system

Abstract

We provide the system. [Solution] A data collection method for acquiring biometric data from users, A data analysis means for analyzing the user's biological rhythm based on the aforementioned biometric data, An environmental synchronization means that optimizes environmental conditions in accordance with the aforementioned biological rhythm, A health advice generation means that generates health advice based on the analysis results, 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 a chatbot 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] Modern health management systems often rely on a standardized approach and do not fully consider the individual biological rhythms of users. As a result, it is difficult to provide appropriate advice and environmental adjustments according to individual health needs, and sustainable health management is challenging. Furthermore, there is a need for means to effectively utilize the diverse data obtained from users and provide individually optimized health management strategies.

Means for Solving the Problems

[0005] This invention provides a means for acquiring biometric data from a user and analyzing the user's biological rhythm based on this data. It also includes an environmental synchronization means for setting optimal environmental conditions based on the analyzed biological rhythm. Furthermore, by using a system that combines this with a health advice generation means for generating and providing health advice to the user based on the analysis results, individually optimized health management is realized. This enables sustainable health management tailored to individual characteristics.

[0006] "User" refers to an individual who uses this system and is the entity receiving health management services.

[0007] "Biometric data" refers to numerical information about a user's health status and lifestyle, such as heart rate, steps taken, and sleep patterns.

[0008] "Biological rhythms" are indicators that show the periodic fluctuations in a user's lifestyle patterns and physical functions.

[0009] "Data collection means" refers to methods and devices for acquiring biometric data from users.

[0010] "Data analysis means" refers to methods and processes for analyzing collected biometric data to identify a user's biological rhythm.

[0011] "Environmental synchronization means" refers to methods or devices for automatically setting and adjusting the optimal living environment based on the user's biological rhythms.

[0012] "Health advice generation method" refers to a method or process for generating information and recommendations to support a healthy lifestyle from analyzed biometric data. [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, a processor with a reference numeral (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, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0019] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 that supports health management using the user's biometric data. Specific embodiments are described below.

[0035] First, the device synchronizes with wearable devices and smartphones to collect the user's biometric data, such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and periodically sent to a server.

[0036] The server stores the received biometric data in a database and performs analysis. Specifically, it compares it with past data to identify the user's biological rhythm. In this process, it calculates the user's sleep time, wake time, peak activity times, etc., and identifies the patterns associated with them.

[0037] Next, the environmental synchronization function is activated. Based on the identified biological rhythm, the server sends instructions to the smart device to adjust the user's living environment. For example, it might change the lighting to a warmer color or adjust the air conditioner to match bedtime. At this stage, it may also suggest playing ambient sounds to promote mental relaxation.

[0038] Subsequently, the health advice generation system takes over. Based on the analysis results, the server creates specific advice to encourage users to improve their lifestyle. This advice covers a wide range of topics, from meal plans and exercise suggestions to stress management methods. This information is then sent to the device as a notification.

[0039] For example, if data is collected showing that a user goes to bed at a different time each day, the server will identify the user's poor sleep patterns and generate advice such as, "Your sleep quality will improve if you make it a habit to go to bed at the same time every day." It will also send environmental settings to the smart device, such as automatically changing the lighting to a warmer color one hour before bedtime. In this way, users can receive personalized health management and a comfortable environment.

[0040] Furthermore, the system uses user feedback to verify the effectiveness of its advice and make necessary adjustments. This two-way interaction is designed to enable long-term and sustainable health management.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device acquires biometric data from the wearable device, such as the user's heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and then formatted for later transmission to the server.

[0044] Step 2:

[0045] The device collects biometric data at regular intervals and sends it to a server via the internet. During this process, the system verifies the success of the data transmission and attempts to resend it if there are any problems. It also displays a notification to the user upon successful transmission.

[0046] Step 3:

[0047] The server stores the received biometric data in a database. The data is saved along with date and time information, forming a dataset ready for future analysis.

[0048] Step 4:

[0049] The server begins data analysis, analyzing biometric data to identify the user's biological rhythms. This analysis identifies lifestyle patterns such as bedtime, wake-up time, and most active periods.

[0050] Step 5:

[0051] The server generates environmental synchronization instructions based on the analysis results. It determines lighting and temperature settings that match the user's biological rhythm and sends instructions to the smart device to execute these settings.

[0052] Step 6:

[0053] The server generates health advice. Considering the data analysis results, it creates specific lifestyle improvement suggestions for the user. This advice includes recommendations regarding diet, exercise, and stress management.

[0054] Step 7:

[0055] The device notifies the user of generated advice and setting instructions. To facilitate user implementation of these suggestions, it provides reminder and calendar integration features.

[0056] Step 8:

[0057] The user enters feedback on the advice provided into the device. This feedback includes comments on the feasibility and effectiveness of the suggestions.

[0058] Step 9:

[0059] The device collects user feedback and sends it to the server. This feedback information is used to generate further personalized advice.

[0060] Step 10:

[0061] The server analyzes feedback and adjusts advice and settings as needed. This ensures that users continuously receive more appropriate health management.

[0062] (Example 1)

[0063] 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."

[0064] In recent years, the importance of personal health management has increased, but traditional methods have difficulty providing timely and effective advice tailored to individual health conditions. Furthermore, while there is a growing demand for personalized health management that matches users' lifestyles and living environments, existing systems are not adequately supporting this.

[0065] 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.

[0066] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's biometric pattern based on the biometric information, and environmental synchronization means for adjusting environmental conditions according to the biometric pattern. This makes it possible to personalize and quickly and effectively provide appropriate advice tailored to the individual's health condition.

[0067] "Information gathering means" refers to devices or methods that have the function of acquiring biometric information from a user and transmitting it to other components within the system.

[0068] "Information analysis means" refers to devices or programs that calculate a user's biological pattern based on acquired biological information, analyze it, and then diagnose and evaluate their health status.

[0069] An "environmental synchronization means" is a mechanism that issues commands to adjust the user's living environment to an appropriate state based on the analysis results, and causes the target device to execute those commands.

[0070] A "proposal generation means" refers to a method or device for generating and effectively presenting lifestyle improvement suggestions tailored to the user's health condition and lifestyle.

[0071] A "feedback adjustment mechanism" is a system that improves the overall accuracy of the system by receiving feedback from users and adjusting and updating the suggested content and environment settings based on that feedback.

[0072] This invention is a system that supports health management based on the user's biometric information. The following describes embodiments for carrying out the invention.

[0073] First, the device synchronizes with wearable devices and smartphones. Specifically, it uses communication protocols such as Bluetooth and Wi-Fi to collect biometric information such as the user's heart rate, steps taken, and sleep patterns, and temporarily stores it in its internal memory.

[0074] Next, the server receives biometric information transmitted from the terminal at regular intervals. For security reasons, the SSL / TLS protocol is used for this communication. The received data is stored in a relational database (e.g., MySQL® or PostgreSQL).

[0075] The server analyzes stored biometric data to identify the user's biometric patterns. This analysis employs machine learning algorithms using Python libraries (such as scikit-learn and pandas). The analysis clarifies the user's sleep and wake times, as well as their peak activity times.

[0076] The server then sends instructions to the smart device based on the analysis results. These instructions may include adjusting the color temperature of the lighting or changing the settings of the air conditioner to optimize the environment. This can be done using an API for controlling home appliances (e.g., a smart home API).

[0077] In addition, the server uses a generative AI model to generate suggestions for improving lifestyle habits. These suggestions include meal plans, exercise methods, and specific advice for stress management, which are sent to the user's device as notifications. This allows the user to receive effective advice tailored to their own health condition.

[0078] A concrete example of its use would be providing advice based on user data, such as, "Incorporating regular exercise will help maintain your health." An example of a prompt would be, "Generate and output appropriate health advice based on the user's data."

[0079] Ultimately, users provide feedback on the effectiveness of the suggestions, allowing the system to continuously evolve and provide more accurate advice and environmental adjustments. This enables long-term and sustainable health management.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The device synchronizes with wearable devices and smartphones to collect the user's biometric information. It acquires data such as heart rate, steps, and sleep patterns as input, and temporarily stores this data in its internal memory. The collected data is obtained as electrical signals and converted into digital data.

[0083] Step 2:

[0084] The device sends biometric information collected at regular intervals to the server. The input is a set of multiple biometric data stored in memory, and the output is an encrypted data packet. This packet is transmitted securely using the SSL / TLS protocol.

[0085] Step 3:

[0086] The server decrypts the received data packets and stores the biometric information in a database. The input is encrypted data packets, and the output is a record of the biometric information in the database. The data is written to a relational database such as MySQL or PostgreSQL.

[0087] Step 4:

[0088] The server analyzes stored biometric information to identify the user's biometric patterns. The input is historical data from a database, and the output is rhythmic information such as the user's sleep and wake times and peak activity times. This process utilizes machine learning algorithms to analyze data trends.

[0089] Step 5:

[0090] The server generates environmental adjustment instructions based on identified biometric patterns and sends them to the smart device. The input is rhythm information, and the output is a message with environmental setting instructions. This allows lighting and air conditioning to be automatically adjusted to match the user's rhythm.

[0091] Step 6:

[0092] The server uses an AI model to generate specific health suggestions based on the user's biometric information and identified rhythms. Rhythm information and health data are used as input, and the output includes suggestions for diet and exercise, as well as stress management advice. These suggestions are then notified to the user's device.

[0093] Step 7:

[0094] Users implement the provided advice and provide feedback to the system about its effectiveness. Input consists of the user's personal impressions and data, while output is the server-side recording of the feedback and the resulting system adjustments. The advice may be updated based on the feedback.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In modern society, there is a need to efficiently manage users' health while simultaneously offering product recommendations that meet their needs. However, conventional health management systems only analyze users' biometric data and provide health advice, lacking concrete product recommendations or connections to purchasing behavior. Therefore, a more consistent integration of health management and product purchase is necessary.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's biological cycle based on the biometric information, health advice generation means for generating health advice based on the analysis results, and electronic commerce means for presenting product information based on the health advice and facilitating purchases through electronic payment. This makes it possible to support the user's health improvement in their daily life while effectively and efficiently encouraging the purchase of related products.

[0100] "Information gathering means" refers to means of obtaining biometric information from a user.

[0101] "Information analysis means" refers to means for analyzing the user's biological cycle using acquired biological state information.

[0102] "Environmental synchronization means" are means for optimizing environmental conditions in accordance with the analyzed biological cycle.

[0103] A "health advice generation method" is a means for generating health advice for a user based on the results of information analysis.

[0104] "Electronic commerce methods" refer to means of presenting product information based on health advice and enabling users to make purchases through electronic payment.

[0105] In the system implementing this invention, a wearable device or smartphone used by the user first collects biometric information. This information is then transmitted to a server via the terminal. The data acquired by the information collection means includes heart rate, exercise level, sleep patterns, and so on.

[0106] The server analyzes the collected biometric information using information analysis tools. The analysis is performed to identify the user's biological cycle by comparing it with past data. This reveals things like sleep patterns and peak activity times. Furthermore, a health advice generation tool is used to generate specific health advice based on the analysis results, which is then sent to the user's device. This may include suggestions for meal plans and exercise routines.

[0107] In addition, based on the generated health advice, relevant product information is presented to the user via e-commerce. This product information is displayed on the user's personal device in a format that allows for electronic payment. The user can then review the suggested products and easily proceed with the purchase process.

[0108] For example, if a user's lack of exercise is detected from their biometric data, the server may suggest fitness-related products or gym memberships. This allows the user to easily start healthy habits. Another example of a prompt for the generating AI model is: "Generate appropriate product suggestions based on the user's health data. Example: Suggest fitness-related products for a user who is not getting enough exercise."

[0109] The entire system utilizes a data analysis platform for information analysis and health advice generation. Specifically, services such as AWS® and Google® Cloud are sometimes used as examples. Communication protocols that prioritize security and efficiency are used for sending and receiving information.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device acquires biometric information from the user's wearable device or smartphone. This includes data such as heart rate, steps taken, and sleep patterns. It receives biometric data directly from the user's device as input and temporarily stores the data within the device.

[0113] Step 2:

[0114] The device transmits the collected biometric information to the server. Specifically, it packages the collected data using a secure communication protocol and transfers it to the server. The output is biometric data formatted in a format that can be processed by the server.

[0115] Step 3:

[0116] The server analyzes the received biometric data using information analysis tools. It compares the input biometric data with past data to identify the user's biological cycle. The output is the analysis results, such as the user's sleep time and peak activity times.

[0117] Step 4:

[0118] The server uses a health advice generation mechanism based on the analysis results to generate specific health advice. Using the analysis results as input, it utilizes a generation AI model to create health advice tailored to the user. The output includes health advice, meal plans, and exercise suggestions.

[0119] Step 5:

[0120] The server uses e-commerce tools based on health advice to send relevant product information to the user's terminal. Based on the health advice generated as input, it selects product information and creates a suitable offer for the user. The output is a suggestion that includes product details and a purchase link.

[0121] Step 6:

[0122] The user reviews the suggested products displayed on the terminal and makes a purchase via electronic payment if necessary. The user uses product information transmitted from the server as input to complete the purchase process. The output is confirmation information regarding the purchased items.

[0123] 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.

[0124] This invention is a system that supports health management based on the user's biometric data and emotions. Here, we present a specific embodiment that includes an emotion engine.

[0125] First, the device acquires the user's biometric data, such as heart rate, steps taken, and sleep patterns, via a wearable device or smartphone. This data is stored on the device and prepared to be sent to a server later.

[0126] The server receives the collected biometric data and stores it in a database. Data analysis tools analyze the user's biological rhythms and identify lifestyle patterns such as sleep patterns and peak activity times.

[0127] Next, the emotion engine functions. It uses the camera and microphone on the device to collect the user's facial recognition data and voice data, and sends it to the server. The emotion engine analyzes this data to identify the user's emotional state (e.g., joy, sadness, stress).

[0128] The server generates and adjusts health advice based on the identified emotional state. For example, if the user is feeling stressed, it provides advice on breathing exercises or meditation to help them relax. Additionally, an environmental synchronization mechanism sends instructions to the smart device to adjust the environment according to the emotional state. Specifically, this includes changing the lighting to softer light or playing relaxation music to help the user relax.

[0129] For example, if the emotion engine detects that a user is showing signs of stress, the server will generate advice such as, "Try a 5-minute deep breathing session" to relieve stress. At the same time, it will send instructions to the smart device to change the room lighting to a more relaxing tone. This allows the user to enjoy individually optimized health management and a comfortable environment.

[0130] Ultimately, the system improves the accuracy of advice and environment synchronization based on user feedback. By inputting feedback into the terminal about the results of following the advice and the effects experienced, the server accumulates and analyzes information to provide more personalized suggestions, thereby improving the overall accuracy of the system. In this way, a system equipped with an emotion engine realizes comprehensive and sustainable health management that responds to the individual needs of users.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device synchronizes with the user's wearable device or personal electronic device to collect biometric data such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device for later analysis.

[0134] Step 2:

[0135] The device uses its built-in camera and microphone to collect user facial recognition data and voice data. This data is used to identify the user's emotional state.

[0136] Step 3:

[0137] The device collects biometric and emotion-related data and sends it to a server via the internet. Once the transmission is complete, the system confirms receipt of the data.

[0138] Step 4:

[0139] The server stores the received biometric data and emotion-related data in a database. This creates a history of the user's past and present health and emotional states.

[0140] Step 5:

[0141] The server's data analysis system analyzes biometric data to identify the user's biological rhythm. Simultaneously, the emotion engine processes emotion-related data to determine the user's current emotion.

[0142] Step 6:

[0143] The server generates health advice based on the user's biological rhythms and emotional state. For example, if the user indicates stress, it creates a specific action plan to reduce stress.

[0144] Step 7:

[0145] The server's environment synchronization mechanism identifies the optimal environmental conditions based on the user's emotional state. It then sends instructions to the smart device to adjust lighting, sound, and temperature.

[0146] Step 8:

[0147] The device notifies the user of health advice and environmental adjustment instructions generated by the device. Specific methods and suggestions are provided in an easy-to-understand format.

[0148] Step 9:

[0149] Users follow health advice and, if necessary, input the provided feedback into their device. They provide information about their own impressions and the effectiveness of the advice.

[0150] Step 10:

[0151] The device sends user feedback it collects to a server, which the system uses to analyze and improve the accuracy of health advice and environmental adjustments. The system incorporates user feedback to make future suggestions more personalized.

[0152] (Example 2)

[0153] 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".

[0154] In modern society, maintaining users' physical and mental health requires precise health management tailored to individual lifestyles and emotional states. However, conventional systems are limited to simple health advice based solely on biometric information, making comprehensive health management that considers the user's emotional state and living environment difficult.

[0155] 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.

[0156] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's lifestyle patterns based on the biometric information, and emotion analysis means for identifying the user's emotional state. This enables precise and comprehensive health management tailored to the user's individual health condition.

[0157] "Information gathering means" refers to a device or system designed to acquire biometric information from a user.

[0158] "Information analysis means" refers to a device or system for analyzing a user's lifestyle patterns based on collected biometric information.

[0159] An "advice generation means" is a device or system for generating health advice based on analysis results and the user's emotional state.

[0160] "Emotional analysis tools" refer to devices or systems designed to identify a user's emotional state.

[0161] A "harmonization tool" is a device or system for adjusting environmental conditions based on analysis results and emotional states.

[0162] A "personal information device" is a device or equipment used by a user individually for collecting information.

[0163] A "household device" is a device or system used to adjust environmental conditions within a household.

[0164] This system provides personalized health advice and environmental adjustments based on the user's biometric information and emotional state. First, the device acquires biometric information such as heart rate, steps taken, and sleep patterns via personal information devices like wearable devices and smartphones. To achieve this, the device periodically communicates with these devices using Bluetooth or Wi-Fi. The acquired data is temporarily stored within the device.

[0165] Subsequently, the device transmits biometric information to a server using a secure communication protocol. The server stores the received information in a database and uses data analysis tools to identify the user's lifestyle patterns. This analysis may involve machine learning algorithms using Python or similar data processing languages.

[0166] Furthermore, the device uses its built-in camera and microphone to collect user facial recognition data and voice data. This information is sent to a server, which uses sentiment analysis tools to identify the user's emotional state. In this process, image processing and machine learning libraries such as OpenCV and TENSORFLOW® are often used.

[0167] Based on analyzed lifestyle patterns and emotional states, the server generates optimal health advice for the user using an advice generation system. For example, if the user is identified as being in a high-stress state, advice such as "Try a 5-minute deep breathing session" might be provided. This advice is generated based on a generative AI model.

[0168] Furthermore, the server uses a harmonization mechanism to send environmental adjustment instructions to the corresponding home devices. For example, an instruction is sent to a smart device to change the room lighting to a warmer color to promote relaxation.

[0169] This system allows users to experience personalized health management and a comfortable environment. For example, if the emotional analysis detects signs of stress in the user, the server can generate advice such as, "To alleviate stress levels, start with deep breathing," and instruct the user to change the room lighting to calming colors. Through this entire process, the user's health is optimized.

[0170] An example of a prompt message might be: "Measure the user's current stress level and suggest appropriate health advice and environmental adjustments based on that level."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device acquires the user's biometric information through wearable devices and smartphones. It uses Bluetooth or Wi-Fi communication to collect heart rate, steps, and sleep patterns from the devices. The collected data is temporarily stored within the device. The input is biometric information from the wearable device, and the output is biometric information stored within the device.

[0174] Step 2:

[0175] The device sends the stored biometric information to the server. A secure protocol such as HTTPS is used for this transmission. The server stores the received data in a database. The input is the biometric information stored on the device, and the output is the biometric information stored in the database.

[0176] Step 3:

[0177] The server uses biometric information stored in the database to identify the user's lifestyle patterns through information analysis. Machine learning algorithms using programming languages ​​such as Python are used to calculate sleep times and peak activity times. The input is biometric information from the database, and the output is the analyzed lifestyle pattern data.

[0178] Step 4:

[0179] The device collects user facial recognition and voice data via its camera and microphone. This collected data is sent to a server. The input is the user's face and voice data, and the output is the facial recognition and voice data sent to the server.

[0180] Step 5:

[0181] The server uses emotion analysis tools to identify the user's emotional state from received facial recognition and voice data. Libraries such as OpenCV and TensorFlow are used to determine emotions like joy and stress. The input is the facial recognition and voice data sent to the server, and the output is the identified emotional state data.

[0182] Step 6:

[0183] The server generates health advice based on analyzed lifestyle pattern data and emotional state data using an advice generation mechanism. A generation AI model is utilized to create, for example, instructions for stress reduction. The input is lifestyle pattern data and emotional state data, and the output is health advice provided to the user.

[0184] Step 7:

[0185] The server, using a harmonization mechanism, transmits environmental adjustment instructions to household devices, along with the generated health advice. It sends instructions to devices such as smart light bulbs and audio equipment to adjust lighting or play music. The input is the generated health advice, and the output is the environmental adjustment instructions to the household devices.

[0186] Step 8:

[0187] Users input feedback into their terminals based on the advice and adjustments provided. This feedback is collected by the server and used to improve the system's accuracy. The input is user feedback, and the output is feedback data stored on the server.

[0188] (Application Example 2)

[0189] 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 device 14 will be referred to as the "terminal."

[0190] In traditional brick-and-mortar stores, it is difficult to provide individualized services that take into account each customer's emotional state and health condition. Furthermore, there is a lack of means to dynamically adjust the store environment according to individual needs, making it difficult to provide a shopping experience optimized for each customer.

[0191] 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.

[0192] In this invention, the server includes means for acquiring biometric data, means for identifying emotional states and generating product suggestions, and means for transmitting instructions to facility equipment and adjusting the environment. This enables optimized product suggestions and environmental adjustments based on the biometric data and emotional states of customers.

[0193] "Data collection means" refers to a device or method that includes a function for acquiring a user's biometric data.

[0194] "Data analysis means" refers to a device or method for analyzing acquired biometric data to identify the user's biological rhythm and emotional state.

[0195] "Environmental synchronization means" refers to a device or method for optimizing environmental conditions in accordance with the user's biological rhythm.

[0196] "Health advice generation means" refers to a device or method for generating health advice for a user based on analysis results.

[0197] "Product suggestion generation means" refers to a device or method for identifying the emotional state of a customer visiting a store and generating product suggestions based on that state.

[0198] "Environmental adjustment instruction means" refers to a device or method that generates instructions for adjusting the environment inside a store and transmits them to the facilities and equipment for carrying them out.

[0199] Modes for carrying out the invention

[0200] The system that realizes this invention is implemented using the following hardware and software. First, the terminal collects necessary data (heart rate, steps, sleep patterns, etc.) via a wearable device or smart terminal to acquire the user's biometric data. This information is stored on the terminal and sent to a server for processing.

[0201] The server stores the received biometric data in a database and analyzes the user's biological rhythm using data analysis tools (e.g., Apache® Hadoop). The analysis results are used to generate health advice tailored to the user through a health advice generation system. To identify the user's emotional state, the emotion engine collects facial recognition data and voice data using the camera and microphone installed in the terminal and requests analysis from the server.

[0202] Furthermore, the product suggestion generation means proposes the most suitable product based on the customer's identified emotional state. This suggestion is presented through a smart device installed in the store, such as a smart glasses display, while the environment adjustment instruction means sends instructions to the facility equipment to adjust, for example, the store's lighting and music to suit the customer's emotional state.

[0203] As an example, if the system determines that a particular customer is experiencing stress, it will display suggestions for relaxing products on their glasses and change the store's lighting to a softer tone. The suggested products could include aromatherapy oils effective in reducing stress.

[0204] An example of a prompt message could be: "Based on the user's latest emotional state data, suggest products that promote relaxation. Also, change the store lighting to a warmer color." This prompt message is used as input to recommend activities optimized for the user, utilizing a generative AI model.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The device collects user biometric data from wearable devices and smart devices. Inputs include heart rate, steps taken, and sleep patterns. This data is stored locally and later prepared for transmission to a server. Data collection is performed in real time, and the data is updated at regular intervals.

[0208] Step 2:

[0209] The terminal transmits the collected biometric data to the server. The input is the biometric data acquired in step 1, and the output is the data sent to the server, which is then stored in the database. Data communication is encrypted to ensure security.

[0210] Step 3:

[0211] The server analyzes the user's biological rhythms using data analysis tools based on biometric data stored in the database. The input is biometric data in the database, and the output is the user's lifestyle patterns, such as sleep times and peak activity times. Machine learning algorithms are used for data analysis to identify individual rhythms.

[0212] Step 4:

[0213] The device uses its camera and microphone to collect user facial recognition and voice data. This serves as input, and the collected data is sent to a server to be prepared for analysis as emotion data. The data is captured in real time based on facial expressions and voice tone.

[0214] Step 5:

[0215] The server uses an emotion engine to analyze facial recognition and voice data to identify the user's emotional state. Based on the facial recognition and voice data received as input, it identifies emotional states such as joy, sadness, and stress as output. The analysis results are dynamically updated to ensure that the most up-to-date emotional state is reflected in real time.

[0216] Step 6:

[0217] The server uses a generative AI model to input prompts based on identified emotional states, generating health advice and product suggestions. The input is the user's emotional state, and the output is health advice such as "Try a 5-minute deep breathing session" or suggestions for relaxing products.

[0218] Step 7:

[0219] The server generates and sends instructions to the facility's equipment for environmental adjustments. The input is a generated prompt message, and the output includes instructions for environmental settings such as adjusting the color of the store's lighting and selecting and playing music. This optimizes the user's shopping experience.

[0220] Step 8:

[0221] Users conduct an experience based on advice from the server and input feedback into their terminal. The input is data about the user's experience, and the entire system uses this feedback to improve its accuracy as output. The improved data is reflected in subsequent analyses and suggestions.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] This invention is a system that supports health management using the user's biometric data. Specific embodiments are described below.

[0239] First, the device synchronizes with wearable devices and smartphones to collect the user's biometric data, such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and periodically sent to a server.

[0240] The server stores the received biometric data in a database and performs analysis. Specifically, it compares it with past data to identify the user's biological rhythm. In this process, it calculates the user's sleep time, wake time, peak activity times, etc., and identifies the patterns associated with them.

[0241] Next, the environmental synchronization function is activated. Based on the identified biological rhythm, the server sends instructions to the smart device to adjust the user's living environment. For example, it might change the lighting to a warmer color or adjust the air conditioner to match bedtime. At this stage, it may also suggest playing ambient sounds to promote mental relaxation.

[0242] Subsequently, the health advice generation system takes over. Based on the analysis results, the server creates specific advice to encourage users to improve their lifestyle. This advice covers a wide range of topics, from meal plans and exercise suggestions to stress management methods. This information is then sent to the device as a notification.

[0243] For example, if data is collected showing that a user goes to bed at a different time each day, the server will identify the user's poor sleep patterns and generate advice such as, "Your sleep quality will improve if you make it a habit to go to bed at the same time every day." It will also send environmental settings to the smart device, such as automatically changing the lighting to a warmer color one hour before bedtime. In this way, users can receive personalized health management and a comfortable environment.

[0244] Furthermore, the system uses user feedback to verify the effectiveness of its advice and make necessary adjustments. This two-way interaction is designed to enable long-term and sustainable health management.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The device acquires biometric data from the wearable device, such as the user's heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and then formatted for later transmission to the server.

[0248] Step 2:

[0249] The device collects biometric data at regular intervals and sends it to a server via the internet. During this process, the system verifies the success of the data transmission and attempts to resend it if there are any problems. It also displays a notification to the user upon successful transmission.

[0250] Step 3:

[0251] The server stores the received biometric data in a database. The data is saved along with date and time information, forming a dataset ready for future analysis.

[0252] Step 4:

[0253] The server begins data analysis, analyzing biometric data to identify the user's biological rhythms. This analysis identifies lifestyle patterns such as bedtime, wake-up time, and most active periods.

[0254] Step 5:

[0255] The server generates environmental synchronization instructions based on the analysis results. It determines lighting and temperature settings that match the user's biological rhythm and sends instructions to the smart device to execute these settings.

[0256] Step 6:

[0257] The server generates health advice. Considering the data analysis results, it creates specific lifestyle improvement suggestions for the user. This advice includes recommendations regarding diet, exercise, and stress management.

[0258] Step 7:

[0259] The device notifies the user of generated advice and setting instructions. To facilitate user implementation of these suggestions, it provides reminder and calendar integration features.

[0260] Step 8:

[0261] The user enters feedback on the advice provided into the device. This feedback includes comments on the feasibility and effectiveness of the suggestions.

[0262] Step 9:

[0263] The device collects user feedback and sends it to the server. This feedback information is used to generate further personalized advice.

[0264] Step 10:

[0265] The server analyzes feedback and adjusts advice and settings as needed. This ensures that users continuously receive more appropriate health management.

[0266] (Example 1)

[0267] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] In recent years, the importance of personal health management has increased, but traditional methods have difficulty providing timely and effective advice tailored to individual health conditions. Furthermore, while there is a growing demand for personalized health management that matches users' lifestyles and living environments, existing systems are not adequately supporting this.

[0269] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0270] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's biometric pattern based on the biometric information, and environmental synchronization means for adjusting environmental conditions according to the biometric pattern. This makes it possible to personalize and quickly and effectively provide appropriate advice tailored to the individual's health condition.

[0271] "Information gathering means" refers to devices or methods that have the function of acquiring biometric information from a user and transmitting it to other components within the system.

[0272] "Information analysis means" refers to devices or programs that calculate a user's biological pattern based on acquired biological information, analyze it, and then diagnose and evaluate their health status.

[0273] An "environmental synchronization means" is a mechanism that issues commands to adjust the user's living environment to an appropriate state based on the analysis results, and causes the target device to execute those commands.

[0274] A "proposal generation means" refers to a method or device for generating and effectively presenting lifestyle improvement suggestions tailored to the user's health condition and lifestyle.

[0275] A "feedback adjustment mechanism" is a system that improves the overall accuracy of the system by receiving feedback from users and adjusting and updating the suggested content and environment settings based on that feedback.

[0276] This invention is a system that supports health management based on the user's biometric information. The following describes embodiments for carrying out the invention.

[0277] First, the device synchronizes with wearable devices and smartphones. Specifically, it uses communication protocols such as Bluetooth and Wi-Fi to collect biometric information such as the user's heart rate, steps taken, and sleep patterns, and temporarily stores it in its internal memory.

[0278] Next, the server receives biometric information transmitted from the terminal at regular intervals. For this communication, the SSL / TLS protocol is used considering security. The received data is stored in a relational database (e.g., MySQL or PostgreSQL).

[0279] The server analyzes the stored biometric information to identify the user's biometric pattern. For this analysis, machine learning algorithms using Python libraries (such as scikit-learn and pandas) are adopted. Through the analysis, the user's bedtime, wake-up time, and peak activity times are clarified.

[0280] After that, based on the analysis results, the server sends instructions to the smart device. To optimize the environment, adjustments such as the color temperature of lighting and changes in the air conditioner settings are made. For this, an API for home appliance control (e.g., Smart Home API) can be used.

[0281] In addition, the server uses a generative AI model to generate proposals for improving lifestyle habits. These proposals include specific advice on diet plans, exercise methods, and stress management and are sent as notifications to the terminal. Through this, the user can receive effective advice tailored to their health status.

[0282] As a specific example of use, it is conceivable to provide advice such as "Incorporating regular exercise contributes to maintaining a healthy state" based on the user's data. An example of a prompt sentence is "Generate and output appropriate health advice based on the user's data."

[0283] Finally, by the user providing feedback on the effectiveness of the proposal, the system continues to evolve so that more accurate advice and environmental adjustments can be made. Through this, long-term and sustainable health management is achieved.

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

[0285] Step 1:

[0286] The terminal synchronizes with a wearable device or a smartphone and collects the user's biometric information. It acquires data such as heart rate, number of steps, and sleep pattern as input, and temporarily stores them in the built-in memory. The collected data is acquired as an electrical signal and converted into digital data.

[0287] Step 2:

[0288] The terminal transmits the biometric information collected at regular intervals to the server. The input is a set of multiple biometric information stored in the memory, and an encrypted data packet is created as the output. This packet is securely transmitted using the SSL / TLS protocol.

[0289] Step 3:

[0290] The server decrypts the received data packet and stores the biometric information in the database. The input is the encrypted data packet, and the output is the record of biometric information in the database. The data is written into a relational database such as MySQL or PostgreSQL.

[0291] Step 4:

[0292] The server analyzes the stored biometric information and identifies the user's biometric pattern. The input is the historical data in the database, and the output is rhythm information such as the user's bedtime and wake-up time, peak activity time, etc. This process uses a machine learning algorithm to analyze the data trend.

[0293] Step 5:

[0294] The server generates environmental adjustment instructions based on identified biometric patterns and sends them to the smart device. The input is rhythm information, and the output is a message with environmental setting instructions. This allows lighting and air conditioning to be automatically adjusted to match the user's rhythm.

[0295] Step 6:

[0296] The server uses an AI model to generate specific health suggestions based on the user's biometric information and identified rhythms. Rhythm information and health data are used as input, and the output includes suggestions for diet and exercise, as well as stress management advice. These suggestions are then notified to the user's device.

[0297] Step 7:

[0298] Users implement the provided advice and provide feedback to the system about its effectiveness. Input consists of the user's personal impressions and data, while output is the server-side recording of the feedback and the resulting system adjustments. The advice may be updated based on the feedback.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] In modern society, there is a need to efficiently manage users' health while simultaneously offering product recommendations that meet their needs. However, conventional health management systems only analyze users' biometric data and provide health advice, lacking concrete product recommendations or connections to purchasing behavior. Therefore, a more consistent integration of health management and product purchase is necessary.

[0302] 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.

[0303] In this invention, the server includes an information collection means for acquiring biometric state information from a user, an information analysis means for analyzing the user's biological cycle based on the biometric state information, a health advice generation means for generating health advice based on the analysis result, and an e-commerce transaction means for presenting product information based on the health advice and performing purchases through electronic payment. Thereby, while supporting the improvement of the user's health in daily life, it becomes possible to effectively and efficiently promote the purchase of related products.

[0304] The "information collection means" is a means for acquiring biometric state information from a user.

[0305] The "information analysis means" is a means for analyzing the user's biological cycle using the acquired biometric state information.

[0306] The "environment synchronization means" is a means for optimizing the environmental situation according to the analyzed biological cycle.

[0307] The "health advice generation means" is a means for generating health advice for the user based on the information analysis result.

[0308] The "e-commerce transaction means" is a means for presenting product information based on health advice and enabling the user to make purchases through electronic payment.

[0309] In the system for implementing this invention, first, wearable devices or smartphones used by the user collect biometric state information. That information is transmitted to the server through the terminal. The data acquired by the information collection means includes heart rate, amount of exercise, sleep pattern, and the like.

[0310] The server analyzes the collected biometric information using information analysis tools. The analysis is performed to identify the user's biological cycle by comparing it with past data. This reveals things like sleep patterns and peak activity times. Furthermore, a health advice generation tool is used to generate specific health advice based on the analysis results, which is then sent to the user's device. This may include suggestions for meal plans and exercise routines.

[0311] In addition, based on the generated health advice, relevant product information is presented to the user via e-commerce. This product information is displayed on the user's personal device in a format that allows for electronic payment. The user can then review the suggested products and easily proceed with the purchase process.

[0312] For example, if a user's lack of exercise is detected from their biometric data, the server may suggest fitness-related products or gym memberships. This allows the user to easily start healthy habits. Another example of a prompt for the generating AI model is: "Generate appropriate product suggestions based on the user's health data. Example: Suggest fitness-related products for a user who is not getting enough exercise."

[0313] The entire system utilizes a data analysis platform for information analysis and health advice generation. Specifically, services such as AWS and Google Cloud are sometimes used as examples. Communication protocols that prioritize security and efficiency are used for sending and receiving information.

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

[0315] Step 1:

[0316] The device acquires biometric information from the user's wearable device or smartphone. This includes data such as heart rate, steps taken, and sleep patterns. It receives biometric data directly from the user's device as input and temporarily stores the data within the device.

[0317] Step 2:

[0318] The device transmits the collected biometric information to the server. Specifically, it packages the collected data using a secure communication protocol and transfers it to the server. The output is biometric data formatted in a format that can be processed by the server.

[0319] Step 3:

[0320] The server analyzes the received biometric data using information analysis tools. It compares the input biometric data with past data to identify the user's biological cycle. The output is the analysis results, such as the user's sleep time and peak activity times.

[0321] Step 4:

[0322] The server uses a health advice generation mechanism based on the analysis results to generate specific health advice. Using the analysis results as input, it utilizes a generation AI model to create health advice tailored to the user. The output includes health advice, meal plans, and exercise suggestions.

[0323] Step 5:

[0324] The server uses e-commerce tools based on health advice to send relevant product information to the user's terminal. Based on the health advice generated as input, it selects product information and creates a suitable offer for the user. The output is a suggestion that includes product details and a purchase link.

[0325] Step 6:

[0326] The user reviews the suggested products displayed on the terminal and makes a purchase via electronic payment if necessary. The user uses product information transmitted from the server as input to complete the purchase process. The output is confirmation information regarding the purchased items.

[0327] 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.

[0328] This invention is a system that supports health management based on the user's biometric data and emotions. Here, we present a specific embodiment that includes an emotion engine.

[0329] First, the device acquires the user's biometric data, such as heart rate, steps taken, and sleep patterns, via a wearable device or smartphone. This data is stored on the device and prepared to be sent to a server later.

[0330] The server receives the collected biometric data and stores it in a database. Data analysis tools analyze the user's biological rhythms and identify lifestyle patterns such as sleep patterns and peak activity times.

[0331] Next, the emotion engine functions. It uses the camera and microphone on the device to collect the user's facial recognition data and voice data, and sends it to the server. The emotion engine analyzes this data to identify the user's emotional state (e.g., joy, sadness, stress).

[0332] The server generates and adjusts health advice based on the identified emotional state. For example, if the user is feeling stressed, it provides advice on breathing exercises or meditation to help them relax. Additionally, an environmental synchronization mechanism sends instructions to the smart device to adjust the environment according to the emotional state. Specifically, this includes changing the lighting to softer light or playing relaxation music to help the user relax.

[0333] For example, if the emotion engine detects that a user is showing signs of stress, the server will generate advice such as, "Try a 5-minute deep breathing session" to relieve stress. At the same time, it will send instructions to the smart device to change the room lighting to a more relaxing tone. This allows the user to enjoy individually optimized health management and a comfortable environment.

[0334] Ultimately, the system improves the accuracy of advice and environment synchronization based on user feedback. By inputting feedback into the terminal about the results of following the advice and the effects experienced, the server accumulates and analyzes information to provide more personalized suggestions, thereby improving the overall accuracy of the system. In this way, a system equipped with an emotion engine realizes comprehensive and sustainable health management that responds to the individual needs of users.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The device synchronizes with the user's wearable device or personal electronic device to collect biometric data such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device for later analysis.

[0338] Step 2:

[0339] The device uses its built-in camera and microphone to collect user facial recognition data and voice data. This data is used to identify the user's emotional state.

[0340] Step 3:

[0341] The device collects biometric and emotion-related data and sends it to a server via the internet. Once the transmission is complete, the system confirms receipt of the data.

[0342] Step 4:

[0343] The server stores the received biometric data and emotion-related data in a database. This creates a history of the user's past and present health and emotional states.

[0344] Step 5:

[0345] The server's data analysis system analyzes biometric data to identify the user's biological rhythm. Simultaneously, the emotion engine processes emotion-related data to determine the user's current emotion.

[0346] Step 6:

[0347] The server generates health advice based on the user's biological rhythms and emotional state. For example, if the user indicates stress, it creates a specific action plan to reduce stress.

[0348] Step 7:

[0349] The server's environment synchronization mechanism identifies the optimal environmental conditions based on the user's emotional state. It then sends instructions to the smart device to adjust lighting, sound, and temperature.

[0350] Step 8:

[0351] The device notifies the user of health advice and environmental adjustment instructions generated by the device. Specific methods and suggestions are provided in an easy-to-understand format.

[0352] Step 9:

[0353] Users follow health advice and, if necessary, input the provided feedback into their device. They provide information about their own impressions and the effectiveness of the advice.

[0354] Step 10:

[0355] The device sends user feedback it collects to a server, which the system uses to analyze and improve the accuracy of health advice and environmental adjustments. The system incorporates user feedback to make future suggestions more personalized.

[0356] (Example 2)

[0357] 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".

[0358] In modern society, maintaining users' physical and mental health requires precise health management tailored to individual lifestyles and emotional states. However, conventional systems are limited to simple health advice based solely on biometric information, making comprehensive health management that considers the user's emotional state and living environment difficult.

[0359] 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.

[0360] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's lifestyle patterns based on the biometric information, and emotion analysis means for identifying the user's emotional state. This enables precise and comprehensive health management tailored to the user's individual health condition.

[0361] "Information gathering means" refers to a device or system designed to acquire biometric information from a user.

[0362] "Information analysis means" refers to a device or system for analyzing a user's lifestyle patterns based on collected biometric information.

[0363] An "advice generation means" is a device or system for generating health advice based on analysis results and the user's emotional state.

[0364] "Emotional analysis tools" refer to devices or systems designed to identify a user's emotional state.

[0365] A "harmonization tool" is a device or system for adjusting environmental conditions based on analysis results and emotional states.

[0366] A "personal information device" is a device or equipment used by a user individually for collecting information.

[0367] A "household device" is a device or system used to adjust environmental conditions within a household.

[0368] This system provides personalized health advice and environmental adjustments based on the user's biometric information and emotional state. First, the device acquires biometric information such as heart rate, steps taken, and sleep patterns via personal information devices like wearable devices and smartphones. To achieve this, the device periodically communicates with these devices using Bluetooth or Wi-Fi. The acquired data is temporarily stored within the device.

[0369] Subsequently, the device transmits biometric information to a server using a secure communication protocol. The server stores the received information in a database and uses data analysis tools to identify the user's lifestyle patterns. This analysis may involve machine learning algorithms using Python or similar data processing languages.

[0370] Furthermore, the device uses its built-in camera and microphone to collect user facial recognition data and voice data. This information is sent to a server, which uses sentiment analysis tools to identify the user's emotional state. In this process, image processing and machine learning libraries such as OpenCV and TensorFlow are often used.

[0371] Based on analyzed lifestyle patterns and emotional states, the server generates optimal health advice for the user using an advice generation system. For example, if the user is identified as being in a high-stress state, advice such as "Try a 5-minute deep breathing session" might be provided. This advice is generated based on a generative AI model.

[0372] Furthermore, the server uses a harmonization mechanism to send environmental adjustment instructions to the corresponding home devices. For example, an instruction is sent to a smart device to change the room lighting to a warmer color to promote relaxation.

[0373] This system allows users to experience personalized health management and a comfortable environment. For example, if the emotional analysis detects signs of stress in the user, the server can generate advice such as, "To alleviate stress levels, start with deep breathing," and instruct the user to change the room lighting to calming colors. Through this entire process, the user's health is optimized.

[0374] An example of a prompt message might be: "Measure the user's current stress level and suggest appropriate health advice and environmental adjustments based on that level."

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The device acquires the user's biometric information through wearable devices and smartphones. It uses Bluetooth or Wi-Fi communication to collect heart rate, steps, and sleep patterns from the devices. The collected data is temporarily stored within the device. The input is biometric information from the wearable device, and the output is biometric information stored within the device.

[0378] Step 2:

[0379] The device sends the stored biometric information to the server. A secure protocol such as HTTPS is used for this transmission. The server stores the received data in a database. The input is the biometric information stored on the device, and the output is the biometric information stored in the database.

[0380] Step 3:

[0381] The server uses biometric information stored in the database to identify the user's lifestyle patterns through information analysis. Machine learning algorithms using programming languages ​​such as Python are used to calculate sleep times and peak activity times. The input is biometric information from the database, and the output is the analyzed lifestyle pattern data.

[0382] Step 4:

[0383] The device collects user facial recognition and voice data via its camera and microphone. This collected data is sent to a server. The input is the user's face and voice data, and the output is the facial recognition and voice data sent to the server.

[0384] Step 5:

[0385] The server uses emotion analysis tools to identify the user's emotional state from received facial recognition and voice data. Libraries such as OpenCV and TensorFlow are used to determine emotions like joy and stress. The input is the facial recognition and voice data sent to the server, and the output is the identified emotional state data.

[0386] Step 6:

[0387] The server generates health advice based on analyzed lifestyle pattern data and emotional state data using an advice generation mechanism. A generation AI model is utilized to create, for example, instructions for stress reduction. The input is lifestyle pattern data and emotional state data, and the output is health advice provided to the user.

[0388] Step 7:

[0389] The server, using a harmonization mechanism, transmits environmental adjustment instructions to household devices, along with the generated health advice. It sends instructions to devices such as smart light bulbs and audio equipment to adjust lighting or play music. The input is the generated health advice, and the output is the environmental adjustment instructions to the household devices.

[0390] Step 8:

[0391] Users input feedback into their terminals based on the advice and adjustments provided. This feedback is collected by the server and used to improve the system's accuracy. The input is user feedback, and the output is feedback data stored on the server.

[0392] (Application Example 2)

[0393] 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."

[0394] In traditional brick-and-mortar stores, it is difficult to provide individualized services that take into account each customer's emotional state and health condition. Furthermore, there is a lack of means to dynamically adjust the store environment according to individual needs, making it difficult to provide a shopping experience optimized for each customer.

[0395] 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.

[0396] In this invention, the server includes means for acquiring biometric data, means for identifying emotional states and generating product suggestions, and means for transmitting instructions to facility equipment and adjusting the environment. This enables optimized product suggestions and environmental adjustments based on the biometric data and emotional states of customers.

[0397] "Data collection means" refers to a device or method that includes a function for acquiring a user's biometric data.

[0398] "Data analysis means" refers to a device or method for analyzing acquired biometric data to identify the user's biological rhythm and emotional state.

[0399] "Environmental synchronization means" refers to a device or method for optimizing environmental conditions in accordance with the user's biological rhythm.

[0400] "Health advice generation means" refers to a device or method for generating health advice for a user based on analysis results.

[0401] "Product suggestion generation means" refers to a device or method for identifying the emotional state of a customer visiting a store and generating product suggestions based on that state.

[0402] "Environmental adjustment instruction means" refers to a device or method that generates instructions for adjusting the environment inside a store and transmits them to the facilities and equipment for carrying them out.

[0403] Modes for carrying out the invention

[0404] The system that realizes this invention is implemented using the following hardware and software. First, the terminal collects necessary data (heart rate, steps, sleep patterns, etc.) via a wearable device or smart terminal to acquire the user's biometric data. This information is stored on the terminal and sent to a server for processing.

[0405] The server stores the received biometric data in a database and analyzes the user's biological rhythm using data analysis tools (e.g., Apache Hadoop). The analysis results are used to generate health advice tailored to the user through a health advice generation system. To identify the user's emotional state, the emotion engine collects facial recognition data and voice data using the camera and microphone installed on the device and requests analysis from the server.

[0406] Furthermore, the product suggestion generation means proposes the most suitable product based on the customer's identified emotional state. This suggestion is presented through a smart device installed in the store, such as a smart glasses display, while the environment adjustment instruction means sends instructions to the facility equipment to adjust, for example, the store's lighting and music to suit the customer's emotional state.

[0407] As an example, if the system determines that a particular customer is experiencing stress, it will display suggestions for relaxing products on their glasses and change the store's lighting to a softer tone. The suggested products could include aromatherapy oils effective in reducing stress.

[0408] An example of a prompt message could be: "Based on the user's latest emotional state data, suggest products that promote relaxation. Also, change the store lighting to a warmer color." This prompt message is used as input to recommend activities optimized for the user, utilizing a generative AI model.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The device collects user biometric data from wearable devices and smart devices. Inputs include heart rate, steps taken, and sleep patterns. This data is stored locally and later prepared for transmission to a server. Data collection is performed in real time, and the data is updated at regular intervals.

[0412] Step 2:

[0413] The terminal transmits the collected biometric data to the server. The input is the biometric data acquired in step 1, and the output is the data sent to the server, which is then stored in the database. Data communication is encrypted to ensure security.

[0414] Step 3:

[0415] The server analyzes the user's biological rhythms using data analysis tools based on biometric data stored in the database. The input is biometric data in the database, and the output is the user's lifestyle patterns, such as sleep times and peak activity times. Machine learning algorithms are used for data analysis to identify individual rhythms.

[0416] Step 4:

[0417] The device uses its camera and microphone to collect user facial recognition and voice data. This serves as input, and the collected data is sent to a server to be prepared for analysis as emotion data. The data is captured in real time based on facial expressions and voice tone.

[0418] Step 5:

[0419] The server uses an emotion engine to analyze facial recognition and voice data to identify the user's emotional state. Based on the facial recognition and voice data received as input, it identifies emotional states such as joy, sadness, and stress as output. The analysis results are dynamically updated to ensure that the most up-to-date emotional state is reflected in real time.

[0420] Step 6:

[0421] The server uses a generative AI model to input prompts based on identified emotional states, generating health advice and product suggestions. The input is the user's emotional state, and the output is health advice such as "Try a 5-minute deep breathing session" or suggestions for relaxing products.

[0422] Step 7:

[0423] The server generates and sends instructions to the facility's equipment for environmental adjustments. The input is a generated prompt message, and the output includes instructions for environmental settings such as adjusting the color of the store's lighting and selecting and playing music. This optimizes the user's shopping experience.

[0424] Step 8:

[0425] Users conduct an experience based on advice from the server and input feedback into their terminal. The input is data about the user's experience, and the entire system uses this feedback to improve its accuracy as output. The improved data is reflected in subsequent analyses and suggestions.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Third Embodiment]

[0430] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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".

[0442] This invention is a system that supports health management using the user's biometric data. Specific embodiments are described below.

[0443] First, the device synchronizes with wearable devices and smartphones to collect the user's biometric data, such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and periodically sent to a server.

[0444] The server stores the received biometric data in a database and performs analysis. Specifically, it compares it with past data to identify the user's biological rhythm. In this process, it calculates the user's sleep time, wake time, peak activity times, etc., and identifies the patterns associated with them.

[0445] Next, the environmental synchronization function is activated. Based on the identified biological rhythm, the server sends instructions to the smart device to adjust the user's living environment. For example, it might change the lighting to a warmer color or adjust the air conditioner to match bedtime. At this stage, it may also suggest playing ambient sounds to promote mental relaxation.

[0446] Subsequently, the health advice generation system takes over. Based on the analysis results, the server creates specific advice to encourage users to improve their lifestyle. This advice covers a wide range of topics, from meal plans and exercise suggestions to stress management methods. This information is then sent to the device as a notification.

[0447] For example, if data is collected showing that a user goes to bed at a different time each day, the server will identify the user's poor sleep patterns and generate advice such as, "Your sleep quality will improve if you make it a habit to go to bed at the same time every day." It will also send environmental settings to the smart device, such as automatically changing the lighting to a warmer color one hour before bedtime. In this way, users can receive personalized health management and a comfortable environment.

[0448] Furthermore, the system uses user feedback to verify the effectiveness of its advice and make necessary adjustments. This two-way interaction is designed to enable long-term and sustainable health management.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The device acquires biometric data from the wearable device, such as the user's heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and then formatted for later transmission to the server.

[0452] Step 2:

[0453] The device collects biometric data at regular intervals and sends it to a server via the internet. During this process, the system verifies the success of the data transmission and attempts to resend it if there are any problems. It also displays a notification to the user upon successful transmission.

[0454] Step 3:

[0455] The server stores the received biometric data in a database. The data is saved along with date and time information, forming a dataset ready for future analysis.

[0456] Step 4:

[0457] The server begins data analysis, analyzing biometric data to identify the user's biological rhythms. This analysis identifies lifestyle patterns such as bedtime, wake-up time, and most active periods.

[0458] Step 5:

[0459] The server generates environmental synchronization instructions based on the analysis results. It determines lighting and temperature settings that match the user's biological rhythm and sends instructions to the smart device to execute these settings.

[0460] Step 6:

[0461] The server generates health advice. Considering the data analysis results, it creates specific lifestyle improvement suggestions for the user. This advice includes recommendations regarding diet, exercise, and stress management.

[0462] Step 7:

[0463] The device notifies the user of generated advice and setting instructions. To facilitate user implementation of these suggestions, it provides reminder and calendar integration features.

[0464] Step 8:

[0465] The user enters feedback on the advice provided into the device. This feedback includes comments on the feasibility and effectiveness of the suggestions.

[0466] Step 9:

[0467] The device collects user feedback and sends it to the server. This feedback information is used to generate further personalized advice.

[0468] Step 10:

[0469] The server analyzes feedback and adjusts advice and settings as needed. This ensures that users continuously receive more appropriate health management.

[0470] (Example 1)

[0471] 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."

[0472] In recent years, the importance of personal health management has increased, but traditional methods have difficulty providing timely and effective advice tailored to individual health conditions. Furthermore, while there is a growing demand for personalized health management that matches users' lifestyles and living environments, existing systems are not adequately supporting this.

[0473] 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.

[0474] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's biometric pattern based on the biometric information, and environmental synchronization means for adjusting environmental conditions according to the biometric pattern. This makes it possible to personalize and quickly and effectively provide appropriate advice tailored to the individual's health condition.

[0475] "Information gathering means" refers to devices or methods that have the function of acquiring biometric information from a user and transmitting it to other components within the system.

[0476] "Information analysis means" refers to devices or programs that calculate a user's biological pattern based on acquired biological information, analyze it, and then diagnose and evaluate their health status.

[0477] An "environmental synchronization means" is a mechanism that issues commands to adjust the user's living environment to an appropriate state based on the analysis results, and causes the target device to execute those commands.

[0478] A "proposal generation means" refers to a method or device for generating and effectively presenting lifestyle improvement suggestions tailored to the user's health condition and lifestyle.

[0479] A "feedback adjustment mechanism" is a system that improves the overall accuracy of the system by receiving feedback from users and adjusting and updating the suggested content and environment settings based on that feedback.

[0480] This invention is a system that supports health management based on the user's biometric information. The following describes embodiments for carrying out the invention.

[0481] First, the device synchronizes with wearable devices and smartphones. Specifically, it uses communication protocols such as Bluetooth and Wi-Fi to collect biometric information such as the user's heart rate, steps taken, and sleep patterns, and temporarily stores it in its internal memory.

[0482] Next, the server receives biometric information transmitted from the terminal at regular intervals. For security reasons, the SSL / TLS protocol is used for this communication. The received data is stored in a relational database (e.g., MySQL or PostgreSQL).

[0483] The server analyzes stored biometric data to identify the user's biometric patterns. This analysis employs machine learning algorithms using Python libraries (such as scikit-learn and pandas). The analysis clarifies the user's sleep and wake times, as well as their peak activity times.

[0484] The server then sends instructions to the smart device based on the analysis results. These instructions may include adjusting the color temperature of the lighting or changing the settings of the air conditioner to optimize the environment. This can be done using an API for controlling home appliances (e.g., a smart home API).

[0485] In addition, the server uses a generative AI model to generate suggestions for improving lifestyle habits. These suggestions include meal plans, exercise methods, and specific advice for stress management, which are sent to the user's device as notifications. This allows the user to receive effective advice tailored to their own health condition.

[0486] A concrete example of its use would be providing advice based on user data, such as, "Incorporating regular exercise will help maintain your health." An example of a prompt would be, "Generate and output appropriate health advice based on the user's data."

[0487] Ultimately, users provide feedback on the effectiveness of the suggestions, allowing the system to continuously evolve and provide more accurate advice and environmental adjustments. This enables long-term and sustainable health management.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The device synchronizes with wearable devices and smartphones to collect the user's biometric information. It acquires data such as heart rate, steps, and sleep patterns as input, and temporarily stores this data in its internal memory. The collected data is obtained as electrical signals and converted into digital data.

[0491] Step 2:

[0492] The device sends biometric information collected at regular intervals to the server. The input is a set of multiple biometric data stored in memory, and the output is an encrypted data packet. This packet is transmitted securely using the SSL / TLS protocol.

[0493] Step 3:

[0494] The server decrypts the received data packets and stores the biometric information in a database. The input is encrypted data packets, and the output is a record of the biometric information in the database. The data is written to a relational database such as MySQL or PostgreSQL.

[0495] Step 4:

[0496] The server analyzes stored biometric information to identify the user's biometric patterns. The input is historical data from a database, and the output is rhythmic information such as the user's sleep and wake times and peak activity times. This process utilizes machine learning algorithms to analyze data trends.

[0497] Step 5:

[0498] The server generates environmental adjustment instructions based on identified biometric patterns and sends them to the smart device. The input is rhythm information, and the output is a message with environmental setting instructions. This allows lighting and air conditioning to be automatically adjusted to match the user's rhythm.

[0499] Step 6:

[0500] The server uses an AI model to generate specific health suggestions based on the user's biometric information and identified rhythms. Rhythm information and health data are used as input, and the output includes suggestions for diet and exercise, as well as stress management advice. These suggestions are then notified to the user's device.

[0501] Step 7:

[0502] Users implement the provided advice and provide feedback to the system about its effectiveness. Input consists of the user's personal impressions and data, while output is the server-side recording of the feedback and the resulting system adjustments. The advice may be updated based on the feedback.

[0503] (Application Example 1)

[0504] 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."

[0505] In modern society, there is a need to efficiently manage users' health while simultaneously offering product recommendations that meet their needs. However, conventional health management systems only analyze users' biometric data and provide health advice, lacking concrete product recommendations or connections to purchasing behavior. Therefore, a more consistent integration of health management and product purchase is necessary.

[0506] 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.

[0507] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's biological cycle based on the biometric information, health advice generation means for generating health advice based on the analysis results, and electronic commerce means for presenting product information based on the health advice and facilitating purchases through electronic payment. This makes it possible to support the user's health improvement in their daily life while effectively and efficiently encouraging the purchase of related products.

[0508] "Information gathering means" refers to means of obtaining biometric information from a user.

[0509] "Information analysis means" refers to means for analyzing the user's biological cycle using acquired biological state information.

[0510] "Environmental synchronization means" are means for optimizing environmental conditions in accordance with the analyzed biological cycle.

[0511] A "health advice generation method" is a means for generating health advice for a user based on the results of information analysis.

[0512] "Electronic commerce methods" refer to means of presenting product information based on health advice and enabling users to make purchases through electronic payment.

[0513] In the system implementing this invention, a wearable device or smartphone used by the user first collects biometric information. This information is then transmitted to a server via the terminal. The data acquired by the information collection means includes heart rate, exercise level, sleep patterns, and so on.

[0514] The server analyzes the collected biometric information using information analysis tools. The analysis is performed to identify the user's biological cycle by comparing it with past data. This reveals things like sleep patterns and peak activity times. Furthermore, a health advice generation tool is used to generate specific health advice based on the analysis results, which is then sent to the user's device. This may include suggestions for meal plans and exercise routines.

[0515] In addition, based on the generated health advice, relevant product information is presented to the user via e-commerce. This product information is displayed on the user's personal device in a format that allows for electronic payment. The user can then review the suggested products and easily proceed with the purchase process.

[0516] For example, if a user's lack of exercise is detected from their biometric data, the server may suggest fitness-related products or gym memberships. This allows the user to easily start healthy habits. Another example of a prompt for the generating AI model is: "Generate appropriate product suggestions based on the user's health data. Example: Suggest fitness-related products for a user who is not getting enough exercise."

[0517] The entire system utilizes a data analysis platform for information analysis and health advice generation. Specifically, services such as AWS and Google Cloud are sometimes used as examples. Communication protocols that prioritize security and efficiency are used for sending and receiving information.

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] The device acquires biometric information from the user's wearable device or smartphone. This includes data such as heart rate, steps taken, and sleep patterns. It receives biometric data directly from the user's device as input and temporarily stores the data within the device.

[0521] Step 2:

[0522] The device transmits the collected biometric information to the server. Specifically, it packages the collected data using a secure communication protocol and transfers it to the server. The output is biometric data formatted in a format that can be processed by the server.

[0523] Step 3:

[0524] The server analyzes the received biometric data using information analysis tools. It compares the input biometric data with past data to identify the user's biological cycle. The output is the analysis results, such as the user's sleep time and peak activity times.

[0525] Step 4:

[0526] The server uses a health advice generation mechanism based on the analysis results to generate specific health advice. Using the analysis results as input, it utilizes a generation AI model to create health advice tailored to the user. The output includes health advice, meal plans, and exercise suggestions.

[0527] Step 5:

[0528] The server uses e-commerce tools based on health advice to send relevant product information to the user's terminal. Based on the health advice generated as input, it selects product information and creates a suitable offer for the user. The output is a suggestion that includes product details and a purchase link.

[0529] Step 6:

[0530] The user reviews the suggested products displayed on the terminal and makes a purchase via electronic payment if necessary. The user uses product information transmitted from the server as input to complete the purchase process. The output is confirmation information regarding the purchased items.

[0531] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0532] This invention is a system that supports health management based on the user's biometric data and emotions. Here, we present a specific embodiment that includes an emotion engine.

[0533] First, the device acquires the user's biometric data, such as heart rate, steps taken, and sleep patterns, via a wearable device or smartphone. This data is stored on the device and prepared to be sent to a server later.

[0534] The server receives the collected biometric data and stores it in a database. Data analysis tools analyze the user's biological rhythms and identify lifestyle patterns such as sleep patterns and peak activity times.

[0535] Next, the emotion engine functions. It uses the camera and microphone on the device to collect the user's facial recognition data and voice data, and sends it to the server. The emotion engine analyzes this data to identify the user's emotional state (e.g., joy, sadness, stress).

[0536] The server generates and adjusts health advice based on the identified emotional state. For example, if the user is feeling stressed, it provides advice on breathing exercises or meditation to help them relax. Additionally, an environmental synchronization mechanism sends instructions to the smart device to adjust the environment according to the emotional state. Specifically, this includes changing the lighting to softer light or playing relaxation music to help the user relax.

[0537] For example, if the emotion engine detects that a user is showing signs of stress, the server will generate advice such as, "Try a 5-minute deep breathing session" to relieve stress. At the same time, it will send instructions to the smart device to change the room lighting to a more relaxing tone. This allows the user to enjoy individually optimized health management and a comfortable environment.

[0538] Ultimately, the system improves the accuracy of advice and environment synchronization based on user feedback. By inputting feedback into the terminal about the results of following the advice and the effects experienced, the server accumulates and analyzes information to provide more personalized suggestions, thereby improving the overall accuracy of the system. In this way, a system equipped with an emotion engine realizes comprehensive and sustainable health management that responds to the individual needs of users.

[0539] The following describes the processing flow.

[0540] Step 1:

[0541] The device synchronizes with the user's wearable device or personal electronic device to collect biometric data such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device for later analysis.

[0542] Step 2:

[0543] The device uses its built-in camera and microphone to collect user facial recognition data and voice data. This data is used to identify the user's emotional state.

[0544] Step 3:

[0545] The device collects biometric and emotion-related data and sends it to a server via the internet. Once the transmission is complete, the system confirms receipt of the data.

[0546] Step 4:

[0547] The server stores the received biometric data and emotion-related data in a database. This creates a history of the user's past and present health and emotional states.

[0548] Step 5:

[0549] The server's data analysis system analyzes biometric data to identify the user's biological rhythm. Simultaneously, the emotion engine processes emotion-related data to determine the user's current emotion.

[0550] Step 6:

[0551] The server generates health advice based on the user's biological rhythms and emotional state. For example, if the user indicates stress, it creates a specific action plan to reduce stress.

[0552] Step 7:

[0553] The server's environment synchronization mechanism identifies the optimal environmental conditions based on the user's emotional state. It then sends instructions to the smart device to adjust lighting, sound, and temperature.

[0554] Step 8:

[0555] The device notifies the user of health advice and environmental adjustment instructions generated by the device. Specific methods and suggestions are provided in an easy-to-understand format.

[0556] Step 9:

[0557] Users follow health advice and, if necessary, input the provided feedback into their device. They provide information about their own impressions and the effectiveness of the advice.

[0558] Step 10:

[0559] The device sends user feedback it collects to a server, which the system uses to analyze and improve the accuracy of health advice and environmental adjustments. The system incorporates user feedback to make future suggestions more personalized.

[0560] (Example 2)

[0561] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0562] In modern society, maintaining users' physical and mental health requires precise health management tailored to individual lifestyles and emotional states. However, conventional systems are limited to simple health advice based solely on biometric information, making comprehensive health management that considers the user's emotional state and living environment difficult.

[0563] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0564] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's lifestyle patterns based on the biometric information, and emotion analysis means for identifying the user's emotional state. This enables precise and comprehensive health management tailored to the user's individual health condition.

[0565] "Information gathering means" refers to a device or system designed to acquire biometric information from a user.

[0566] "Information analysis means" refers to a device or system for analyzing a user's lifestyle patterns based on collected biometric information.

[0567] An "advice generation means" is a device or system for generating health advice based on analysis results and the user's emotional state.

[0568] "Emotional analysis tools" refer to devices or systems designed to identify a user's emotional state.

[0569] A "harmonization tool" is a device or system for adjusting environmental conditions based on analysis results and emotional states.

[0570] A "personal information device" is a device or equipment used by a user individually for collecting information.

[0571] A "household device" is a device or system used to adjust environmental conditions within a household.

[0572] This system provides personalized health advice and environmental adjustments based on the user's biometric information and emotional state. First, the device acquires biometric information such as heart rate, steps taken, and sleep patterns via personal information devices like wearable devices and smartphones. To achieve this, the device periodically communicates with these devices using Bluetooth or Wi-Fi. The acquired data is temporarily stored within the device.

[0573] Subsequently, the device transmits biometric information to a server using a secure communication protocol. The server stores the received information in a database and uses data analysis tools to identify the user's lifestyle patterns. This analysis may involve machine learning algorithms using Python or similar data processing languages.

[0574] Furthermore, the device uses its built-in camera and microphone to collect user facial recognition data and voice data. This information is sent to a server, which uses sentiment analysis tools to identify the user's emotional state. In this process, image processing and machine learning libraries such as OpenCV and TensorFlow are often used.

[0575] Based on analyzed lifestyle patterns and emotional states, the server generates optimal health advice for the user using an advice generation system. For example, if the user is identified as being in a high-stress state, advice such as "Try a 5-minute deep breathing session" might be provided. This advice is generated based on a generative AI model.

[0576] Furthermore, the server uses a harmonization mechanism to send environmental adjustment instructions to the corresponding home devices. For example, an instruction is sent to a smart device to change the room lighting to a warmer color to promote relaxation.

[0577] This system allows users to experience personalized health management and a comfortable environment. For example, if the emotional analysis detects signs of stress in the user, the server can generate advice such as, "To alleviate stress levels, start with deep breathing," and instruct the user to change the room lighting to calming colors. Through this entire process, the user's health is optimized.

[0578] An example of a prompt message might be: "Measure the user's current stress level and suggest appropriate health advice and environmental adjustments based on that level."

[0579] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0580] Step 1:

[0581] The device acquires the user's biometric information through wearable devices and smartphones. It uses Bluetooth or Wi-Fi communication to collect heart rate, steps, and sleep patterns from the devices. The collected data is temporarily stored within the device. The input is biometric information from the wearable device, and the output is biometric information stored within the device.

[0582] Step 2:

[0583] The device sends the stored biometric information to the server. A secure protocol such as HTTPS is used for this transmission. The server stores the received data in a database. The input is the biometric information stored on the device, and the output is the biometric information stored in the database.

[0584] Step 3:

[0585] The server uses biometric information stored in the database to identify the user's lifestyle patterns through information analysis. Machine learning algorithms using programming languages ​​such as Python are used to calculate sleep times and peak activity times. The input is biometric information from the database, and the output is the analyzed lifestyle pattern data.

[0586] Step 4:

[0587] The device collects user facial recognition and voice data via its camera and microphone. This collected data is sent to a server. The input is the user's face and voice data, and the output is the facial recognition and voice data sent to the server.

[0588] Step 5:

[0589] The server uses emotion analysis tools to identify the user's emotional state from received facial recognition and voice data. Libraries such as OpenCV and TensorFlow are used to determine emotions like joy and stress. The input is the facial recognition and voice data sent to the server, and the output is the identified emotional state data.

[0590] Step 6:

[0591] The server generates health advice based on analyzed lifestyle pattern data and emotional state data using an advice generation mechanism. A generation AI model is utilized to create, for example, instructions for stress reduction. The input is lifestyle pattern data and emotional state data, and the output is health advice provided to the user.

[0592] Step 7:

[0593] The server, using a harmonization mechanism, transmits environmental adjustment instructions to household devices, along with the generated health advice. It sends instructions to devices such as smart light bulbs and audio equipment to adjust lighting or play music. The input is the generated health advice, and the output is the environmental adjustment instructions to the household devices.

[0594] Step 8:

[0595] Users input feedback into their terminals based on the advice and adjustments provided. This feedback is collected by the server and used to improve the system's accuracy. The input is user feedback, and the output is feedback data stored on the server.

[0596] (Application Example 2)

[0597] 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."

[0598] In traditional brick-and-mortar stores, it is difficult to provide individualized services that take into account each customer's emotional state and health condition. Furthermore, there is a lack of means to dynamically adjust the store environment according to individual needs, making it difficult to provide a shopping experience optimized for each customer.

[0599] 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.

[0600] In this invention, the server includes means for acquiring biometric data, means for identifying emotional states and generating product suggestions, and means for transmitting instructions to facility equipment and adjusting the environment. This enables optimized product suggestions and environmental adjustments based on the biometric data and emotional states of customers.

[0601] "Data collection means" refers to a device or method that includes a function for acquiring a user's biometric data.

[0602] "Data analysis means" refers to a device or method for analyzing acquired biometric data to identify the user's biological rhythm and emotional state.

[0603] "Environmental synchronization means" refers to a device or method for optimizing environmental conditions in accordance with the user's biological rhythm.

[0604] "Health advice generation means" refers to a device or method for generating health advice for a user based on analysis results.

[0605] "Product suggestion generation means" refers to a device or method for identifying the emotional state of a customer visiting a store and generating product suggestions based on that state.

[0606] "Environmental adjustment instruction means" refers to a device or method that generates instructions for adjusting the environment inside a store and transmits them to the facilities and equipment for carrying them out.

[0607] Modes for carrying out the invention

[0608] The system that realizes this invention is implemented using the following hardware and software. First, the terminal collects necessary data (heart rate, steps, sleep patterns, etc.) via a wearable device or smart terminal to acquire the user's biometric data. This information is stored on the terminal and sent to a server for processing.

[0609] The server stores the received biometric data in a database and analyzes the user's biological rhythm using data analysis tools (e.g., Apache Hadoop). The analysis results are used to generate health advice tailored to the user through a health advice generation system. To identify the user's emotional state, the emotion engine collects facial recognition data and voice data using the camera and microphone installed on the device and requests analysis from the server.

[0610] Furthermore, the product suggestion generation means proposes the most suitable product based on the customer's identified emotional state. This suggestion is presented through a smart device installed in the store, such as a smart glasses display, while the environment adjustment instruction means sends instructions to the facility equipment to adjust, for example, the store's lighting and music to suit the customer's emotional state.

[0611] As an example, if the system determines that a particular customer is experiencing stress, it will display suggestions for relaxing products on their glasses and change the store's lighting to a softer tone. The suggested products could include aromatherapy oils effective in reducing stress.

[0612] An example of a prompt message could be: "Based on the user's latest emotional state data, suggest products that promote relaxation. Also, change the store lighting to a warmer color." This prompt message is used as input to recommend activities optimized for the user, utilizing a generative AI model.

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The device collects user biometric data from wearable devices and smart devices. Inputs include heart rate, steps taken, and sleep patterns. This data is stored locally and later prepared for transmission to a server. Data collection is performed in real time, and the data is updated at regular intervals.

[0616] Step 2:

[0617] The terminal transmits the collected biometric data to the server. The input is the biometric data acquired in step 1, and the output is the data sent to the server, which is then stored in the database. Data communication is encrypted to ensure security.

[0618] Step 3:

[0619] The server analyzes the user's biological rhythms using data analysis tools based on biometric data stored in the database. The input is biometric data in the database, and the output is the user's lifestyle patterns, such as sleep times and peak activity times. Machine learning algorithms are used for data analysis to identify individual rhythms.

[0620] Step 4:

[0621] The device uses its camera and microphone to collect user facial recognition and voice data. This serves as input, and the collected data is sent to a server to be prepared for analysis as emotion data. The data is captured in real time based on facial expressions and voice tone.

[0622] Step 5:

[0623] The server uses an emotion engine to analyze facial recognition and voice data to identify the user's emotional state. Based on the facial recognition and voice data received as input, it identifies emotional states such as joy, sadness, and stress as output. The analysis results are dynamically updated to ensure that the most up-to-date emotional state is reflected in real time.

[0624] Step 6:

[0625] The server uses a generative AI model to input prompts based on identified emotional states, generating health advice and product suggestions. The input is the user's emotional state, and the output is health advice such as "Try a 5-minute deep breathing session" or suggestions for relaxing products.

[0626] Step 7:

[0627] The server generates and sends instructions to the facility's equipment for environmental adjustments. The input is a generated prompt message, and the output includes instructions for environmental settings such as adjusting the color of the store's lighting and selecting and playing music. This optimizes the user's shopping experience.

[0628] Step 8:

[0629] Users conduct an experience based on advice from the server and input feedback into their terminal. The input is data about the user's experience, and the entire system uses this feedback to improve its accuracy as output. The improved data is reflected in subsequent analyses and suggestions.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] [Fourth Embodiment]

[0634] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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".

[0647] This invention is a system that supports health management using the user's biometric data. Specific embodiments are described below.

[0648] First, the device synchronizes with wearable devices and smartphones to collect the user's biometric data, such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and periodically sent to a server.

[0649] The server stores the received biometric data in a database and performs analysis. Specifically, it compares it with past data to identify the user's biological rhythm. In this process, it calculates the user's sleep time, wake time, peak activity times, etc., and identifies the patterns associated with them.

[0650] Next, the environmental synchronization function is activated. Based on the identified biological rhythm, the server sends instructions to the smart device to adjust the user's living environment. For example, it might change the lighting to a warmer color or adjust the air conditioner to match bedtime. At this stage, it may also suggest playing ambient sounds to promote mental relaxation.

[0651] Subsequently, the health advice generation system takes over. Based on the analysis results, the server creates specific advice to encourage users to improve their lifestyle. This advice covers a wide range of topics, from meal plans and exercise suggestions to stress management methods. This information is then sent to the device as a notification.

[0652] For example, if data is collected showing that a user goes to bed at a different time each day, the server will identify the user's poor sleep patterns and generate advice such as, "Your sleep quality will improve if you make it a habit to go to bed at the same time every day." It will also send environmental settings to the smart device, such as automatically changing the lighting to a warmer color one hour before bedtime. In this way, users can receive personalized health management and a comfortable environment.

[0653] Furthermore, the system uses user feedback to verify the effectiveness of its advice and make necessary adjustments. This two-way interaction is designed to enable long-term and sustainable health management.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The device acquires biometric data from the wearable device, such as the user's heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device and then formatted for later transmission to the server.

[0657] Step 2:

[0658] The device collects biometric data at regular intervals and sends it to a server via the internet. During this process, the system verifies the success of the data transmission and attempts to resend it if there are any problems. It also displays a notification to the user upon successful transmission.

[0659] Step 3:

[0660] The server stores the received biometric data in a database. The data is saved along with date and time information, forming a dataset ready for future analysis.

[0661] Step 4:

[0662] The server begins data analysis, analyzing biometric data to identify the user's biological rhythms. This analysis identifies lifestyle patterns such as bedtime, wake-up time, and most active periods.

[0663] Step 5:

[0664] The server generates environmental synchronization instructions based on the analysis results. It determines lighting and temperature settings that match the user's biological rhythm and sends instructions to the smart device to execute these settings.

[0665] Step 6:

[0666] The server generates health advice. Considering the data analysis results, it creates specific lifestyle improvement suggestions for the user. This advice includes recommendations regarding diet, exercise, and stress management.

[0667] Step 7:

[0668] The device notifies the user of generated advice and setting instructions. To facilitate user implementation of these suggestions, it provides reminder and calendar integration features.

[0669] Step 8:

[0670] The user enters feedback on the advice provided into the device. This feedback includes comments on the feasibility and effectiveness of the suggestions.

[0671] Step 9:

[0672] The device collects user feedback and sends it to the server. This feedback information is used to generate further personalized advice.

[0673] Step 10:

[0674] The server analyzes feedback and adjusts advice and settings as needed. This ensures that users continuously receive more appropriate health management.

[0675] (Example 1)

[0676] 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".

[0677] In recent years, the importance of personal health management has increased, but traditional methods have difficulty providing timely and effective advice tailored to individual health conditions. Furthermore, while there is a growing demand for personalized health management that matches users' lifestyles and living environments, existing systems are not adequately supporting this.

[0678] 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.

[0679] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's biometric pattern based on the biometric information, and environmental synchronization means for adjusting environmental conditions according to the biometric pattern. This makes it possible to personalize and quickly and effectively provide appropriate advice tailored to the individual's health condition.

[0680] "Information gathering means" refers to devices or methods that have the function of acquiring biometric information from a user and transmitting it to other components within the system.

[0681] "Information analysis means" refers to devices or programs that calculate a user's biological pattern based on acquired biological information, analyze it, and then diagnose and evaluate their health status.

[0682] An "environmental synchronization means" is a mechanism that issues commands to adjust the user's living environment to an appropriate state based on the analysis results, and causes the target device to execute those commands.

[0683] A "proposal generation means" refers to a method or device for generating and effectively presenting lifestyle improvement suggestions tailored to the user's health condition and lifestyle.

[0684] A "feedback adjustment mechanism" is a system that improves the overall accuracy of the system by receiving feedback from users and adjusting and updating the suggested content and environment settings based on that feedback.

[0685] This invention is a system that supports health management based on the user's biometric information. The following describes embodiments for carrying out the invention.

[0686] First, the device synchronizes with wearable devices and smartphones. Specifically, it uses communication protocols such as Bluetooth and Wi-Fi to collect biometric information such as the user's heart rate, steps taken, and sleep patterns, and temporarily stores it in its internal memory.

[0687] Next, the server receives biometric information transmitted from the terminal at regular intervals. For security reasons, the SSL / TLS protocol is used for this communication. The received data is stored in a relational database (e.g., MySQL or PostgreSQL).

[0688] The server analyzes stored biometric data to identify the user's biometric patterns. This analysis employs machine learning algorithms using Python libraries (such as scikit-learn and pandas). The analysis clarifies the user's sleep and wake times, as well as their peak activity times.

[0689] The server then sends instructions to the smart device based on the analysis results. These instructions may include adjusting the color temperature of the lighting or changing the settings of the air conditioner to optimize the environment. This can be done using an API for controlling home appliances (e.g., a smart home API).

[0690] In addition, the server uses a generative AI model to generate suggestions for improving lifestyle habits. These suggestions include meal plans, exercise methods, and specific advice for stress management, which are sent to the user's device as notifications. This allows the user to receive effective advice tailored to their own health condition.

[0691] A concrete example of its use would be providing advice based on user data, such as, "Incorporating regular exercise will help maintain your health." An example of a prompt would be, "Generate and output appropriate health advice based on the user's data."

[0692] Ultimately, users provide feedback on the effectiveness of the suggestions, allowing the system to continuously evolve and provide more accurate advice and environmental adjustments. This enables long-term and sustainable health management.

[0693] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0694] Step 1:

[0695] The device synchronizes with wearable devices and smartphones to collect the user's biometric information. It acquires data such as heart rate, steps, and sleep patterns as input, and temporarily stores this data in its internal memory. The collected data is obtained as electrical signals and converted into digital data.

[0696] Step 2:

[0697] The device sends biometric information collected at regular intervals to the server. The input is a set of multiple biometric data stored in memory, and the output is an encrypted data packet. This packet is transmitted securely using the SSL / TLS protocol.

[0698] Step 3:

[0699] The server decrypts the received data packets and stores the biometric information in a database. The input is encrypted data packets, and the output is a record of the biometric information in the database. The data is written to a relational database such as MySQL or PostgreSQL.

[0700] Step 4:

[0701] The server analyzes stored biometric information to identify the user's biometric patterns. The input is historical data from a database, and the output is rhythmic information such as the user's sleep and wake times and peak activity times. This process utilizes machine learning algorithms to analyze data trends.

[0702] Step 5:

[0703] The server generates environmental adjustment instructions based on identified biometric patterns and sends them to the smart device. The input is rhythm information, and the output is a message with environmental setting instructions. This allows lighting and air conditioning to be automatically adjusted to match the user's rhythm.

[0704] Step 6:

[0705] The server uses an AI model to generate specific health suggestions based on the user's biometric information and identified rhythms. Rhythm information and health data are used as input, and the output includes suggestions for diet and exercise, as well as stress management advice. These suggestions are then notified to the user's device.

[0706] Step 7:

[0707] Users implement the provided advice and provide feedback to the system about its effectiveness. Input consists of the user's personal impressions and data, while output is the server-side recording of the feedback and the resulting system adjustments. The advice may be updated based on the feedback.

[0708] (Application Example 1)

[0709] 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".

[0710] In modern society, there is a need to efficiently manage users' health while simultaneously offering product recommendations that meet their needs. However, conventional health management systems only analyze users' biometric data and provide health advice, lacking concrete product recommendations or connections to purchasing behavior. Therefore, a more consistent integration of health management and product purchase is necessary.

[0711] 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.

[0712] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's biological cycle based on the biometric information, health advice generation means for generating health advice based on the analysis results, and electronic commerce means for presenting product information based on the health advice and facilitating purchases through electronic payment. This makes it possible to support the user's health improvement in their daily life while effectively and efficiently encouraging the purchase of related products.

[0713] "Information gathering means" refers to means of obtaining biometric information from a user.

[0714] "Information analysis means" refers to means for analyzing the user's biological cycle using acquired biological state information.

[0715] "Environmental synchronization means" are means for optimizing environmental conditions in accordance with the analyzed biological cycle.

[0716] A "health advice generation method" is a means for generating health advice for a user based on the results of information analysis.

[0717] "Electronic commerce methods" refer to means of presenting product information based on health advice and enabling users to make purchases through electronic payment.

[0718] In the system implementing this invention, a wearable device or smartphone used by the user first collects biometric information. This information is then transmitted to a server via the terminal. The data acquired by the information collection means includes heart rate, exercise level, sleep patterns, and so on.

[0719] The server analyzes the collected biometric information using information analysis tools. The analysis is performed to identify the user's biological cycle by comparing it with past data. This reveals things like sleep patterns and peak activity times. Furthermore, a health advice generation tool is used to generate specific health advice based on the analysis results, which is then sent to the user's device. This may include suggestions for meal plans and exercise routines.

[0720] In addition, based on the generated health advice, relevant product information is presented to the user via e-commerce. This product information is displayed on the user's personal device in a format that allows for electronic payment. The user can then review the suggested products and easily proceed with the purchase process.

[0721] For example, if a user's lack of exercise is detected from their biometric data, the server may suggest fitness-related products or gym memberships. This allows the user to easily start healthy habits. Another example of a prompt for the generating AI model is: "Generate appropriate product suggestions based on the user's health data. Example: Suggest fitness-related products for a user who is not getting enough exercise."

[0722] The entire system utilizes a data analysis platform for information analysis and health advice generation. Specifically, services such as AWS and Google Cloud are sometimes used as examples. Communication protocols that prioritize security and efficiency are used for sending and receiving information.

[0723] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0724] Step 1:

[0725] The device acquires biometric information from the user's wearable device or smartphone. This includes data such as heart rate, steps taken, and sleep patterns. It receives biometric data directly from the user's device as input and temporarily stores the data within the device.

[0726] Step 2:

[0727] The device transmits the collected biometric information to the server. Specifically, it packages the collected data using a secure communication protocol and transfers it to the server. The output is biometric data formatted in a format that can be processed by the server.

[0728] Step 3:

[0729] The server analyzes the received biometric data using information analysis tools. It compares the input biometric data with past data to identify the user's biological cycle. The output is the analysis results, such as the user's sleep time and peak activity times.

[0730] Step 4:

[0731] The server uses a health advice generation mechanism based on the analysis results to generate specific health advice. Using the analysis results as input, it utilizes a generation AI model to create health advice tailored to the user. The output includes health advice, meal plans, and exercise suggestions.

[0732] Step 5:

[0733] The server uses e-commerce tools based on health advice to send relevant product information to the user's terminal. Based on the health advice generated as input, it selects product information and creates a suitable offer for the user. The output is a suggestion that includes product details and a purchase link.

[0734] Step 6:

[0735] The user reviews the suggested products displayed on the terminal and makes a purchase via electronic payment if necessary. The user uses product information transmitted from the server as input to complete the purchase process. The output is confirmation information regarding the purchased items.

[0736] 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.

[0737] This invention is a system that supports health management based on the user's biometric data and emotions. Here, we present a specific embodiment that includes an emotion engine.

[0738] First, the device acquires the user's biometric data, such as heart rate, steps taken, and sleep patterns, via a wearable device or smartphone. This data is stored on the device and prepared to be sent to a server later.

[0739] The server receives the collected biometric data and stores it in a database. Data analysis tools analyze the user's biological rhythms and identify lifestyle patterns such as sleep patterns and peak activity times.

[0740] Next, the emotion engine functions. It uses the camera and microphone on the device to collect the user's facial recognition data and voice data, and sends it to the server. The emotion engine analyzes this data to identify the user's emotional state (e.g., joy, sadness, stress).

[0741] The server generates and adjusts health advice based on the identified emotional state. For example, if the user is feeling stressed, it provides advice on breathing exercises or meditation to help them relax. Additionally, an environmental synchronization mechanism sends instructions to the smart device to adjust the environment according to the emotional state. Specifically, this includes changing the lighting to softer light or playing relaxation music to help the user relax.

[0742] For example, if the emotion engine detects that a user is showing signs of stress, the server will generate advice such as, "Try a 5-minute deep breathing session" to relieve stress. At the same time, it will send instructions to the smart device to change the room lighting to a more relaxing tone. This allows the user to enjoy individually optimized health management and a comfortable environment.

[0743] Ultimately, the system improves the accuracy of advice and environment synchronization based on user feedback. By inputting feedback into the terminal about the results of following the advice and the effects experienced, the server accumulates and analyzes information to provide more personalized suggestions, thereby improving the overall accuracy of the system. In this way, a system equipped with an emotion engine realizes comprehensive and sustainable health management that responds to the individual needs of users.

[0744] The following describes the processing flow.

[0745] Step 1:

[0746] The device synchronizes with the user's wearable device or personal electronic device to collect biometric data such as heart rate, steps taken, and sleep patterns. This data is temporarily stored on the device for later analysis.

[0747] Step 2:

[0748] The device uses its built-in camera and microphone to collect user facial recognition data and voice data. This data is used to identify the user's emotional state.

[0749] Step 3:

[0750] The device collects biometric and emotion-related data and sends it to a server via the internet. Once the transmission is complete, the system confirms receipt of the data.

[0751] Step 4:

[0752] The server stores the received biometric data and emotion-related data in a database. This creates a history of the user's past and present health and emotional states.

[0753] Step 5:

[0754] The server's data analysis system analyzes biometric data to identify the user's biological rhythm. Simultaneously, the emotion engine processes emotion-related data to determine the user's current emotion.

[0755] Step 6:

[0756] The server generates health advice based on the user's biological rhythms and emotional state. For example, if the user indicates stress, it creates a specific action plan to reduce stress.

[0757] Step 7:

[0758] The server's environment synchronization mechanism identifies the optimal environmental conditions based on the user's emotional state. It then sends instructions to the smart device to adjust lighting, sound, and temperature.

[0759] Step 8:

[0760] The device notifies the user of health advice and environmental adjustment instructions generated by the device. Specific methods and suggestions are provided in an easy-to-understand format.

[0761] Step 9:

[0762] Users follow health advice and, if necessary, input the provided feedback into their device. They provide information about their own impressions and the effectiveness of the advice.

[0763] Step 10:

[0764] The device sends user feedback it collects to a server, which the system uses to analyze and improve the accuracy of health advice and environmental adjustments. The system incorporates user feedback to make future suggestions more personalized.

[0765] (Example 2)

[0766] 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".

[0767] In modern society, maintaining users' physical and mental health requires precise health management tailored to individual lifestyles and emotional states. However, conventional systems are limited to simple health advice based solely on biometric information, making comprehensive health management that considers the user's emotional state and living environment difficult.

[0768] 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.

[0769] In this invention, the server includes information gathering means for acquiring biometric information from the user, information analysis means for analyzing the user's lifestyle patterns based on the biometric information, and emotion analysis means for identifying the user's emotional state. This enables precise and comprehensive health management tailored to the user's individual health condition.

[0770] "Information gathering means" refers to a device or system designed to acquire biometric information from a user.

[0771] "Information analysis means" refers to a device or system for analyzing a user's lifestyle patterns based on collected biometric information.

[0772] An "advice generation means" is a device or system for generating health advice based on analysis results and the user's emotional state.

[0773] "Emotional analysis tools" refer to devices or systems designed to identify a user's emotional state.

[0774] A "harmonization tool" is a device or system for adjusting environmental conditions based on analysis results and emotional states.

[0775] A "personal information device" is a device or equipment used by a user individually for collecting information.

[0776] A "household device" is a device or system used to adjust environmental conditions within a household.

[0777] This system provides personalized health advice and environmental adjustments based on the user's biometric information and emotional state. First, the device acquires biometric information such as heart rate, steps taken, and sleep patterns via personal information devices like wearable devices and smartphones. To achieve this, the device periodically communicates with these devices using Bluetooth or Wi-Fi. The acquired data is temporarily stored within the device.

[0778] Subsequently, the device transmits biometric information to a server using a secure communication protocol. The server stores the received information in a database and uses data analysis tools to identify the user's lifestyle patterns. This analysis may involve machine learning algorithms using Python or similar data processing languages.

[0779] Furthermore, the device uses its built-in camera and microphone to collect user facial recognition data and voice data. This information is sent to a server, which uses sentiment analysis tools to identify the user's emotional state. In this process, image processing and machine learning libraries such as OpenCV and TensorFlow are often used.

[0780] Based on analyzed lifestyle patterns and emotional states, the server generates optimal health advice for the user using an advice generation system. For example, if the user is identified as being in a high-stress state, advice such as "Try a 5-minute deep breathing session" might be provided. This advice is generated based on a generative AI model.

[0781] Furthermore, the server uses a harmonization mechanism to send environmental adjustment instructions to the corresponding home devices. For example, an instruction is sent to a smart device to change the room lighting to a warmer color to promote relaxation.

[0782] This system allows users to experience personalized health management and a comfortable environment. For example, if the emotional analysis detects signs of stress in the user, the server can generate advice such as, "To alleviate stress levels, start with deep breathing," and instruct the user to change the room lighting to calming colors. Through this entire process, the user's health is optimized.

[0783] An example of a prompt message might be: "Measure the user's current stress level and suggest appropriate health advice and environmental adjustments based on that level."

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] The device acquires the user's biometric information through wearable devices and smartphones. It uses Bluetooth or Wi-Fi communication to collect heart rate, steps, and sleep patterns from the devices. The collected data is temporarily stored within the device. The input is biometric information from the wearable device, and the output is biometric information stored within the device.

[0787] Step 2:

[0788] The device sends the stored biometric information to the server. A secure protocol such as HTTPS is used for this transmission. The server stores the received data in a database. The input is the biometric information stored on the device, and the output is the biometric information stored in the database.

[0789] Step 3:

[0790] The server uses biometric information stored in the database to identify the user's lifestyle patterns through information analysis. Machine learning algorithms using programming languages ​​such as Python are used to calculate sleep times and peak activity times. The input is biometric information from the database, and the output is the analyzed lifestyle pattern data.

[0791] Step 4:

[0792] The device collects user facial recognition and voice data via its camera and microphone. This collected data is sent to a server. The input is the user's face and voice data, and the output is the facial recognition and voice data sent to the server.

[0793] Step 5:

[0794] The server uses emotion analysis tools to identify the user's emotional state from received facial recognition and voice data. Libraries such as OpenCV and TensorFlow are used to determine emotions like joy and stress. The input is the facial recognition and voice data sent to the server, and the output is the identified emotional state data.

[0795] Step 6:

[0796] The server generates health advice based on analyzed lifestyle pattern data and emotional state data using an advice generation mechanism. A generation AI model is utilized to create, for example, instructions for stress reduction. The input is lifestyle pattern data and emotional state data, and the output is health advice provided to the user.

[0797] Step 7:

[0798] The server, using a harmonization mechanism, transmits environmental adjustment instructions to household devices, along with the generated health advice. It sends instructions to devices such as smart light bulbs and audio equipment to adjust lighting or play music. The input is the generated health advice, and the output is the environmental adjustment instructions to the household devices.

[0799] Step 8:

[0800] Users input feedback into their terminals based on the advice and adjustments provided. This feedback is collected by the server and used to improve the system's accuracy. The input is user feedback, and the output is feedback data stored on the server.

[0801] (Application Example 2)

[0802] 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".

[0803] In traditional brick-and-mortar stores, it is difficult to provide individualized services that take into account each customer's emotional state and health condition. Furthermore, there is a lack of means to dynamically adjust the store environment according to individual needs, making it difficult to provide a shopping experience optimized for each customer.

[0804] 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.

[0805] In this invention, the server includes means for acquiring biometric data, means for identifying emotional states and generating product suggestions, and means for transmitting instructions to facility equipment and adjusting the environment. This enables optimized product suggestions and environmental adjustments based on the biometric data and emotional states of customers.

[0806] "Data collection means" refers to a device or method that includes a function for acquiring a user's biometric data.

[0807] "Data analysis means" refers to a device or method for analyzing acquired biometric data to identify the user's biological rhythm and emotional state.

[0808] "Environmental synchronization means" refers to a device or method for optimizing environmental conditions in accordance with the user's biological rhythm.

[0809] "Health advice generation means" refers to a device or method for generating health advice for a user based on analysis results.

[0810] "Product suggestion generation means" refers to a device or method for identifying the emotional state of a customer visiting a store and generating product suggestions based on that state.

[0811] "Environmental adjustment instruction means" refers to a device or method that generates instructions for adjusting the environment inside a store and transmits them to the facilities and equipment for carrying them out.

[0812] Modes for carrying out the invention

[0813] The system that realizes this invention is implemented using the following hardware and software. First, the terminal collects necessary data (heart rate, steps, sleep patterns, etc.) via a wearable device or smart terminal to acquire the user's biometric data. This information is stored on the terminal and sent to a server for processing.

[0814] The server stores the received biometric data in a database and analyzes the user's biological rhythm using data analysis tools (e.g., Apache Hadoop). The analysis results are used to generate health advice tailored to the user through a health advice generation system. To identify the user's emotional state, the emotion engine collects facial recognition data and voice data using the camera and microphone installed on the device and requests analysis from the server.

[0815] Furthermore, the product suggestion generation means proposes the most suitable product based on the customer's identified emotional state. This suggestion is presented through a smart device installed in the store, such as a smart glasses display, while the environment adjustment instruction means sends instructions to the facility equipment to adjust, for example, the store's lighting and music to suit the customer's emotional state.

[0816] As an example, if the system determines that a particular customer is experiencing stress, it will display suggestions for relaxing products on their glasses and change the store's lighting to a softer tone. The suggested products could include aromatherapy oils effective in reducing stress.

[0817] An example of a prompt message could be: "Based on the user's latest emotional state data, suggest products that promote relaxation. Also, change the store lighting to a warmer color." This prompt message is used as input to recommend activities optimized for the user, utilizing a generative AI model.

[0818] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0819] Step 1:

[0820] The device collects user biometric data from wearable devices and smart devices. Inputs include heart rate, steps taken, and sleep patterns. This data is stored locally and later prepared for transmission to a server. Data collection is performed in real time, and the data is updated at regular intervals.

[0821] Step 2:

[0822] The terminal transmits the collected biometric data to the server. The input is the biometric data acquired in step 1, and the output is the data sent to the server, which is then stored in the database. Data communication is encrypted to ensure security.

[0823] Step 3:

[0824] The server analyzes the user's biological rhythms using data analysis tools based on biometric data stored in the database. The input is biometric data in the database, and the output is the user's lifestyle patterns, such as sleep times and peak activity times. Machine learning algorithms are used for data analysis to identify individual rhythms.

[0825] Step 4:

[0826] The device uses its camera and microphone to collect user facial recognition and voice data. This serves as input, and the collected data is sent to a server to be prepared for analysis as emotion data. The data is captured in real time based on facial expressions and voice tone.

[0827] Step 5:

[0828] The server uses an emotion engine to analyze facial recognition and voice data to identify the user's emotional state. Based on the facial recognition and voice data received as input, it identifies emotional states such as joy, sadness, and stress as output. The analysis results are dynamically updated to ensure that the most up-to-date emotional state is reflected in real time.

[0829] Step 6:

[0830] The server uses a generative AI model to input prompts based on identified emotional states, generating health advice and product suggestions. The input is the user's emotional state, and the output is health advice such as "Try a 5-minute deep breathing session" or suggestions for relaxing products.

[0831] Step 7:

[0832] The server generates and sends instructions to the facility's equipment for environmental adjustments. The input is a generated prompt message, and the output includes instructions for environmental settings such as adjusting the color of the store's lighting and selecting and playing music. This optimizes the user's shopping experience.

[0833] Step 8:

[0834] Users conduct an experience based on advice from the server and input feedback into their terminal. The input is data about the user's experience, and the entire system uses this feedback to improve its accuracy as output. The improved data is reflected in subsequent analyses and suggestions.

[0835] 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.

[0836] 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.

[0837] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0838] 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.

[0839] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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."

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0856] The following is further disclosed regarding the embodiments described above.

[0857] (Claim 1)

[0858] A data collection method for acquiring biometric data from users,

[0859] A data analysis means for analyzing the user's biological rhythm based on the aforementioned biometric data,

[0860] An environmental synchronization means that optimizes environmental conditions in accordance with the aforementioned biological rhythm,

[0861] A health advice generation means that generates health advice based on the analysis results,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, wherein the data collection means communicates with a personal electronic device to collect biometric data.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the environment synchronization means transmits instructions to household appliances to adjust the environment.

[0867] "Example 1"

[0868] (Claim 1)

[0869] Information collection means for acquiring biometric information from users,

[0870] Information analysis means for analyzing the user's biological pattern based on the aforementioned biological information,

[0871] An environmental synchronization means that adjusts environmental conditions according to the aforementioned biological pattern,

[0872] A proposal generation means that generates lifestyle improvement suggestions according to the aforementioned analysis results,

[0873] A feedback adjustment mechanism for receiving feedback on the aforementioned proposal and updating its content,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the information gathering means communicates with a personal electronic device to collect biometric information.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the environment synchronization means transmits instructions to home appliances to optimize the environment.

[0879] "Application Example 1"

[0880] (Claim 1)

[0881] A means of collecting information to acquire biometric information from the user,

[0882] Information analysis means for analyzing the user's biological cycle based on the aforementioned biological state information,

[0883] An environmental synchronization means that optimizes environmental conditions in accordance with the aforementioned biological cycle,

[0884] A health advice generation means that generates health advice based on the analysis results,

[0885] An electronic commerce method that presents product information based on the aforementioned health advice and allows purchases to be made via electronic payment,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, wherein the information gathering means communicates with a personal information device to collect biological state information.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein the environment synchronization means transmits instructions to residential equipment to adjust the environment.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] Information collection means for obtaining biometric information from users,

[0894] Information analysis means for analyzing the user's lifestyle patterns based on the aforementioned biometric information,

[0895] An advice generation means that generates health advice based on the analysis results and the user's emotional state,

[0896] A means of sentiment analysis for identifying the emotional state of a user,

[0897] Harmonization means for adjusting environmental conditions based on the aforementioned analysis results and emotional state,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein the information gathering means communicates with a personal information device to collect information.

[0901] (Claim 3)

[0902] The system according to claim 1, wherein the harmonizing means transmits instructions to a household device to adjust the environment.

[0903] "Application example 2 of combining emotional engines"

[0904] (Claim 1)

[0905] A data collection method for acquiring biometric data from users,

[0906] A data analysis means for analyzing the user's biological rhythm based on the aforementioned biometric data,

[0907] An environmental synchronization means that optimizes environmental conditions in accordance with the aforementioned biological rhythm,

[0908] A health advice generation means that generates health advice based on the analysis results,

[0909] A product suggestion generation method that identifies the emotional state of customers visiting a store and generates product suggestions,

[0910] An environmental adjustment instruction means that outputs instructions to adjust the in-store environment,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the data collection means communicates with an information processing device to collect biological data.

[0914] (Claim 3)

[0915] The system according to claim 1, wherein the environmental adjustment instruction means transmits instructions to facility equipment to adjust the environment. [Explanation of Symbols]

[0916] 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 data collection method for acquiring biometric data from users, A data analysis means for analyzing the user's biological rhythm based on the aforementioned biometric data, An environmental synchronization means that optimizes environmental conditions in accordance with the aforementioned biological rhythm, A health advice generation means that generates health advice based on the analysis results, A system that includes this.

2. The system according to claim 1, wherein the data collection means communicates with a personal electronic device to collect biometric data.

3. The system according to claim 1, wherein the environment synchronization means transmits instructions to household appliances to adjust the environment.

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