected data and evaluates mental and physical health.

[0413] "Emotion recognition functionality" is a technology that analyzes a user's biometric data and voice data to infer the user's emotional state.

[0414] "Mental health status" refers to a psychological state that comprehensively evaluates the user's emotions and stress levels.

[0415] "Improvement suggestions" are recommendations aimed at improving the user's health by suggesting specific actions or activities based on collected and analyzed data.

[0416] A "user terminal" refers to a mobile device or wearable device used to receive information and notify the user.

[0417] A "physical store" refers to a retail business that conducts sales activities or provides services in a physical location.

[0418] The system that realizes this application example is a technology that grasps the emotional state of customers in real time and provides individualized responses based on that. Here, it is implemented using smart devices and a server system.

[0419] The device takes the form of smart glasses and is equipped with sensors for collecting biometric data and a microphone for analyzing voice. The smart glasses use a generative AI model to analyze changes in voice tone and heart rate to infer the customer's emotions. After the customer's emotional state is determined, the data is transmitted to a server using a wireless communication module.

[0420] The server performs complex data processing based on the received data. This uses an integrated data analysis system (for example, leveraging Python's NumPy and Pandas libraries). Emotion recognition capabilities evaluate the customer's stress level and mental health status from the analyzed data and generate improvement suggestions as needed. These suggestions are sent to terminals in real time and notified to store staff. This allows for the suggestion of relaxation activities and services tailored to each customer.

[0421] For example, when a customer entering a store is deemed to need relaxation, an alert is sent via smart glasses to store staff recommending a hot drink. In this way, retail services can be customized to meet customer needs.

[0422] Examples of prompts include, "Please suggest how to provide service in situations where a customer has shown positive emotions," and "Please advise on how to handle situations where a customer is experiencing high stress levels." By using these prompts, the generative AI model can derive appropriate responses based on the situation.

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

[0424] Step 1:

[0425] The device collects the customer's biometric and voice data through smart glasses. Sensors are used to acquire information such as heart rate and voice tone, which is then recorded as digital data.

[0426] Step 2:

[0427] The device uses a generated AI model to analyze the customer's emotional state from digital data. This analysis uses acquired heart rate and voice tone as input data and applies an emotion recognition algorithm to infer the customer's emotional state. The output is emotional information such as the customer's stress level and relaxation state.

[0428] Step 3:

[0429] The terminal sends emotional information to the server. Using a wireless communication module, it sends inferred emotional data to the server. This transmitted data includes emotional information necessary for subsequent processing.

[0430] Step 4:

[0431] Based on the emotional information received by the server, data analysis is performed. Here, Python libraries (such as NumPy and Pandas) are used to collect and integrate data, making it possible to evaluate the overall mental health of customers.

[0432] Step 5:

[0433] The server generates improvement suggestions. Based on the analysis results, it creates recommendations for what kind of relaxation activities and services should be provided, especially for customers with high stress levels, and uses a generative AI model to derive specific suggestions.

[0434] Step 6:

[0435] The server sends the generated improvement suggestions to the terminal. Using the suggested information, the terminal sends notifications to store staff in real time. This allows staff to quickly provide appropriate service to customers.

[0436] Step 7:

[0437] Users receive the services provided. The in-store customer experience is improved, and individually customized services are delivered based on the customer's emotional and mental well-being.

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

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

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

[0441] [Third Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0454] This invention is a system for supporting user health management, collecting user activity data, evaluating health status, and notifying users of improvement suggestions. This system mainly consists of a server, a user terminal, and a series of sensor devices.

[0455] User terminal operation

[0456] The user device typically functions as a smartphone, constantly receiving data from sensors and sending it to the server. The device then notifies the user of improvement suggestions and advertisements in an appropriate format. For example, the user can see in real time how many steps they take in a day and how their heart rate has changed over time.

[0457] Server operation

[0458] The server receives activity data sent by the user and stores it in a database. The server analyzes the stored data, calculates a health index, and compares it to a baseline value. For example, it calculates the user's BMI and calorie consumption to determine if the user is within a healthy range. If an anomaly is detected, the server uses a generative AI model to construct improvement suggestions based on the user's activity data and health status.

[0459] Specific examples of user usage scenarios

[0460] Users carry their smartphones with them in their daily lives. For example, based on step count and heart rate data collected through daily commutes and exercise, the server generates personalized health reports. Users can track their daily and weekly health trends on their smartphones and adjust their diet and exercise as needed.

[0461] The system notifies users' mobile devices with suggestions for improving their health. For example, it might send a notification such as, "You've reached your step goal for today. We recommend adding stretching next," providing the necessary motivation.

[0462] This system allows users to monitor changes in their lifestyle and proactively work towards maintaining their health.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The device collects user activity data from sensors, including steps taken, location information, and heart rate. The acquired data is temporarily stored within the device.

[0466] Step 2:

[0467] The device sends collected activity data to the server at regular intervals. Because the data is sent compactly, transmission is performed with minimal burden.

[0468] Step 3:

[0469] The server stores the received data in the database. New data is added in real time and managed on a per-user basis.

[0470] Step 4:

[0471] The server analyzes activity data stored in the database and calculates a health index. The calculation results are compared to general health standards.

[0472] Step 5:

[0473] If the server detects an anomaly using the comparison results, it will use a generated AI model to create improvement suggestions tailored to the user. These suggestions will include specific actions and points to note.

[0474] Step 6:

[0475] The server sends the generated improvement suggestions and related advertising information to the user's terminal. This transmission is performed using a secure communication protocol.

[0476] Step 7:

[0477] The device notifies the user of improvement suggestions and advertising information received from the server. Notifications are sent via push notifications and in-app messages.

[0478] Step 8:

[0479] Users refer to the improvement suggestions they receive and take action. They can check their progress and plan actions based on the suggestions through the app.

[0480] (Example 1)

[0481] 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."

[0482] In modern times, there is a need to efficiently collect and analyze individual activity information for health management and generate appropriate health improvement suggestions. However, conventional technologies have difficulty fully utilizing user activity information to provide effective health management, and generating personalized improvement suggestions is a particular challenge.

[0483] 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.

[0484] In this invention, the server includes means for collecting user activity information using a measuring device, means for transmitting said activity information to a computing device, and means for storing said activity information in a storage device and calculating health indicators based on said activity information. This makes it possible to provide users with personalized health improvement suggestions and support their health management.

[0485] "User activity information" refers to information about an individual's actions and biological state in their daily life, including step count, location data, and biometric measurements.

[0486] A "measuring device" is a device used to collect user activity information and has the function of measuring activity status using sensors.

[0487] A "computational device" is a device that receives collected user activity information and performs necessary calculations, and functions as a server.

[0488] A "memory device" is a device used by a computing device to store user activity information received from the device, making the data available for subsequent processing.

[0489] A "health indicator" is a numerical value calculated based on a user's activity information, serving as a standard for evaluating their physical condition and health status.

[0490] A "reference value" is a value that indicates a target or acceptable range when evaluating health indicators, and is used as a standard for determining a normal state of health.

[0491] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and generate output such as improvement suggestions.

[0492] "Public information" refers to general information and notices provided to users, including health advice and product information.

[0493] This invention provides a system for efficiently supporting users' health management. This system mainly consists of a server, a user terminal, and a measuring device.

[0494] User terminal functions

[0495] The user terminal typically functions as a smartphone and is carried daily. This terminal has the function of receiving data from the measuring device and transmitting it to the server in real time. Specifically, it receives activity information such as steps taken and heart rate collected by the measuring device when the user is going about their daily life or exercising. Based on this information, the user terminal provides health-related notifications. For example, the user may receive a notification in real time saying, "Your heart rate is stable today."

[0496] Server Functions

[0497] The server receives activity information transmitted from the user's terminal and stores the data in its internal storage. Furthermore, it analyzes this data using a generative AI model and calculates health indicators. These health indicators are compared to baseline values, and if an abnormality is detected, the generative AI technology is used to generate specific improvement suggestions tailored to the user's health condition. These suggestions provide the user with specific, goal-oriented motivation, such as, "You achieved 7,000 steps today. Next, try an hour of yoga on the weekend."

[0498] Examples of specific cases and prompt statements

[0499] For example, if a user runs five days a week, their activity data is recorded by a measuring device and analyzed on a server. As a result, the generating AI model can compare this data with past data to create suggestions for a new running schedule. An example of a prompt in this case would be the text, "Analyze the user's running data for this week and generate exercise suggestions for next week."

[0500] This system allows users to easily understand their health status and work towards maintaining or improving their health based on personalized improvement suggestions.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The user terminal collects activity information from the measuring device. This information includes steps taken, heart rate, and location data. The input is the user's real-time activity, and this data is taken into the terminal as output. Specifically, when the user is walking or exercising, the terminal connects to the measuring device via Bluetooth or Wi-Fi and acquires data.

[0504] Step 2:

[0505] The user terminal transmits collected activity information to the server. The input is the acquired user activity data, which is then transmitted to the server as output. The terminal ensures the security of the data being transmitted by using a secure communication protocol. Specifically, data transfer is performed automatically at regular intervals.

[0506] Step 3:

[0507] The server stores activity information sent from user terminals in its storage device. The input is activity data sent from the terminal, and the output is storage in a database in an organized format. Specifically, the server classifies the data by date and time and activity type, and stores it in a way that allows for quick access.

[0508] Step 4:

[0509] The server analyzes data stored in its storage device. The input is saved user activity information, and the output is health indicators and anomaly detection results. Using a generative AI model, it converts each data point into health indicators and compares them to baseline values ​​to check for abnormalities. Specifically, it sends prompt messages to the generative AI model to flexibly perform analysis based on the data.

[0510] Step 5:

[0511] The server uses AI-generated technology based on health indicators to create improvement suggestions. The input is the health indicators and abnormality detection results obtained from the analysis, and the output is specific suggestions for improving the user's health. For example, based on the user's recent activity level, it may generate suggestions such as, "We recommend you walk more."

[0512] Step 6:

[0513] The server notifies the user terminal of the generated improvement suggestions. The input is the generated health improvement suggestions, and the output is the notification to the user. The user terminal receives the suggestions and displays them as push notifications, making it easy for the user to check the notification content. Specifically, the timing of sending notifications is optimized to match the user's daily routine.

[0514] (Application Example 1)

[0515] 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."

[0516] Maintaining people's health is a crucial issue in modern society. However, many people are unable to adequately understand their own health status due to their busy daily lives, and therefore do not receive proper health management. To address this issue, there is a need for a system that provides personalized health support based on the user's living environment and behavior.

[0517] 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.

[0518] In this invention, the server includes means for collecting user behavior data using measuring means and transmitting said behavior data to a computing device, means for accumulating said behavior data and calculating health indicators based on said behavior data, and means for detecting abnormalities by comparing said health indicators with reference values ​​and generating improvement suggestions. As a result, users can understand their own health status in real time and use healthy products and services that correspond to their movements in physical stores.

[0519] A "user" is an individual who uses the system to collect activity data and receive health management support.

[0520] "Behavioral data" refers to information about a user's activities, such as distance traveled, location information, and heart rate.

[0521] "Measurement means" refers to hardware including sensor devices for acquiring user behavior data.

[0522] A "computational processing unit" refers to a computer system used to process and analyze collected behavioral data.

[0523] "Health indicators" are numerical values ​​or information that indicate a user's health status, calculated based on behavioral data.

[0524] A "reference value" refers to a numerical value or range that serves as a standard when evaluating health indicators.

[0525] "Abnormal" refers to a state where health indicators deviate from the reference value, indicating a health problem or a condition requiring improvement.

[0526] "Improvement suggestions" are suggestions and advice provided to users based on health indicators, aimed at improving their health status.

[0527] "User terminal" refers to portable information devices such as smartphones and tablets that users carry with them.

[0528] "Information" includes data on improvement suggestions and recommended products within stores.

[0529] "Inside the store" refers to the physical retail location or facility that a user visits.

[0530] "Products" refer to the products and services offered within a store.

[0531] "Events" refer to events and special programs held within the store.

[0532] The programs necessary to implement this system are installed on user devices such as smartphones and tablets, as well as on servers running in the cloud.

[0533] The server receives behavioral data sent by users and stores it in a cloud-based database. This process utilizes database services such as Google Cloud BigQuery and Amazon Relational Database Service (RDS).

[0534] The accumulated data is analyzed using generative AI models such as "TensorFlow" and "PyTorch." This analysis calculates user health indicators and compares them to reference values. If an anomaly is detected, improvement suggestions are generated based on the generative AI model. These improvement suggestions include behavioral guidelines within physical stores or recommendations for healthy products and services.

[0535] The user's device uses the smartphone's built-in sensors (accelerometer and GPS) to acquire behavioral data in real time and transmit it to the server. The system also notifies the user of generated improvement suggestions and product recommendations. Push notification systems on iOS and Android devices are used as the appropriate technology for this purpose.

[0536] For example, if a user moves around the store for more than 20 minutes and the system determines that they haven't gotten enough exercise after lunch, it will send a notification such as, "We recommend using the cardio machines located in specific areas. Please also try our low-calorie menu on your next visit."

[0537] An example of a prompt message might be: "The user's location information and heart rate were found to be significantly below normal levels. Please generate suggestions to encourage the use of cardio machines in the store."

[0538] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0539] Step 1:

[0540] The user collects behavioral data (distance traveled, location information, heart rate) in real time using sensors in their smartphone. The input is the smartphone's accelerometer and GPS data, and the output is a behavioral data stream combining this data.

[0541] Step 2:

[0542] The user terminal sends the collected behavioral data to the server. The input is the behavioral data obtained in step 1, and the output is the latest behavioral information obtained through data transfer to the server.

[0543] Step 3:

[0544] The server stores the received behavioral data in a cloud database. The input is the behavioral data sent from the terminal, and the output is the updated behavioral data record in the database.

[0545] Step 4:

[0546] The server analyzes the accumulated behavioral data using generative AI models such as TensorFlow. At this stage, data processing is performed for calculating health indicators; the input is behavioral data from the database, and the output is the calculated health indicators.

[0547] Step 5:

[0548] The server compares the calculated health indicators to reference values ​​and uses a generative AI model to generate improvement suggestions. The input to this process is the health indicators obtained in step 4, and the output is appropriate improvement suggestions for the user.

[0549] Step 6:

[0550] The server notifies the user terminal of the improvement suggestions. The input is the improvement suggestions generated in step 5, and the output is the information displayed as a push notification on the user terminal.

[0551] Step 7:

[0552] Users check notifications displayed on their devices and adjust their in-store behavior accordingly. The input is the notification information pushed to the device, and the output is changes in user behavior or promotion of the target product.

[0553] 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.

[0554] This invention is a system equipped with emotion recognition capabilities to further enhance user health management. In addition to collecting user activity data, it can comprehensively manage the user's mental health state by utilizing an emotion engine.

[0555] User terminal operation

[0556] The user terminal is equipped with sensors that collect activity data, as well as sensors that can detect voice input and changes in heart rate. This allows the system to analyze the user's voice tone and heart rate patterns to infer their emotions in real time. It also includes a mechanism to capture heart rate trends when the user is exercising or relaxing and transmit this information to the emotion engine.

[0557] Server operation

[0558] The server integrates activity and emotional data transmitted from user terminals, stores it in a database, and analyzes it from multiple perspectives. Specifically, it uses an emotion engine to analyze patterns in the data and incorporates emotional balance as additional information to the user's health index. For example, it estimates the user's stress level and, if necessary, suggests relaxing sports or mental health care.

[0559] Specific examples of user usage scenarios

[0560] Based on data collected through the user's daily activities (commuting, exercise, work, etc.), the server generates a daily health and emotional state report. This report can also indicate periods of high stress, providing valuable information for the user to take appropriate action.

[0561] For example, if a user exhibits a high stress level during work, the device will send a notification recommending relaxation activities or short breaks. Similarly, if a positive emotional state is detected after exercise, this can be used to inform future exercise plans.

[0562] The introduction of this system will enable users to achieve a balance between their physical and mental health in their daily lives.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The device uses sensors to collect user activity and voice data. This includes steps taken, location information, heart rate, and voice tone. This data is temporarily stored on the device.

[0566] Step 2:

[0567] The device infers the user's emotions from collected activity and voice data and processes this as emotion data. The emotion engine handles this process and analyzes the data.

[0568] Step 3:

[0569] The device sends collected activity and emotion data to the server. This transmission is conducted via a secure protocol, protecting user privacy.

[0570] Step 4:

[0571] The server integrates the received activity data and emotion data and stores it in a database. The stored data is managed on a per-user basis.

[0572] Step 5:

[0573] The server analyzes the integrated data and calculates health and emotional indices. The calculated indices are then compared to baseline values.

[0574] Step 6:

[0575] The server generates personalized improvement suggestions based on health and emotional indices. These suggestions include recommendations for stress-reducing activities and relaxation techniques.

[0576] Step 7:

[0577] The server sends the generated improvement suggestions to the user's terminal. The notification also includes personalized advice tailored to the user's emotional state.

[0578] Step 8:

[0579] The device notifies the user of improvement suggestions received from the server. The notification is displayed as a push notification, and the user can tap it to view details.

[0580] Step 9:

[0581] Based on the information they receive, users can check their health and emotional state and adjust their behavior as needed. They can also use the app to refer to past data and understand trends.

[0582] (Example 2)

[0583] 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."

[0584] Traditional health management systems primarily calculate physical health indicators based on user activity data, often neglecting to consider mental health information such as emotional state and stress levels. As a result, users often struggle to grasp their overall health status.

[0585] 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.

[0586] In this invention, the server includes means for preprocessing user activity information and biometric data, performing noise reduction and outlier filtering; means for generating analysis results that incorporate emotional balance into a health index using an emotion engine; and means for generating improvement suggestions in accordance with emotional changes and stress levels, and notifying the user terminal. This enables the user to achieve harmony between physical and mental health.

[0587] A "sensor" is a device used to collect user activity information and biometric data, and has the function of measuring voice tone, heart rate, exercise patterns, etc.

[0588] A "processing device" is a machine or software that receives data collected from sensors, performs preprocessing, noise reduction, and filtering, and prepares the data for analysis.

[0589] An "emotion engine" is software or an algorithm that performs analysis based on a user's activity data and biometric data to incorporate emotional balance into health indices.

[0590] A "health index" is a numerical indicator that quantifies the overall state of physical and mental health, and is calculated based on activity data and emotional data.

[0591] "Improvement suggestions" are specific suggestions for improving behavior, style, or environment, generated based on the user's health index and emotional data, and are notified to the user's device.

[0592] This invention is a technology for comprehensively managing the physical and mental health of users. The system is primarily implemented using user terminals and servers.

[0593] The user terminal is equipped with sensors to collect activity information and biometric data. Specifically, it can measure voice tone, heart rate, and exercise patterns. For example, when a user is running, it can detect heart rate, exercise rhythm, and speech tone in real time. The collected data is preprocessed within the terminal, including noise reduction and outlier filtering.

[0594] The server receives data sent from the user's terminal. The received data is stored in a database and analyzed using an emotion engine. The emotion engine implements a generative AI model and performs advanced analysis to incorporate emotional balance into a health index. This analysis makes it possible to identify the user's stress level and emotional changes.

[0595] The analysis results are generated as improvement suggestions and notified to the user's device. For example, if a user shows a high stress level after a meeting, a notification is sent recommending taking a short break or engaging in relaxing activities. This allows users to live healthier lives based on actionable improvement measures.

[0596] For example, if a user exercises regularly and then positive emotions are detected, the system can provide feedback indicating that the exercise was effective, which can then be used to inform future exercise plans.

[0597] Examples of prompt messages include the following:

[0598] "Please explain how to analyze individual emotional states in real time using user activity data and heart rate. Show how this data can be used to provide users with relaxation recommendations."

[0599] "I would like more detailed information about the process of generating a daily emotional balance report using data obtained through users' daily activities. In particular, please explain how to identify times of day when stress levels are high and suggest countermeasures."

[0600] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0601] Step 1:

[0602] The user terminal uses sensors to collect user activity information and biometric data. Inputs include the user's voice tone, heart rate, and exercise patterns. The terminal acquires this raw data in real time and uses it as input data for subsequent processing. Specifically, it records heart rate during running and voice tone during everyday conversation.

[0603] Step 2:

[0604] The device performs noise reduction and outlier filtering on the collected data. The input is raw sensor data, and by performing data cleaning based on this, it outputs data with improved accuracy. For example, it removes background noise from loud voices in audio data, and corrects sudden fluctuations in heart rate detected as noise during exercise.

[0605] Step 3:

[0606] The terminal periodically sends pre-processed data to the server. The input is pre-processed data, which is then communicated as output to the server. If a significant change or anomaly is detected, the data is immediately reported to the server. This enables real-time feedback.

[0607] Step 4:

[0608] The server stores the received data in a database and prepares it for analysis. The input consists of various data sent from terminals, which are accumulated through registration in the database. This enables long-term trend analysis and management of the data.

[0609] Step 5:

[0610] The server uses an emotion engine to analyze data and calculate health indices and emotional balance. The input is accumulated data, and a generative AI model is used for pattern recognition and emotional state estimation. This generates the user's current stress level and overall emotional balance as output.

[0611] Step 6:

[0612] The server generates specific improvement suggestions based on the analysis results and notifies the user's terminal. The input is the results of the health index and emotional balance, and it outputs helpful feedback and suggestions for action for the user. For example, it might send a notification recommending relaxation techniques to reduce stress.

[0613] Step 7:

[0614] Users utilize suggestions received from their devices to manage their own health. Input consists of notifications from the device, which they can use to adjust their daily actions and lifestyle habits. Specifically, they practice relaxation techniques according to the suggestions they receive.

[0615] (Application Example 2)

[0616] 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."

[0617] Many brick-and-mortar stores struggle to understand customers' mental health and emotions and provide customized services based on that understanding. Therefore, there is a need for means to improve the customer experience and increase satisfaction. In particular, there is a lack of real-time systems to appropriately respond to customers experiencing stress.

[0618] 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.

[0619] In this invention, the server includes means for analyzing biometric data and voice data, means for determining the mental health state using an emotion recognition function, and means for generating improvement suggestions and notifying the user terminal. This makes it possible to grasp the emotions of customers in real time in physical stores and provide individualized responses and services based on that.

[0620] "Biometric data" refers to information that quantifies a user's physical condition, such as heart rate and heart rate variability.

[0621] "Voice data" refers to digital information that records a user's speech and tone of voice, and is used to analyze their emotional state.

[0622] An "analysis device" is a computer system that processes collected data and evaluates mental and physical health.

[0623] "Emotion recognition functionality" is a technology that analyzes a user's biometric data and voice data to infer the user's emotional state.

[0624] "Mental health status" refers to a psychological state that comprehensively evaluates the user's emotions and stress levels.

[0625] "Improvement suggestions" are recommendations aimed at improving the user's health by suggesting specific actions or activities based on collected and analyzed data.

[0626] A "user terminal" refers to a mobile device or wearable device used to receive information and notify the user.

[0627] A "physical store" refers to a retail business that conducts sales activities or provides services in a physical location.

[0628] The system that realizes this application example is a technology that grasps the emotional state of customers in real time and provides individualized responses based on that. Here, it is implemented using smart devices and a server system.

[0629] The device takes the form of smart glasses and is equipped with sensors for collecting biometric data and a microphone for analyzing voice. The smart glasses use a generative AI model to analyze changes in voice tone and heart rate to infer the customer's emotions. After the customer's emotional state is determined, the data is transmitted to a server using a wireless communication module.

[0630] The server performs complex data processing based on the received data. This uses an integrated data analysis system (for example, leveraging Python's NumPy and Pandas libraries). Emotion recognition capabilities evaluate the customer's stress level and mental health status from the analyzed data and generate improvement suggestions as needed. These suggestions are sent to terminals in real time and notified to store staff. This allows for the suggestion of relaxation activities and services tailored to each customer.

[0631] For example, when a customer entering a store is deemed to need relaxation, an alert is sent via smart glasses to store staff recommending a hot drink. In this way, retail services can be customized to meet customer needs.

[0632] Examples of prompts include, "Please suggest how to provide service in situations where a customer has shown positive emotions," and "Please advise on how to handle situations where a customer is experiencing high stress levels." By using these prompts, the generative AI model can derive appropriate responses based on the situation.

[0633] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0634] Step 1:

[0635] The device collects the customer's biometric and voice data through smart glasses. Sensors are used to acquire information such as heart rate and voice tone, which is then recorded as digital data.

[0636] Step 2:

[0637] The device uses a generated AI model to analyze the customer's emotional state from digital data. This analysis uses acquired heart rate and voice tone as input data and applies an emotion recognition algorithm to infer the customer's emotional state. The output is emotional information such as the customer's stress level and relaxation state.

[0638] Step 3:

[0639] The terminal sends emotional information to the server. Using a wireless communication module, it sends inferred emotional data to the server. This transmitted data includes emotional information necessary for subsequent processing.

[0640] Step 4:

[0641] Based on the emotional information received by the server, data analysis is performed. Here, Python libraries (such as NumPy and Pandas) are used to collect and integrate data, making it possible to evaluate the overall mental health of customers.

[0642] Step 5:

[0643] The server generates improvement suggestions. Based on the analysis results, it creates recommendations for what kind of relaxation activities and services should be provided, especially for customers with high stress levels, and uses a generative AI model to derive specific suggestions.

[0644] Step 6:

[0645] The server sends the generated improvement suggestions to the terminal. Using the suggested information, the terminal sends notifications to store staff in real time. This allows staff to quickly provide appropriate service to customers.

[0646] Step 7:

[0647] Users receive the services provided. The in-store customer experience is improved, and individually customized services are delivered based on the customer's emotional and mental well-being.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] [Fourth Embodiment]

[0652] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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).

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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".

[0665] This invention is a system for supporting user health management, collecting user activity data, evaluating health status, and notifying users of improvement suggestions. This system mainly consists of a server, a user terminal, and a series of sensor devices.

[0666] User terminal operation

[0667] The user device typically functions as a smartphone, constantly receiving data from sensors and sending it to the server. The device then notifies the user of improvement suggestions and advertisements in an appropriate format. For example, the user can see in real time how many steps they take in a day and how their heart rate has changed over time.

[0668] Server operation

[0669] The server receives activity data sent by the user and stores it in a database. The server analyzes the stored data, calculates a health index, and compares it to a baseline value. For example, it calculates the user's BMI and calorie consumption to determine if the user is within a healthy range. If an anomaly is detected, the server uses a generative AI model to construct improvement suggestions based on the user's activity data and health status.

[0670] Specific examples of user usage scenarios

[0671] Users carry their smartphones with them in their daily lives. For example, based on step count and heart rate data collected through daily commutes and exercise, the server generates personalized health reports. Users can track their daily and weekly health trends on their smartphones and adjust their diet and exercise as needed.

[0672] The system notifies users' mobile devices with suggestions for improving their health. For example, it might send a notification such as, "You've reached your step goal for today. We recommend adding stretching next," providing the necessary motivation.

[0673] This system allows users to monitor changes in their lifestyle and proactively work towards maintaining their health.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The device collects user activity data from sensors, including steps taken, location information, and heart rate. The acquired data is temporarily stored within the device.

[0677] Step 2:

[0678] The device sends collected activity data to the server at regular intervals. Because the data is sent compactly, transmission is performed with minimal burden.

[0679] Step 3:

[0680] The server stores the received data in the database. New data is added in real time and managed on a per-user basis.

[0681] Step 4:

[0682] The server analyzes activity data stored in the database and calculates a health index. The calculation results are compared to general health standards.

[0683] Step 5:

[0684] If the server detects an anomaly using the comparison results, it will use a generated AI model to create improvement suggestions tailored to the user. These suggestions will include specific actions and points to note.

[0685] Step 6:

[0686] The server sends the generated improvement suggestions and related advertising information to the user's terminal. This transmission is performed using a secure communication protocol.

[0687] Step 7:

[0688] The device notifies the user of improvement suggestions and advertising information received from the server. Notifications are sent via push notifications and in-app messages.

[0689] Step 8:

[0690] Users refer to the improvement suggestions they receive and take action. They can check their progress and plan actions based on the suggestions through the app.

[0691] (Example 1)

[0692] 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".

[0693] In modern times, there is a need to efficiently collect and analyze individual activity information for health management and generate appropriate health improvement suggestions. However, conventional technologies have difficulty fully utilizing user activity information to provide effective health management, and generating personalized improvement suggestions is a particular challenge.

[0694] 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.

[0695] In this invention, the server includes means for collecting user activity information using a measuring device, means for transmitting said activity information to a computing device, and means for storing said activity information in a storage device and calculating health indicators based on said activity information. This makes it possible to provide users with personalized health improvement suggestions and support their health management.

[0696] "User activity information" refers to information about an individual's actions and biological state in their daily life, including step count, location data, and biometric measurements.

[0697] A "measuring device" is a device used to collect user activity information and has the function of measuring activity status using sensors.

[0698] A "computational device" is a device that receives collected user activity information and performs necessary calculations, and functions as a server.

[0699] A "memory device" is a device used by a computing device to store user activity information received from the device, making the data available for subsequent processing.

[0700] A "health indicator" is a numerical value calculated based on a user's activity information, serving as a standard for evaluating their physical condition and health status.

[0701] A "reference value" is a value that indicates a target or acceptable range when evaluating health indicators, and is used as a standard for determining a normal state of health.

[0702] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and generate output such as improvement suggestions.

[0703] "Public information" refers to general information and notices provided to users, including health advice and product information.

[0704] This invention provides a system for efficiently supporting users' health management. This system mainly consists of a server, a user terminal, and a measuring device.

[0705] User terminal functions

[0706] The user terminal typically functions as a smartphone and is carried daily. This terminal has the function of receiving data from the measuring device and transmitting it to the server in real time. Specifically, it receives activity information such as steps taken and heart rate collected by the measuring device when the user is going about their daily life or exercising. Based on this information, the user terminal provides health-related notifications. For example, the user may receive a notification in real time saying, "Your heart rate is stable today."

[0707] Server Functions

[0708] The server receives activity information transmitted from the user's terminal and stores the data in its internal storage. Furthermore, it analyzes this data using a generative AI model and calculates health indicators. These health indicators are compared to baseline values, and if an abnormality is detected, the generative AI technology is used to generate specific improvement suggestions tailored to the user's health condition. These suggestions provide the user with specific, goal-oriented motivation, such as, "You achieved 7,000 steps today. Next, try an hour of yoga on the weekend."

[0709] Examples of specific cases and prompt statements

[0710] For example, if a user runs five days a week, their activity data is recorded by a measuring device and analyzed on a server. As a result, the generating AI model can compare this data with past data to create suggestions for a new running schedule. An example of a prompt in this case would be the text, "Analyze the user's running data for this week and generate exercise suggestions for next week."

[0711] This system allows users to easily understand their health status and work towards maintaining or improving their health based on personalized improvement suggestions.

[0712] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0713] Step 1:

[0714] The user terminal collects activity information from the measuring device. This information includes steps taken, heart rate, and location data. The input is the user's real-time activity, and this data is taken into the terminal as output. Specifically, when the user is walking or exercising, the terminal connects to the measuring device via Bluetooth or Wi-Fi and acquires data.

[0715] Step 2:

[0716] The user terminal transmits collected activity information to the server. The input is the acquired user activity data, which is then transmitted to the server as output. The terminal ensures the security of the data being transmitted by using a secure communication protocol. Specifically, data transfer is performed automatically at regular intervals.

[0717] Step 3:

[0718] The server stores activity information sent from user terminals in its storage device. The input is activity data sent from the terminal, and the output is storage in a database in an organized format. Specifically, the server classifies the data by date and time and activity type, and stores it in a way that allows for quick access.

[0719] Step 4:

[0720] The server analyzes data stored in its storage device. The input is saved user activity information, and the output is health indicators and anomaly detection results. Using a generative AI model, it converts each data point into health indicators and compares them to baseline values ​​to check for abnormalities. Specifically, it sends prompt messages to the generative AI model to flexibly perform analysis based on the data.

[0721] Step 5:

[0722] The server uses AI-generated technology based on health indicators to create improvement suggestions. The input is the health indicators and abnormality detection results obtained from the analysis, and the output is specific suggestions for improving the user's health. For example, based on the user's recent activity level, it may generate suggestions such as, "We recommend you walk more."

[0723] Step 6:

[0724] The server notifies the user terminal of the generated improvement suggestions. The input is the generated health improvement suggestions, and the output is the notification to the user. The user terminal receives the suggestions and displays them as push notifications, making it easy for the user to check the notification content. Specifically, the timing of sending notifications is optimized to match the user's daily routine.

[0725] (Application Example 1)

[0726] 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".

[0727] Maintaining people's health is a crucial issue in modern society. However, many people are unable to adequately understand their own health status due to their busy daily lives, and therefore do not receive proper health management. To address this issue, there is a need for a system that provides personalized health support based on the user's living environment and behavior.

[0728] 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.

[0729] In this invention, the server includes means for collecting user behavior data using measuring means and transmitting said behavior data to a computing device, means for accumulating said behavior data and calculating health indicators based on said behavior data, and means for detecting abnormalities by comparing said health indicators with reference values ​​and generating improvement suggestions. As a result, users can understand their own health status in real time and use healthy products and services that correspond to their movements in physical stores.

[0730] A "user" is an individual who uses the system to collect activity data and receive health management support.

[0731] "Behavioral data" refers to information about a user's activities, such as distance traveled, location information, and heart rate.

[0732] "Measurement means" refers to hardware including sensor devices for acquiring user behavior data.

[0733] A "computational processing unit" refers to a computer system used to process and analyze collected behavioral data.

[0734] "Health indicators" are numerical values ​​or information that indicate a user's health status, calculated based on behavioral data.

[0735] A "reference value" refers to a numerical value or range that serves as a standard when evaluating health indicators.

[0736] "Abnormal" refers to a state where health indicators deviate from the reference value, indicating a health problem or a condition requiring improvement.

[0737] "Improvement suggestions" are suggestions and advice provided to users based on health indicators, aimed at improving their health status.

[0738] "User terminal" refers to portable information devices such as smartphones and tablets that users carry with them.

[0739] "Information" includes data on improvement suggestions and recommended products within stores.

[0740] "Inside the store" refers to the physical retail location or facility that a user visits.

[0741] "Products" refer to the products and services offered within a store.

[0742] "Events" refer to events and special programs held within the store.

[0743] The programs necessary to implement this system are installed on user devices such as smartphones and tablets, as well as on servers running in the cloud.

[0744] The server receives behavioral data sent by users and stores it in a cloud-based database. This process utilizes database services such as Google Cloud BigQuery and Amazon Relational Database Service (RDS).

[0745] The accumulated data is analyzed using generative AI models such as "TensorFlow" and "PyTorch." This analysis calculates user health indicators and compares them to reference values. If an anomaly is detected, improvement suggestions are generated based on the generative AI model. These improvement suggestions include behavioral guidelines within physical stores or recommendations for healthy products and services.

[0746] The user's device uses the smartphone's built-in sensors (accelerometer and GPS) to acquire behavioral data in real time and transmit it to the server. The system also notifies the user of generated improvement suggestions and product recommendations. Push notification systems on iOS and Android devices are used as the appropriate technology for this purpose.

[0747] For example, if a user moves around the store for more than 20 minutes and the system determines that they haven't gotten enough exercise after lunch, it will send a notification such as, "We recommend using the cardio machines located in specific areas. Please also try our low-calorie menu on your next visit."

[0748] An example of a prompt message might be: "The user's location information and heart rate were found to be significantly below normal levels. Please generate suggestions to encourage the use of cardio machines in the store."

[0749] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0750] Step 1:

[0751] The user collects behavioral data (distance traveled, location information, heart rate) in real time using sensors in their smartphone. The input is the smartphone's accelerometer and GPS data, and the output is a behavioral data stream combining this data.

[0752] Step 2:

[0753] The user terminal sends the collected behavioral data to the server. The input is the behavioral data obtained in step 1, and the output is the latest behavioral information obtained through data transfer to the server.

[0754] Step 3:

[0755] The server stores the received behavioral data in a cloud database. The input is the behavioral data sent from the terminal, and the output is the updated behavioral data record in the database.

[0756] Step 4:

[0757] The server analyzes the accumulated behavioral data using generative AI models such as TensorFlow. At this stage, data processing is performed for calculating health indicators; the input is behavioral data from the database, and the output is the calculated health indicators.

[0758] Step 5:

[0759] The server compares the calculated health indicators to reference values ​​and uses a generative AI model to generate improvement suggestions. The input to this process is the health indicators obtained in step 4, and the output is appropriate improvement suggestions for the user.

[0760] Step 6:

[0761] The server notifies the user terminal of the improvement suggestions. The input is the improvement suggestions generated in step 5, and the output is the information displayed as a push notification on the user terminal.

[0762] Step 7:

[0763] Users check notifications displayed on their devices and adjust their in-store behavior accordingly. The input is the notification information pushed to the device, and the output is changes in user behavior or promotion of the target product.

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

[0765] This invention is a system equipped with emotion recognition capabilities to further enhance user health management. In addition to collecting user activity data, it can comprehensively manage the user's mental health state by utilizing an emotion engine.

[0766] User terminal operation

[0767] The user terminal is equipped with sensors that collect activity data, as well as sensors that can detect voice input and changes in heart rate. This allows the system to analyze the user's voice tone and heart rate patterns to infer their emotions in real time. It also includes a mechanism to capture heart rate trends when the user is exercising or relaxing and transmit this information to the emotion engine.

[0768] Server operation

[0769] The server integrates activity and emotional data transmitted from user terminals, stores it in a database, and analyzes it from multiple perspectives. Specifically, it uses an emotion engine to analyze patterns in the data and incorporates emotional balance as additional information to the user's health index. For example, it estimates the user's stress level and, if necessary, suggests relaxing sports or mental health care.

[0770] Specific examples of user usage scenarios

[0771] Based on data collected through the user's daily activities (commuting, exercise, work, etc.), the server generates a daily health and emotional state report. This report can also indicate periods of high stress, providing valuable information for the user to take appropriate action.

[0772] For example, if a user exhibits a high stress level during work, the device will send a notification recommending relaxation activities or short breaks. Similarly, if a positive emotional state is detected after exercise, this can be used to inform future exercise plans.

[0773] The introduction of this system will enable users to achieve a balance between their physical and mental health in their daily lives.

[0774] The following describes the processing flow.

[0775] Step 1:

[0776] The device uses sensors to collect user activity and voice data. This includes steps taken, location information, heart rate, and voice tone. This data is temporarily stored on the device.

[0777] Step 2:

[0778] The device infers the user's emotions from collected activity and voice data and processes this as emotion data. The emotion engine handles this process and analyzes the data.

[0779] Step 3:

[0780] The device sends collected activity and emotion data to the server. This transmission is conducted via a secure protocol, protecting user privacy.

[0781] Step 4:

[0782] The server integrates the received activity data and emotion data and stores it in a database. The stored data is managed on a per-user basis.

[0783] Step 5:

[0784] The server analyzes the integrated data and calculates health and emotional indices. The calculated indices are then compared to baseline values.

[0785] Step 6:

[0786] The server generates personalized improvement suggestions based on health and emotional indices. These suggestions include recommendations for stress-reducing activities and relaxation techniques.

[0787] Step 7:

[0788] The server sends the generated improvement suggestions to the user's terminal. The notification also includes personalized advice tailored to the user's emotional state.

[0789] Step 8:

[0790] The device notifies the user of improvement suggestions received from the server. The notification is displayed as a push notification, and the user can tap it to view details.

[0791] Step 9:

[0792] Based on the information they receive, users can check their health and emotional state and adjust their behavior as needed. They can also use the app to refer to past data and understand trends.

[0793] (Example 2)

[0794] 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".

[0795] Traditional health management systems primarily calculate physical health indicators based on user activity data, often neglecting to consider mental health information such as emotional state and stress levels. As a result, users often struggle to grasp their overall health status.

[0796] 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.

[0797] In this invention, the server includes means for preprocessing user activity information and biometric data, performing noise reduction and outlier filtering; means for generating analysis results that incorporate emotional balance into a health index using an emotion engine; and means for generating improvement suggestions in accordance with emotional changes and stress levels, and notifying the user terminal. This enables the user to achieve harmony between physical and mental health.

[0798] A "sensor" is a device used to collect user activity information and biometric data, and has the function of measuring voice tone, heart rate, exercise patterns, etc.

[0799] A "processing device" is a machine or software that receives data collected from sensors, performs preprocessing, noise reduction, and filtering, and prepares the data for analysis.

[0800] An "emotion engine" is software or an algorithm that performs analysis based on a user's activity data and biometric data to incorporate emotional balance into health indices.

[0801] A "health index" is a numerical indicator that quantifies the overall state of physical and mental health, and is calculated based on activity data and emotional data.

[0802] "Improvement suggestions" are specific suggestions for improving behavior, style, or environment, generated based on the user's health index and emotional data, and are notified to the user's device.

[0803] This invention is a technology for comprehensively managing the physical and mental health of users. The system is primarily implemented using user terminals and servers.

[0804] The user terminal is equipped with sensors to collect activity information and biometric data. Specifically, it can measure voice tone, heart rate, and exercise patterns. For example, when a user is running, it can detect heart rate, exercise rhythm, and speech tone in real time. The collected data is preprocessed within the terminal, including noise reduction and outlier filtering.

[0805] The server receives data sent from the user's terminal. The received data is stored in a database and analyzed using an emotion engine. The emotion engine implements a generative AI model and performs advanced analysis to incorporate emotional balance into a health index. This analysis makes it possible to identify the user's stress level and emotional changes.

[0806] The analysis results are generated as improvement suggestions and notified to the user's device. For example, if a user shows a high stress level after a meeting, a notification is sent recommending taking a short break or engaging in relaxing activities. This allows users to live healthier lives based on actionable improvement measures.

[0807] For example, if a user exercises regularly and then positive emotions are detected, the system can provide feedback indicating that the exercise was effective, which can then be used to inform future exercise plans.

[0808] Examples of prompt messages include the following:

[0809] "Please explain how to analyze individual emotional states in real time using user activity data and heart rate. Show how this data can be used to provide users with relaxation recommendations."

[0810] "I would like more detailed information about the process of generating a daily emotional balance report using data obtained through users' daily activities. In particular, please explain how to identify times of day when stress levels are high and suggest countermeasures."

[0811] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0812] Step 1:

[0813] The user terminal uses sensors to collect user activity information and biometric data. Inputs include the user's voice tone, heart rate, and exercise patterns. The terminal acquires this raw data in real time and uses it as input data for subsequent processing. Specifically, it records heart rate during running and voice tone during everyday conversation.

[0814] Step 2:

[0815] The device performs noise reduction and outlier filtering on the collected data. The input is raw sensor data, and by performing data cleaning based on this, it outputs data with improved accuracy. For example, it removes background noise from loud voices in audio data, and corrects sudden fluctuations in heart rate detected as noise during exercise.

[0816] Step 3:

[0817] The terminal periodically sends pre-processed data to the server. The input is pre-processed data, which is then communicated as output to the server. If a significant change or anomaly is detected, the data is immediately reported to the server. This enables real-time feedback.

[0818] Step 4:

[0819] The server stores the received data in a database and prepares it for analysis. The input consists of various data sent from terminals, which are accumulated through registration in the database. This enables long-term trend analysis and management of the data.

[0820] Step 5:

[0821] The server uses an emotion engine to analyze data and calculate health indices and emotional balance. The input is accumulated data, and a generative AI model is used for pattern recognition and emotional state estimation. This generates the user's current stress level and overall emotional balance as output.

[0822] Step 6:

[0823] The server generates specific improvement suggestions based on the analysis results and notifies the user's terminal. The input is the results of the health index and emotional balance, and it outputs helpful feedback and suggestions for action for the user. For example, it might send a notification recommending relaxation techniques to reduce stress.

[0824] Step 7:

[0825] Users utilize suggestions received from their devices to manage their own health. Input consists of notifications from the device, which they can use to adjust their daily actions and lifestyle habits. Specifically, they practice relaxation techniques according to the suggestions they receive.

[0826] (Application Example 2)

[0827] 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".

[0828] Many brick-and-mortar stores struggle to understand customers' mental health and emotions and provide customized services based on that understanding. Therefore, there is a need for means to improve the customer experience and increase satisfaction. In particular, there is a lack of real-time systems to appropriately respond to customers experiencing stress.

[0829] 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.

[0830] In this invention, the server includes means for analyzing biometric data and voice data, means for determining the mental health state using an emotion recognition function, and means for generating improvement suggestions and notifying the user terminal. This makes it possible to grasp the emotions of customers in real time in physical stores and provide individualized responses and services based on that.

[0831] "Biometric data" refers to information that quantifies a user's physical condition, such as heart rate and heart rate variability.

[0832] "Voice data" refers to digital information that records a user's speech and tone of voice, and is used to analyze their emotional state.

[0833] An "analysis device" is a computer system that processes collected data and evaluates mental and physical health.

[0834] "Emotion recognition functionality" is a technology that analyzes a user's biometric data and voice data to infer the user's emotional state.

[0835] "Mental health status" refers to a psychological state that comprehensively evaluates the user's emotions and stress levels.

[0836] "Improvement suggestions" are recommendations aimed at improving the user's health by suggesting specific actions or activities based on collected and analyzed data.

[0837] A "user terminal" refers to a mobile device or wearable device used to receive information and notify the user.

[0838] A "physical store" refers to a retail business that conducts sales activities or provides services in a physical location.

[0839] The system that realizes this application example is a technology that grasps the emotional state of customers in real time and provides individualized responses based on that. Here, it is implemented using smart devices and a server system.

[0840] The device takes the form of smart glasses and is equipped with sensors for collecting biometric data and a microphone for analyzing voice. The smart glasses use a generative AI model to analyze changes in voice tone and heart rate to infer the customer's emotions. After the customer's emotional state is determined, the data is transmitted to a server using a wireless communication module.

[0841] The server performs complex data processing based on the received data. This uses an integrated data analysis system (for example, leveraging Python's NumPy and Pandas libraries). Emotion recognition capabilities evaluate the customer's stress level and mental health status from the analyzed data and generate improvement suggestions as needed. These suggestions are sent to terminals in real time and notified to store staff. This allows for the suggestion of relaxation activities and services tailored to each customer.

[0842] For example, when a customer entering a store is deemed to need relaxation, an alert is sent via smart glasses to store staff recommending a hot drink. In this way, retail services can be customized to meet customer needs.

[0843] Examples of prompts include, "Please suggest how to provide service in situations where a customer has shown positive emotions," and "Please advise on how to handle situations where a customer is experiencing high stress levels." By using these prompts, the generative AI model can derive appropriate responses based on the situation.

[0844] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0845] Step 1:

[0846] The device collects the customer's biometric and voice data through smart glasses. Sensors are used to acquire information such as heart rate and voice tone, which is then recorded as digital data.

[0847] Step 2:

[0848] The device uses a generated AI model to analyze the customer's emotional state from digital data. This analysis uses acquired heart rate and voice tone as input data and applies an emotion recognition algorithm to infer the customer's emotional state. The output is emotional information such as the customer's stress level and relaxation state.

[0849] Step 3:

[0850] The terminal sends emotional information to the server. Using a wireless communication module, it sends inferred emotional data to the server. This transmitted data includes emotional information necessary for subsequent processing.

[0851] Step 4:

[0852] Based on the emotional information received by the server, data analysis is performed. Here, Python libraries (such as NumPy and Pandas) are used to collect and integrate data, making it possible to evaluate the overall mental health of customers.

[0853] Step 5:

[0854] The server generates improvement suggestions. Based on the analysis results, it creates recommendations for what kind of relaxation activities and services should be provided, especially for customers with high stress levels, and uses a generative AI model to derive specific suggestions.

[0855] Step 6:

[0856] The server sends the generated improvement suggestions to the terminal. Using the suggested information, the terminal sends notifications to store staff in real time. This allows staff to quickly provide appropriate service to customers.

[0857] Step 7:

[0858] Users receive the services provided. The in-store customer experience is improved, and individually customized services are delivered based on the customer's emotional and mental well-being.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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."

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0880] The following is further disclosed regarding the embodiments described above.

[0881] (Claim 1)

[0882] A means for collecting user activity data using sensors and transmitting said activity data to a processing device,

[0883] A means for accumulating the activity data and calculating a health index based on the activity data,

[0884] A means for detecting abnormalities by comparing the health index with a reference value and generating improvement suggestions,

[0885] A means of notifying the user terminal of the improvement suggestion and providing advertising information,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that the sensor that collects activity data measures at least the number of steps, location information, and heart rate.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that the generated improvement suggestions include dietary improvements and exercise methods.

[0891] "Example 1"

[0892] (Claim 1)

[0893] A means for collecting user activity information using a measuring device,

[0894] Means for transmitting the activity information to a computing device,

[0895] A means for storing the activity information in a storage device and calculating health indicators based on the activity information,

[0896] A means for detecting abnormalities by comparing the health indicator with reference values ​​and generating improvement suggestions using AI technology,

[0897] Means for notifying the user device of the improvement proposal and providing public information,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, characterized in that the measuring device for collecting activity information measures at least the number of movements, location data, and biological measurements.

[0901] (Claim 3)

[0902] The system according to claim 1, characterized in that the generated improvement suggestions include improvements to nutritional intake and methods of physical activity.

[0903] "Application Example 1"

[0904] (Claim 1)

[0905] A means for collecting user behavior data using a measurement means and transmitting said behavior data to a computing device,

[0906] A means for accumulating the behavioral data and calculating health indicators based on the behavioral data,

[0907] A means for detecting abnormalities by comparing the health indicator with a reference value and generating improvement suggestions,

[0908] A means of notifying the user terminal of the improvement suggestion and providing information,

[0909] A means of monitoring customers' behavior within the store and recommending products and promotions tailored to their health condition,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, characterized in that the measuring means for collecting behavioral data measures at least distance traveled, location information, and heart rate.

[0913] (Claim 3)

[0914] The system according to claim 1, characterized in that the generated improvement suggestions include nutritional improvements and exercise types, and provide service and product suggestions within a physical store.

[0915] "Example 2 of combining an emotion engine"

[0916] (Claim 1)

[0917] A means for collecting user activity information and biometric data using sensors and transmitting said data to a processing device,

[0918] Means for preprocessing the data and performing noise reduction and outlier filtering,

[0919] A means for accumulating the data and generating analysis results that incorporate emotional balance into the health index using an emotion engine,

[0920] A means for generating improvement suggestions based on the analysis results, corresponding to changes in emotions and stress levels, and notifying the user terminal,

[0921] A system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, characterized in that the sensor that collects activity information measures at least voice tone, heart rate, and exercise pattern.

[0924] (Claim 3)

[0925] The system according to claim 1, characterized in that the generated improvement suggestions include recommendations for relaxation activities and suggestions for exercise plans based on emotional state.

[0926] "Application example 2 of combining emotional engines"

[0927] (Claim 1)

[0928] A means for collecting the user's biometric and voice data using sensors and transmitting the data to an analysis device,

[0929] A means for integrating and accumulating the data and determining the mental health status using an emotion recognition function,

[0930] A means for detecting abnormalities in the mental health state by comparing it to a standard value and generating improvement suggestions,

[0931] A means of notifying the user terminal of the improvement suggestion and providing information to improve customer service,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, characterized in that the sensor that collects biometric data and voice data is capable of analyzing at least heart rate and voice tone to infer emotions.

[0935] (Claim 3)

[0936] The system according to claim 1, characterized in that the generated improvement suggestions include suggestions regarding relaxation activities and recommendations for mental health care. [Explanation of symbols]

[0937] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting user activity data using sensors and transmitting said activity data to a processing device, A means for accumulating the activity data and calculating a health index based on the activity data, A means for detecting abnormalities by comparing the health index with a reference value and generating improvement suggestions, A means of notifying the user terminal of the improvement suggestion and providing advertising information, A system that includes this.

2. The system according to claim 1, characterized in that the sensor that collects activity data measures at least the number of steps, location information, and heart rate.

3. The system according to claim 1, characterized in that the generated improvement suggestions include improvements to diet and exercise methods.

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