system

Abstract

We provide the system. [Solution] Multiple sensor means for collecting biological information, Processing means for processing data collected by the aforementioned sensor means and analyzing the user's emotional state, A proposal generation means that proposes appropriate actions to the user based on the aforementioned analysis, A notification means for notifying the user of the proposals from the proposal generation means, A feedback processing means for collecting user feedback and improving the next proposal by taking said feedback into consideration, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the number of patients with mental disorders is increasing, and the accompanying social and economic losses have become a serious problem. Mental disorders are often difficult to recover once they occur, and prevention and early detection are important. However, it is difficult to grasp an individual's mental state in real time and propose appropriate countermeasures with conventional methods. In addition, there is a lack of a mechanism for early catching of emotional changes that are difficult for the user himself / herself to notice and presenting specific countermeasures.

Means for Solving the Problems

[0005] This invention first collects biometric information such as the user's heart rate, body temperature, facial expression, and voice using multiple sensor means. Then, a processing means analyzes this data and evaluates the user's emotional state. Next, based on the analysis results, a suggestion generation means creates an optimal action for the user. This suggestion is communicated to the user by a notification means using an audio output device. The invention also includes a feedback processing means that collects feedback from the user and uses that information to improve future suggestions. This provides a system that can help maintain the user's mental health and support the prevention and early detection of mental illness.

[0006] A "sensor means" is a device for acquiring the user's biometric information, and has the function of collecting heart rate, body temperature, facial expressions, and voice.

[0007] "Processing means" refers to a processing unit within the system that analyzes biometric information collected by sensor means and evaluates the user's emotional state.

[0008] A "proposal generation means" is a mechanism or algorithm that creates optimal action suggestions for the user based on the results of an evaluation of the emotional state by a processing means.

[0009] "Notification means" refers to a device for communicating proposals created by the proposal generation means to the user, and in particular, refers to an audio output device.

[0010] A "feedback processing mechanism" is a system-internal processing mechanism that analyzes feedback received from users and uses it to improve future proposals.

[0011] "Biometric information" refers to data that indicates the user's health and emotional state, including the user's heart rate, body temperature, facial expressions, and voice. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0017] In the following embodiments, the labeled storage 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, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a comprehensive system for maintaining the mental health of users, and it continuously collects the user's biometric information using multiple sensors. Specifically, the terminal is equipped with various sensors for acquiring heart rate, body temperature, facial expressions, and voice, and these sensors acquire the user's biometric information in real time.

[0034] The acquired biometric information is temporarily stored on the device and simultaneously encrypted and securely transmitted to the server. The server decrypts the received data and uses various processing methods to analyze the emotional state. Here, an AI algorithm is used to identify fluctuations in heart rate, voice tone, and changes in facial expression to evaluate the user's mental state.

[0035] For example, if the server's analysis detects that a user is showing significant stress during a specific time period or situation, the suggestion generation system will create specific action suggestions for the user, such as "It would be good to take a short break and drink some water." These suggestions are then communicated to the user via an audio output device on the terminal.

[0036] Furthermore, user feedback is collected via voice or the device interface and analyzed by a feedback processing system. This allows the system to continuously adjust to provide more optimized suggestions for individual users. This process effectively supports the prevention and early detection of users' mental health issues.

[0037] As a concrete example demonstrating the advantages of this system, if a user feels stressed after a meeting, fluctuations in their heart rate are detected, and the server suggests a 5-minute break. After the break, improved productivity is confirmed, and the user submits feedback on its effectiveness. This feedback is then used to improve future suggestions, enabling more personalized responses.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The device acquires the user's biometric information using various sensors. The heart rate sensor continuously monitors the heart rate, the camera periodically captures the user's facial expressions, and the microphone records the tone and content of their voice. This data is collected at regular intervals and stored in storage.

[0041] Step 2:

[0042] The terminal preprocesses the collected biometric information, performing noise reduction and data formatting, then encrypts it using a security protocol before sending it to the server. To achieve real-time data transfer, the data is delivered to the server in streaming format.

[0043] Step 3:

[0044] The server receives data transmitted from the terminal and performs analysis using processing tools. Utilizing AI algorithms, it extracts patterns indicating emotional states from biometric data and evaluates the user's current mental state. It also compares this data with past data to identify signs of stress or abnormalities.

[0045] Step 4:

[0046] Based on the processing results, the server generates action suggestions for the user using a suggestion generation mechanism. These suggestions include specific actions that help improve the user's mental state and are customizable.

[0047] Step 5:

[0048] The terminal receives suggestions from the server and notifies the user using an audio output device. The suggestions are adjusted according to the time and situation and converted into audio in a format that is easy for the user to understand.

[0049] Step 6:

[0050] The user chooses whether or not to act on the suggestion and inputs the result as feedback into the device. Feedback can be entered using voice input or touch gestures and sent back to the system.

[0051] Step 7:

[0052] The server analyzes user feedback and uses it to improve future suggestions through feedback processing mechanisms. User behavior history and feedback data are used to provide individually optimized solutions and improve the accuracy of the system.

[0053] (Example 1)

[0054] 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."

[0055] In modern society, maintaining individual mental health is a crucial issue, and stress management in daily life is a central theme. However, there is no system that provides individualized approaches tailored to each user, offers appropriate behavioral suggestions in real time, and continuously improves their effectiveness through feedback. This invention aims to provide comprehensive and flexible support for improving and maintaining users' mental health.

[0056] 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.

[0057] In this invention, the server includes processing means using artificial intelligence to decode information and analyze emotional states, suggestion generation means that propose specific actions to the user based on the analysis results, and feedback improvement means that collect user feedback and optimize the suggestions based on that feedback. This makes it possible to provide personalized, real-time action suggestions and improve the quality of the suggestions based on user feedback.

[0058] A "sensor unit" is a hardware component used to acquire a user's biometric data, and it has the function of detecting heart rate, temperature, facial expressions, voice, etc., in real time.

[0059] A "data management means" is a part of a system configuration that has the function of temporarily storing acquired biometric information and then encrypting and securely storing that information.

[0060] "Communication methods" refer to a set of infrastructure and protocols used to transmit encrypted data from a terminal to a server, enabling secure and efficient data transfer.

[0061] "Processing methods using artificial intelligence" refers to algorithms and their implementations used to analyze decoded data on a server and evaluate the user's emotional state.

[0062] A "proposal generation means" is a part of a system configuration that has the function of automatically generating specific action suggestions for the user based on the results of the analyzed emotional state.

[0063] "Notification means" refers to devices or methods that transmit generated action suggestions to the user via voice or other means, enabling the user to receive the suggestions immediately.

[0064] "Feedback improvement methods" are technical elements that improve the accuracy and effectiveness of proposals by analyzing feedback collected from users and reflecting the results in future proposals.

[0065] This invention is an information processing system for maintaining and improving the mental health of users. It acquires and analyzes the user's biometric data using various sensor units and provides individually adapted behavioral suggestions.

[0066] The device is equipped with multiple sensor units that capture heart rate, body temperature, facial expressions, and voice. These sensors collect the user's real-time biometric data, and this information is temporarily stored and encrypted by a data management system. For example, AES encryption technology is used in data management.

[0067] The device securely transmits encrypted biometric data to the server via a communication method. HTTPS is used as the communication protocol to ensure data integrity and privacy.

[0068] The server decodes the received data and evaluates the emotional state using artificial intelligence processing. Specifically, it utilizes a generative AI model to analyze heart rate variability, voice tone, and changes in facial expressions to determine the user's stress level and other factors.

[0069] Based on the analysis results, the server automatically generates appropriate action suggestions for the user using a suggestion generation mechanism. These suggestions are sent to the terminal via a notification mechanism, and the terminal notifies the user through an audio output device.

[0070] After implementing a suggested action, the user provides feedback on its effectiveness. This feedback is collected via voice or a terminal interface and analyzed by feedback improvement tools. This improves the accuracy of future suggestions.

[0071] For example, if a user experiences stress after a meeting and fluctuations in their heart rate are detected, the server suggests taking a 5-minute break. If the user then finds the break effective, this feedback is used to inform future suggestions.

[0072] An example of a prompt is, "What should be suggested when the user's heart rate is higher than normal?" This prompts the generative AI model to analyze and suggest a response based on the user's state.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The device uses a sensor unit to collect the user's heart rate, body temperature, facial expressions, and voice data in real time. The input is the user's biometric data, and the output is this data transmitted to the device through the sensor interface.

[0076] Step 2:

[0077] The device temporarily stores the collected biometric data in its internal storage and then encrypts the data using AES encryption technology. The input is the collected biometric data, and the output is the encrypted data, which is prepared for the next step.

[0078] Step 3:

[0079] The device sends encrypted data to the server via a secure communication protocol (HTTPS). The input is encrypted biometric data, and the output is data sent to the server.

[0080] Step 4:

[0081] The server receives the data and decrypts it using a pre-configured key. The input is encrypted data, and the output is decrypted biometric data. Based on this, the server is ready to perform the next processing step.

[0082] Step 5:

[0083] The server applies a generative AI model to the decoded biometric data, analyzing heart rate, voice tone, and facial expression changes to evaluate the user's emotional state. The input is the decoded biometric data, and the output is an indicator of the analyzed emotional state.

[0084] Step 6:

[0085] The server generates appropriate action suggestions for the user using a suggestion generation mechanism based on the analyzed data. The input is an indicator of the user's emotional state, and the output is the action suggestion.

[0086] Step 7:

[0087] The terminal notifies the user of action suggestions received from the server via an audio output device. The input is the action suggestion, and the output is the suggestion notification to the user.

[0088] Step 8:

[0089] The user takes action based on the suggestion and provides feedback on the results via voice or an interface. The input is the user's response to the suggestion, and the output is the feedback information.

[0090] Step 9:

[0091] The server analyzes user feedback and incorporates it into future suggestions through feedback improvement mechanisms. The input is feedback information, and the output is the improved suggestion.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In today's consumer environment, there is a demand for real-time understanding of consumers' emotional needs and stress levels, and the provision of individually optimized customer service. However, conventional systems have struggled to provide such individualized support, resulting in challenges in fully increasing consumer satisfaction. In particular, in virtual stores, it is difficult to grasp consumers' emotions and stress levels because their faces are not visible, and a new approach is needed to improve the quality of the consumer experience.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes at least one detection means for collecting biometric information, an information processing means for processing the data collected by the detection means and analyzing the user's emotional state, and a service adjustment means for adjusting the service method using the analyzed data. This makes it possible to provide personalized service that responds to the user's real-time stress level and emotional state.

[0097] "Biometric information" refers to data about the user's physical and mental state, particularly circulatory activity, body temperature, facial expression changes, and acoustics.

[0098] "Detection means" refers to a device or sensor for acquiring biometric information from a user.

[0099] "Information processing means" refers to a device or system that has the function of analyzing biometric information collected by detection means and evaluating the emotional state of the user.

[0100] A "proposal formation means" refers to a system that, based on the analysis results of information processing means, presents the most appropriate action for the user.

[0101] "Information transmission means" refers to a system that transmits information generated by proposal formation means to users.

[0102] "Adjustment processing means" refers to a system that collects responses from users and adjusts them while considering the feedback in order to generate more optimized suggestions.

[0103] "Customer service adjustment means" refers to a function that uses analytical data from information processing means to appropriately improve and adjust customer service methods.

[0104] A system for implementing this invention comprises a detection means for collecting biological information, an information processing means for analyzing the collected information, a proposal formation means for generating proposals based on the analysis results, an information transmission means for communicating the generated proposals to the user, an adjustment processing means for collecting user feedback and optimizing the next proposal, and a customer service adjustment means for adjusting the customer service method.

[0105] The server receives circulatory activity, body temperature, facial expression changes, and acoustic data transmitted from the detection means, and analyzes this data using information processing means. The analysis is performed using AI algorithms such as TENSORFLOW® and PyTorch to evaluate the user's emotional state.

[0106] After the analysis results are obtained, the suggestion generation system generates optimal action instructions for the user. For example, if the system determines that the user is in a stressful state, it might suggest "relaxing activities." This suggestion is then communicated to the user using an acoustic information transmission system.

[0107] Furthermore, the terminal collects feedback from users and sends it to the server. The server analyzes this feedback using an adjustment processing mechanism and makes adjustments to help generate future suggestions. At the same time, the customer service adjustment mechanism improves the customer service method by reflecting the analyzed data.

[0108] As a concrete example, consider a situation where a user who visited a virtual store on a holiday experiences increased cardiovascular activity and stress is detected. In this case, the AI ​​system can suggest "relaxing products that might be good to try" along with calming music.

[0109] An example of a prompt to input into the generation AI model is: "Create a prompt that generates relaxation suggestions when the user's heart rate is elevated and their facial expression shows signs of tension."

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

[0111] Step 1:

[0112] The device collects biometric information from the user in real time. Specifically, it uses a heart rate sensor, temperature sensor, camera, and microphone to detect heart rate, body temperature, facial expressions, and voice. The input is raw data from each sensor, and the output is digital data compiled from this data.

[0113] Step 2:

[0114] The terminal temporarily stores the collected digital data locally and then encrypts and transmits it to the server via a secure communication protocol. The input is the digital data of biometric information generated in step 1, and the output is the secure transmission of data to the server.

[0115] Step 3:

[0116] The server receives the incoming data and analyzes it using information processing tools. Here, generative AI models such as TensorFlow and PyTorch are used to determine the user's emotional state. The input is encrypted digital data, and the output is the analysis result representing the user's emotional state.

[0117] Step 4:

[0118] Based on the analysis results, the server generates optimal suggestions for the user using a suggestion formation mechanism. Prompt messages are set according to the analysis results, and the AI ​​generation model generates specific action guidelines such as "suggest relaxation activities." The input is the analysis results of the emotional state, and the output is specific action suggestions.

[0119] Step 5:

[0120] The terminal receives a suggestion sent from the server and notifies the user using an information transmission method. The operation here involves transmitting the notification content to the user using an audio output device or similar. The input is the suggestion content from the server, and the output is an audio notification to the user.

[0121] Step 6:

[0122] Users send feedback on suggestions to the server via their device. This feedback is collected through voice and the user interface. The input is user feedback, and the output is data sent to the server.

[0123] Step 7:

[0124] The server analyzes the received feedback and uses adjustment processing to improve the algorithm for future proposal generation. Simultaneously, it uses customer service adjustment to improve customer service methods. The input is user feedback, and the output is the optimized proposal generation process.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system that effectively maintains and manages a user's mental health, and proposes the optimal action for the user by comprehensively analyzing biometric information and emotions. This system acquires biometric information via a terminal equipped with multiple sensor means. Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the terminal continuously collect heart rate, body temperature, facial expression, and voice information.

[0127] By incorporating an emotion engine, the server performs detailed emotional analysis on this biometric information. The emotion engine utilizes machine learning algorithms to accurately recognize the user's emotions from acquired voice and facial expression data. In this process, it also refers to the user's long-term data history and performs trend analysis to understand the trends in emotional fluctuations.

[0128] For example, if a user shows signs of stress more frequently than usual in their daily activities, the emotion engine quickly detects this change and collects data to provide appropriate behavioral suggestions. These suggestions are then specifically customized depending on the situation, such as "try a particular relaxation technique" or "practice a short meditation."

[0129] The suggestion generation system automatically creates useful and specific action suggestions for the user based on information from analysis and the emotion engine. These suggestions are communicated to the user via the terminal's voice output device. The user selects an action based on the suggestion and provides feedback to the terminal regarding the result and the effectiveness of the suggestion. This feedback is analyzed by the server and used by the feedback processing system to improve the accuracy of future suggestions.

[0130] Thus, the present invention provides support for users to actively engage in managing their emotions and improving their mental health, and the combination of emotion engines enables more accurate and personalized services.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device uses multiple sensors to collect the user's biometric information. Specifically, a heart rate sensor records heart rate, a temperature sensor records body temperature, a camera captures facial expressions, and a microphone records voice, and this data is collected in real time.

[0134] Step 2:

[0135] The terminal temporarily stores the collected biometric information and performs noise reduction as a preprocessing step. After preprocessing, the data is encrypted and transmitted to the server using a secure communication method.

[0136] Step 3:

[0137] The server receives data sent from the terminal and analyzes the emotional state using an emotion engine. Voice and facial expression data are labeled with emotions using machine learning algorithms, and indicators of high stress or anxiety are identified.

[0138] Step 4:

[0139] The server investigates the user's long-term data history and analyzes trends in their current emotional state. This makes it possible to monitor increasing stress levels and abnormal emotional fluctuations in real time.

[0140] Step 5:

[0141] The suggestion generation mechanism proposes appropriate actions to the user based on the aforementioned analysis results. The suggested content is customized according to the emotional state recognized by the engine and includes specific actions such as "take a deep breath" or "take a 30-minute break."

[0142] Step 6:

[0143] The terminal notifies the user of suggestions generated by the suggestion generation mechanism via an audio output device. The suggestions are delivered appropriately at a time that does not interrupt the user's focus.

[0144] Step 7:

[0145] Users evaluate the effectiveness of the actions taken based on the received suggestions and provide feedback to their device. This feedback is provided via voice input or touch gestures and is used for subsequent analysis and suggestion generation.

[0146] Step 8:

[0147] The server analyzes user feedback and uses it in feedback processing to improve the accuracy of future suggestions. This process ensures that suggestions provided to users become increasingly tailored to their individual needs.

[0148] (Example 2)

[0149] 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".

[0150] In modern society, maintaining and improving users' mental health is a crucial issue, but there are limited systems capable of comprehensively analyzing users' biometric data and suggesting appropriate actions. Existing methods have the problem of difficulty in grasping subtle fluctuations in users' emotions and physical condition in real time and providing appropriate feedback. Therefore, there is a need to analyze users' biometric information and emotions with high accuracy and provide personalized suggestions that meet individual needs.

[0151] 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.

[0152] In this invention, the server includes a measuring device for collecting biometric data, a processing device for processing the information collected by the measuring device and analyzing the user's emotional state with high accuracy, and a suggestion generation device for suggesting actions based on the analysis and the user's long-term data history. This makes it possible to analyze the user's biometric information in real time and to quickly and appropriately suggest specific actions necessary to maintain and improve the user's mental health.

[0153] "Biometric data" refers to information that indicates the user's physical condition, including heart rate, body temperature, changes in facial expression, and voice information.

[0154] A "measuring device" is a device used to collect biological data and is equipped with various sensors.

[0155] A "processing device" is a device that analyzes biological data obtained from measuring devices and makes highly accurate judgments about the user's emotional state.

[0156] A "proposal generation device" is a device that proposes actions to users based on the analysis results from the processing device and the user's long-term data history.

[0157] An "output device" is a device that notifies the user of suggestions from the suggestion generation device, and conveys information through visual or auditory means.

[0158] A "feedback processing device" is a device that collects feedback from users, analyzes it, and uses that feedback to improve the accuracy of future suggestions.

[0159] This invention is a system that comprehensively analyzes a user's biometric information and emotional state and provides appropriate behavioral suggestions. The system comprises multiple measuring devices, processing devices, suggestion generation devices, feedback processing devices, and output devices.

[0160] Hardware and software

[0161] The device continuously collects biometric data such as heart rate, body temperature, facial expression changes, and voice information through measuring devices such as smartwatches and smartphones. This data is analyzed by a processing unit, and sentiment analysis is performed using machine learning algorithms. This analysis accurately determines the user's emotional state and also takes into account the long-term history of the data. Based on these analysis results, the server configures a suggestion generation device to propose the most appropriate action to the user.

[0162] Specific example

[0163] For example, if a user experiences a lot of stress in their daily life, the server will suggest relaxation methods or meditation based on the results of an emotion analysis. The suggestions are notified to the user via an audio output device, so the user can receive notifications on their smartphone or smart speaker. By inputting a prompt message such as, "Please suggest effective relaxation methods when the user wants to relax. Please refer to the current heart rate and voice tone," into the AI ​​model, the suggestion generator will derive specific actions based on that message.

[0164] Furthermore, user feedback is collected by a feedback processing device and used to improve the accuracy of future suggestions. This allows the system to provide more personalized and suitable suggestions to the user over time, thereby contributing to the user's mental well-being.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The device collects biometric data such as the user's heart rate, body temperature, facial expression changes, and voice information through measuring devices. This allows for the accumulation of real-time biometric data. The input biometric data is temporarily stored within the device for further processing. Specifically, the smartwatch measures the heart rate, and the smartphone captures audio and video.

[0168] Step 2:

[0169] The device transmits the collected biometric data to the server. This involves encrypting the data using a secure communication protocol and transmitting it to the server over the network. The input is the biometric data transmitted from the device, and the server generates output to receive it. Specifically, the data is uploaded to the server in real time using Wi-Fi or mobile data communication.

[0170] Step 3:

[0171] The server analyzes the received biometric data using a processing unit. The emotion engine uses machine learning algorithms to analyze the user's emotional state from the biometric data with high accuracy. The input is the biometric data sent to the server, and the output is the analysis result indicating the user's emotional state. Specifically, it performs voice tone analysis and facial expression recognition to determine stress levels and relaxation levels.

[0172] Step 4:

[0173] The server uses a suggestion generation device to create action suggestions for the user based on the analysis results. Considering the analysis results and the user's long-term history, it inputs appropriate prompt sentences into a generation AI model to create personalized suggestions. The input is the analysis results and user history, and the output is specific action suggestions. For example, a suggestion such as "You can reduce stress by taking deep breaths" might be generated.

[0174] Step 5:

[0175] Action suggestions generated by the suggestion generation device are communicated to the user via an output device on the terminal. Notifications are made via voice or display, presented in a way that is easily understandable to the user. The input is the generated action suggestion, and the output is the notification to the user. For example, a smart speaker might announce, "Try a short meditation session."

[0176] Step 6:

[0177] The user performs the suggested action and provides feedback on the result to the device. This feedback is provided via button presses or voice input and sent to the server by a feedback processing unit. The input is the user's feedback, and the output is the feedback data. Specifically, feedback might include statements like, "The suggested meditation helped reduce stress."

[0178] Step 7:

[0179] The server uses a feedback processing unit to analyze user feedback. This analysis enables a learning process that improves the accuracy of future suggestions. The input is user feedback data, and the output is an updated suggestion model and analysis algorithm. This improves the system so that it can provide suggestions that are more suitable for the user.

[0180] (Application Example 2)

[0181] 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."

[0182] In today's world, consumer shopping experiences are diversifying, and improving customer experience in this environment is essential. Especially in physical stores, personalized service based on the customer's emotions and mental state is crucial, but achieving this is difficult. Therefore, the challenge lies in providing a better shopping experience by utilizing the customer's biometric information to offer personalized suggestions.

[0183] 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.

[0184] In this invention, the server includes a plurality of sensor means for collecting biometric information, a processing means for processing the data collected by the sensor means and analyzing the user's emotional state, a suggestion generation means for proposing appropriate actions to the user based on the analysis, a notification means for notifying the user of the suggestions from the suggestion generation means, a feedback processing means for collecting user feedback and improving the next suggestion considering the feedback, and a presentation means for providing the user with an action to execute the suggestion generated by the suggestion generation means. This enables personalized suggestions in real time according to the biometric information of store visitors, providing a comfortable shopping experience in physical stores.

[0185] "Biometric information" refers to data that indicates a person's physical and emotional state, such as heart rate, body temperature, facial expressions, and voice.

[0186] "Sensor means" refers to devices or equipment used to acquire biological information.

[0187] "Processing means" refers to software or hardware functions for analyzing the user's emotional state from collected biometric information.

[0188] "Proposal generation means" refers to a device that handles the process of constructing and generating actions and choices suitable for the user based on the analyzed emotional state.

[0189] "Notification means" refers to an output device in the form of audio, visual, or other means that informs the user of the proposal generated by the proposal generation means.

[0190] "Feedback processing means" refers to a process or device that collects responses from users and uses them to improve the accuracy of suggestions.

[0191] "Presentation means" refers to methods or devices that allow the user to concretely perform the actions provided by the proposal generation means.

[0192] In this embodiment of the invention, a system is used that collects and analyzes a user's biometric information and provides appropriate action suggestions. The server receives data acquired from a terminal equipped with multiple sensor means and performs emotion analysis using a machine learning algorithm. Based on this analysis, the user's current emotional state is determined, and the suggestion generation means constructs specific actions.

[0193] Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the device continuously collect biometric information and transmit this data wirelessly to a server. The server processes this data using Python and utilizes an emotion engine to recognize the user's emotions with high accuracy. Emotional fluctuation trends are analyzed by referring to long-term data history.

[0194] Based on the analysis results, the suggestion generation means generates appropriate actions according to the user's state. Subsequently, the action suggestion is provided to the user via a notification means, either verbally or visually. The user acts according to this suggestion and inputs the result as feedback into the terminal, allowing the feedback processing means to improve the accuracy of future suggestions.

[0195] For example, if the server determines that a customer is feeling stressed, it generates a suggestion such as "Take a short break in the relaxation area" and notifies the user via their device. When the user actually takes action and provides feedback on their experience, the suggestions for future visits become even more personalized.

[0196] An example of a prompt to input into the generating AI model is, "What action suggestions would you come up with to help a customer find a way to relax in the store?" In this way, the system can improve the user experience in physical stores.

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

[0198] Step 1:

[0199] The device collects biometric information. The device's heart rate sensor, temperature sensor, camera, and microphone continuously acquire the user's heart rate, body temperature, facial expressions, and voice, and this data is stored as primary data.

[0200] Step 2:

[0201] The device transmits the collected biometric information to the server. Using wireless communication, the primary data is transferred to the server in real time. During this process, the data format is converted to a format suitable for processing on the server.

[0202] Step 3:

[0203] The server analyzes biometric information. It applies machine learning algorithms to the received data to analyze the user's emotional state. Specifically, a generative AI model is used to convert facial expressions and voice data into emotion labels, and changes in heart rate and body temperature are evaluated as stress levels.

[0204] Step 4:

[0205] The server generates suggestions based on the analysis results. Based on emotional state and stress levels, the suggestion generation system constructs actions such as relaxation techniques or guidance to specific areas within a store. In this process, past data history is referenced to personalize the suggestions.

[0206] Step 5:

[0207] The server notifies the terminal of the generated suggestion. It sends the suggestion content to the terminal as a text or voice message. The terminal prompts the user to take action by presenting the message to the user through a voice output device or display.

[0208] Step 6:

[0209] Users act according to the suggestions they receive. They perform the suggested actions in a physical store and provide feedback by entering the results of their experience into a terminal.

[0210] Step 7:

[0211] The device sends feedback to the server. The feedback data received from the user is transferred to the server, conveying the effectiveness of the suggestion and the user's evaluation. This data is analyzed on the server and reflected in future suggestions.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is a comprehensive system for maintaining the mental health of users, and it continuously collects the user's biometric information using multiple sensors. Specifically, the terminal is equipped with various sensors for acquiring heart rate, body temperature, facial expressions, and voice, and these sensors acquire the user's biometric information in real time.

[0229] The acquired biometric information is temporarily stored on the device and simultaneously encrypted and securely transmitted to the server. The server decrypts the received data and uses various processing methods to analyze the emotional state. Here, an AI algorithm is used to identify fluctuations in heart rate, voice tone, and changes in facial expression to evaluate the user's mental state.

[0230] For example, if the server's analysis detects that a user is showing significant stress during a specific time period or situation, the suggestion generation system will create specific action suggestions for the user, such as "It would be good to take a short break and drink some water." These suggestions are then communicated to the user via an audio output device on the terminal.

[0231] Furthermore, user feedback is collected via voice or the device interface and analyzed by a feedback processing system. This allows the system to continuously adjust to provide more optimized suggestions for individual users. This process effectively supports the prevention and early detection of users' mental health issues.

[0232] As a concrete example demonstrating the advantages of this system, if a user feels stressed after a meeting, fluctuations in their heart rate are detected, and the server suggests a 5-minute break. After the break, improved productivity is confirmed, and the user submits feedback on its effectiveness. This feedback is then used to improve future suggestions, enabling more personalized responses.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The device acquires the user's biometric information using various sensors. The heart rate sensor continuously monitors the heart rate, the camera periodically captures the user's facial expressions, and the microphone records the tone and content of their voice. This data is collected at regular intervals and stored in storage.

[0236] Step 2:

[0237] The terminal preprocesses the collected biometric information, performing noise reduction and data formatting, then encrypts it using a security protocol before sending it to the server. To achieve real-time data transfer, the data is delivered to the server in streaming format.

[0238] Step 3:

[0239] The server receives data transmitted from the terminal and performs analysis using processing tools. Utilizing AI algorithms, it extracts patterns indicating emotional states from biometric data and evaluates the user's current mental state. It also compares this data with past data to identify signs of stress or abnormalities.

[0240] Step 4:

[0241] Based on the processing results, the server generates action suggestions for the user using a suggestion generation mechanism. These suggestions include specific actions that help improve the user's mental state and are customizable.

[0242] Step 5:

[0243] The terminal receives suggestions from the server and notifies the user using an audio output device. The suggestions are adjusted according to the time and situation and converted into audio in a format that is easy for the user to understand.

[0244] Step 6:

[0245] The user chooses whether or not to act on the suggestion and inputs the result as feedback into the device. Feedback can be entered using voice input or touch gestures and sent back to the system.

[0246] Step 7:

[0247] The server analyzes user feedback and uses it to improve future suggestions through feedback processing mechanisms. User behavior history and feedback data are used to provide individually optimized solutions and improve the accuracy of the system.

[0248] (Example 1)

[0249] 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."

[0250] In modern society, maintaining individual mental health is a crucial issue, and stress management in daily life is a central theme. However, there is no system that provides individualized approaches tailored to each user, offers appropriate behavioral suggestions in real time, and continuously improves their effectiveness through feedback. This invention aims to provide comprehensive and flexible support for improving and maintaining users' mental health.

[0251] 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.

[0252] In this invention, the server includes processing means using artificial intelligence to decode information and analyze emotional states, suggestion generation means that propose specific actions to the user based on the analysis results, and feedback improvement means that collect user feedback and optimize the suggestions based on that feedback. This makes it possible to provide personalized, real-time action suggestions and improve the quality of the suggestions based on user feedback.

[0253] A "sensor unit" is a hardware component used to acquire a user's biometric data, and it has the function of detecting heart rate, temperature, facial expressions, voice, etc., in real time.

[0254] A "data management means" is a part of a system configuration that has the function of temporarily storing acquired biometric information and then encrypting and securely storing that information.

[0255] "Communication methods" refer to a set of infrastructure and protocols used to transmit encrypted data from a terminal to a server, enabling secure and efficient data transfer.

[0256] "Processing methods using artificial intelligence" refers to algorithms and their implementations used to analyze decoded data on a server and evaluate the user's emotional state.

[0257] A "proposal generation means" is a part of a system configuration that has the function of automatically generating specific action suggestions for the user based on the results of the analyzed emotional state.

[0258] "Notification means" refers to devices or methods that transmit generated action suggestions to the user via voice or other means, enabling the user to receive the suggestions immediately.

[0259] "Feedback improvement methods" are technical elements that improve the accuracy and effectiveness of proposals by analyzing feedback collected from users and reflecting the results in future proposals.

[0260] This invention is an information processing system for maintaining and improving the mental health of users. It acquires and analyzes the user's biometric data using various sensor units and provides individually adapted behavioral suggestions.

[0261] The device is equipped with multiple sensor units that capture heart rate, body temperature, facial expressions, and voice. These sensors collect the user's real-time biometric data, and this information is temporarily stored and encrypted by a data management system. For example, AES encryption technology is used in data management.

[0262] The device securely transmits encrypted biometric data to the server via a communication method. HTTPS is used as the communication protocol to ensure data integrity and privacy.

[0263] The server decodes the received data and evaluates the emotional state using artificial intelligence processing. Specifically, it utilizes a generative AI model to analyze heart rate variability, voice tone, and changes in facial expressions to determine the user's stress level and other factors.

[0264] Based on the analysis results, the server automatically generates appropriate action suggestions for the user using a suggestion generation mechanism. These suggestions are sent to the terminal via a notification mechanism, and the terminal notifies the user through an audio output device.

[0265] After implementing a suggested action, the user provides feedback on its effectiveness. This feedback is collected via voice or a terminal interface and analyzed by feedback improvement tools. This improves the accuracy of future suggestions.

[0266] For example, if a user experiences stress after a meeting and fluctuations in their heart rate are detected, the server suggests taking a 5-minute break. If the user then finds the break effective, this feedback is used to inform future suggestions.

[0267] An example of a prompt is, "What should be suggested when the user's heart rate is higher than normal?" This prompts the generative AI model to analyze and suggest a response based on the user's state.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The device uses a sensor unit to collect the user's heart rate, body temperature, facial expressions, and voice data in real time. The input is the user's biometric data, and the output is this data transmitted to the device through the sensor interface.

[0271] Step 2:

[0272] The device temporarily stores the collected biometric data in its internal storage and then encrypts the data using AES encryption technology. The input is the collected biometric data, and the output is the encrypted data, which is prepared for the next step.

[0273] Step 3:

[0274] The device sends encrypted data to the server via a secure communication protocol (HTTPS). The input is encrypted biometric data, and the output is data sent to the server.

[0275] Step 4:

[0276] The server receives the data and decrypts it using a pre-configured key. The input is encrypted data, and the output is decrypted biometric data. Based on this, the server is ready to perform the next processing step.

[0277] Step 5:

[0278] The server applies a generative AI model to the decoded biometric data, analyzing heart rate, voice tone, and facial expression changes to evaluate the user's emotional state. The input is the decoded biometric data, and the output is an indicator of the analyzed emotional state.

[0279] Step 6:

[0280] The server generates appropriate action suggestions for the user using a suggestion generation mechanism based on the analyzed data. The input is an indicator of the user's emotional state, and the output is the action suggestion.

[0281] Step 7:

[0282] The terminal notifies the user of the action proposal received from the server through the voice output device. The input is the action proposal, and the output is the proposal notification to the user.

[0283] Step 8:

[0284] The user implements an action based on the proposal and provides feedback on the result through voice or an interface. The input is the user's reaction to the proposal, and the output is the feedback information.

[0285] Step 9:

[0286] The server analyzes the feedback from the user and reflects it in the next proposal through the feedback improvement means. The input is the feedback information, and the output is the improved proposal content.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In the modern consumption environment, it is required to grasp the emotional needs and stresses of consumers in real time and provide customer service optimized individually. However, in the conventional system, it is difficult to provide such individual responses, and there is a problem that the satisfaction of consumers cannot be sufficiently improved. In particular, in a virtual store, it is difficult to grasp the emotions and stress levels of consumers because their faces cannot be seen, and a new approach is needed to improve the quality of the consumer experience.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes at least one detection means for collecting biometric information, an information processing means for processing the data collected by the detection means and analyzing the user's emotional state, and a service adjustment means for adjusting the service method using the analyzed data. This makes it possible to provide personalized service that responds to the user's real-time stress level and emotional state.

[0292] "Biometric information" refers to data about the user's physical and mental state, particularly circulatory activity, body temperature, facial expression changes, and acoustics.

[0293] "Detection means" refers to a device or sensor for acquiring biometric information from a user.

[0294] "Information processing means" refers to a device or system that has the function of analyzing biometric information collected by detection means and evaluating the emotional state of the user.

[0295] A "proposal formation means" refers to a system that, based on the analysis results of information processing means, presents the most appropriate action for the user.

[0296] "Information transmission means" refers to a system that transmits information generated by proposal formation means to users.

[0297] "Adjustment processing means" refers to a system that collects responses from users and adjusts them while considering the feedback in order to generate more optimized suggestions.

[0298] "Customer service adjustment means" refers to a function that uses analytical data from information processing means to appropriately improve and adjust customer service methods.

[0299] A system for implementing this invention comprises a detection means for collecting biological information, an information processing means for analyzing the collected information, a proposal formation means for generating proposals based on the analysis results, an information transmission means for communicating the generated proposals to the user, an adjustment processing means for collecting user feedback and optimizing the next proposal, and a customer service adjustment means for adjusting the customer service method.

[0300] The server receives circulatory activity, body temperature, facial expression changes, and acoustic data transmitted from the detection means, and analyzes this data using information processing means. The analysis is performed using AI algorithms such as TensorFlow and PyTorch to evaluate the user's emotional state.

[0301] After the analysis results are obtained, the suggestion generation system generates optimal action instructions for the user. For example, if the system determines that the user is in a stressful state, it might suggest "relaxing activities." This suggestion is then communicated to the user using an acoustic information transmission system.

[0302] Furthermore, the terminal collects feedback from users and sends it to the server. The server analyzes this feedback using an adjustment processing mechanism and makes adjustments to help generate future suggestions. At the same time, the customer service adjustment mechanism improves the customer service method by reflecting the analyzed data.

[0303] As a concrete example, consider a situation where a user who visited a virtual store on a holiday experiences increased cardiovascular activity and stress is detected. In this case, the AI ​​system can suggest "relaxing products that might be good to try" along with calming music.

[0304] An example of a prompt to input into the generation AI model is: "Create a prompt that generates relaxation suggestions when the user's heart rate is elevated and their facial expression shows signs of tension."

[0305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0306] Step 1:

[0307] The terminal collects biometric information from the user in real time. Specifically, it uses a heart rate sensor, a temperature sensor, a camera, and a microphone to detect the heart rate, body temperature, facial expression, and voice. The input is the raw data from each sensor, and the output is the digital data that combines this.

[0308] Step 2:

[0309] The terminal temporarily stores the collected digital data locally and encrypts and transmits it to the server via a secure communication protocol. The input is the digital data of the biometric information generated in Step 1, and the output is the secure data transmission to the server.

[0310] Step 3:

[0311] The server accepts the received data and analyzes the data using information processing means. Here, a generative AI model such as TensorFlow or PyTorch is used to determine the user's emotional state. The input is the encrypted digital data, and the output is the analysis result representing the user's emotional state.

[0312] Step 4:

[0313] Based on the analysis result, the server uses proposal formation means to generate an optimal proposal for the user. The prompt sentence is set according to the analysis result, and a specific action guideline such as "propose relaxation activities" is generated by the generative AI model. The input is the analysis result of the emotional state, and the output is the specific action proposal.

[0314] Step 5:

[0315] The terminal receives the proposal sent from the server and notifies the user using information transmission means. The operation here is to transmit the notification content to the user using a voice output device or the like. The input is the proposal content from the server, and the output is the voice notification to the user.

[0316] Step 6:

[0317] Users send feedback on suggestions to the server via their device. This feedback is collected through voice and the user interface. The input is user feedback, and the output is data sent to the server.

[0318] Step 7:

[0319] The server analyzes the received feedback and uses adjustment processing to improve the algorithm for future proposal generation. Simultaneously, it uses customer service adjustment to improve customer service methods. The input is user feedback, and the output is the optimized proposal generation process.

[0320] 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.

[0321] This invention is a system that effectively maintains and manages a user's mental health, and proposes the optimal action for the user by comprehensively analyzing biometric information and emotions. This system acquires biometric information via a terminal equipped with multiple sensor means. Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the terminal continuously collect heart rate, body temperature, facial expression, and voice information.

[0322] By incorporating an emotion engine, the server performs detailed emotional analysis on this biometric information. The emotion engine utilizes machine learning algorithms to accurately recognize the user's emotions from acquired voice and facial expression data. In this process, it also refers to the user's long-term data history and performs trend analysis to understand the trends in emotional fluctuations.

[0323] For example, if a user shows signs of stress more frequently than usual in their daily activities, the emotion engine quickly detects this change and collects data to provide appropriate behavioral suggestions. These suggestions are then specifically customized depending on the situation, such as "try a particular relaxation technique" or "practice a short meditation."

[0324] The suggestion generation system automatically creates useful and specific action suggestions for the user based on information from analysis and the emotion engine. These suggestions are communicated to the user via the terminal's voice output device. The user selects an action based on the suggestion and provides feedback to the terminal regarding the result and the effectiveness of the suggestion. This feedback is analyzed by the server and used by the feedback processing system to improve the accuracy of future suggestions.

[0325] Thus, the present invention provides support for users to actively engage in managing their emotions and improving their mental health, and the combination of emotion engines enables more accurate and personalized services.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The device uses multiple sensors to collect the user's biometric information. Specifically, a heart rate sensor records heart rate, a temperature sensor records body temperature, a camera captures facial expressions, and a microphone records voice, and this data is collected in real time.

[0329] Step 2:

[0330] The terminal temporarily stores the collected biometric information and performs noise reduction as a preprocessing step. After preprocessing, the data is encrypted and transmitted to the server using a secure communication method.

[0331] Step 3:

[0332] The server receives data sent from the terminal and analyzes the emotional state using an emotion engine. Voice and facial expression data are labeled with emotions using machine learning algorithms, and indicators of high stress or anxiety are identified.

[0333] Step 4:

[0334] The server investigates the user's long-term data history and analyzes trends in their current emotional state. This makes it possible to monitor increasing stress levels and abnormal emotional fluctuations in real time.

[0335] Step 5:

[0336] The suggestion generation mechanism proposes appropriate actions to the user based on the aforementioned analysis results. The suggested content is customized according to the emotional state recognized by the engine and includes specific actions such as "take a deep breath" or "take a 30-minute break."

[0337] Step 6:

[0338] The terminal notifies the user of suggestions generated by the suggestion generation mechanism via an audio output device. The suggestions are delivered appropriately at a time that does not interrupt the user's focus.

[0339] Step 7:

[0340] Users evaluate the effectiveness of the actions taken based on the received suggestions and provide feedback to their device. This feedback is provided via voice input or touch gestures and is used for subsequent analysis and suggestion generation.

[0341] Step 8:

[0342] The server analyzes user feedback and uses it in feedback processing to improve the accuracy of future suggestions. This process ensures that suggestions provided to users become increasingly tailored to their individual needs.

[0343] (Example 2)

[0344] 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".

[0345] In modern society, maintaining and improving users' mental health is a crucial issue, but there are limited systems capable of comprehensively analyzing users' biometric data and suggesting appropriate actions. Existing methods have the problem of difficulty in grasping subtle fluctuations in users' emotions and physical condition in real time and providing appropriate feedback. Therefore, there is a need to analyze users' biometric information and emotions with high accuracy and provide personalized suggestions that meet individual needs.

[0346] 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.

[0347] In this invention, the server includes a measuring device for collecting biometric data, a processing device for processing the information collected by the measuring device and analyzing the user's emotional state with high accuracy, and a suggestion generation device for suggesting actions based on the analysis and the user's long-term data history. This makes it possible to analyze the user's biometric information in real time and to quickly and appropriately suggest specific actions necessary to maintain and improve the user's mental health.

[0348] "Biometric data" refers to information that indicates the user's physical condition, including heart rate, body temperature, changes in facial expression, and voice information.

[0349] A "measuring device" is a device used to collect biological data and is equipped with various sensors.

[0350] A "processing device" is a device that analyzes biological data obtained from measuring devices and makes highly accurate judgments about the user's emotional state.

[0351] A "proposal generation device" is a device that proposes actions to users based on the analysis results from the processing device and the user's long-term data history.

[0352] An "output device" is a device that notifies the user of suggestions from the suggestion generation device, and conveys information through visual or auditory means.

[0353] A "feedback processing device" is a device that collects feedback from users, analyzes it, and uses that feedback to improve the accuracy of future suggestions.

[0354] This invention is a system that comprehensively analyzes a user's biometric information and emotional state and provides appropriate behavioral suggestions. The system comprises multiple measuring devices, processing devices, suggestion generation devices, feedback processing devices, and output devices.

[0355] Hardware and software

[0356] The device continuously collects biometric data such as heart rate, body temperature, facial expression changes, and voice information through measuring devices such as smartwatches and smartphones. This data is analyzed by a processing unit, and sentiment analysis is performed using machine learning algorithms. This analysis accurately determines the user's emotional state and also takes into account the long-term history of the data. Based on these analysis results, the server configures a suggestion generation device to propose the most appropriate action to the user.

[0357] Specific example

[0358] For example, if a user experiences a lot of stress in their daily life, the server will suggest relaxation methods or meditation based on the results of an emotion analysis. The suggestions are notified to the user via an audio output device, so the user can receive notifications on their smartphone or smart speaker. By inputting a prompt message such as, "Please suggest effective relaxation methods when the user wants to relax. Please refer to the current heart rate and voice tone," into the AI ​​model, the suggestion generator will derive specific actions based on that message.

[0359] Furthermore, user feedback is collected by a feedback processing device and used to improve the accuracy of future suggestions. This allows the system to provide more personalized and suitable suggestions to the user over time, thereby contributing to the user's mental well-being.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The device collects biometric data such as the user's heart rate, body temperature, facial expression changes, and voice information through measuring devices. This allows for the accumulation of real-time biometric data. The input biometric data is temporarily stored within the device for further processing. Specifically, the smartwatch measures the heart rate, and the smartphone captures audio and video.

[0363] Step 2:

[0364] The device transmits the collected biometric data to the server. This involves encrypting the data using a secure communication protocol and transmitting it to the server over the network. The input is the biometric data transmitted from the device, and the server generates output to receive it. Specifically, the data is uploaded to the server in real time using Wi-Fi or mobile data communication.

[0365] Step 3:

[0366] The server analyzes the received biometric data using a processing unit. The emotion engine uses machine learning algorithms to analyze the user's emotional state from the biometric data with high accuracy. The input is the biometric data sent to the server, and the output is the analysis result indicating the user's emotional state. Specifically, it performs voice tone analysis and facial expression recognition to determine stress levels and relaxation levels.

[0367] Step 4:

[0368] The server uses a suggestion generation device to create action suggestions for the user based on the analysis results. Considering the analysis results and the user's long-term history, it inputs appropriate prompt sentences into a generation AI model to create personalized suggestions. The input is the analysis results and user history, and the output is specific action suggestions. For example, a suggestion such as "You can reduce stress by taking deep breaths" might be generated.

[0369] Step 5:

[0370] Action suggestions generated by the suggestion generation device are communicated to the user via an output device on the terminal. Notifications are made via voice or display, presented in a way that is easily understandable to the user. The input is the generated action suggestion, and the output is the notification to the user. For example, a smart speaker might announce, "Try a short meditation session."

[0371] Step 6:

[0372] The user performs the suggested action and provides feedback on the result to the device. This feedback is provided via button presses or voice input and sent to the server by a feedback processing unit. The input is the user's feedback, and the output is the feedback data. Specifically, feedback might include statements like, "The suggested meditation helped reduce stress."

[0373] Step 7:

[0374] The server uses a feedback processing unit to analyze user feedback. This analysis enables a learning process that improves the accuracy of future suggestions. The input is user feedback data, and the output is an updated suggestion model and analysis algorithm. This improves the system so that it can provide suggestions that are more suitable for the user.

[0375] (Application Example 2)

[0376] 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 as the "terminal".

[0377] In today's world, consumer shopping experiences are diversifying, and improving customer experience in this environment is essential. Especially in physical stores, personalized service based on the customer's emotions and mental state is crucial, but achieving this is difficult. Therefore, the challenge lies in providing a better shopping experience by utilizing the customer's biometric information to offer personalized suggestions.

[0378] 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.

[0379] In this invention, the server includes a plurality of sensor means for collecting biometric information, a processing means for processing the data collected by the sensor means and analyzing the user's emotional state, a suggestion generation means for proposing appropriate actions to the user based on the analysis, a notification means for notifying the user of the suggestions from the suggestion generation means, a feedback processing means for collecting user feedback and improving the next suggestion considering the feedback, and a presentation means for providing the user with an action to execute the suggestion generated by the suggestion generation means. This enables personalized suggestions in real time according to the biometric information of store visitors, providing a comfortable shopping experience in physical stores.

[0380] "Biometric information" refers to data that indicates a person's physical and emotional state, such as heart rate, body temperature, facial expressions, and voice.

[0381] "Sensor means" refers to devices or equipment used to acquire biological information.

[0382] "Processing means" refers to software or hardware functions for analyzing the user's emotional state from collected biometric information.

[0383] "Proposal generation means" refers to a device that handles the process of constructing and generating actions and choices suitable for the user based on the analyzed emotional state.

[0384] "Notification means" refers to an output device in the form of audio, visual, or other means that informs the user of the proposal generated by the proposal generation means.

[0385] "Feedback processing means" refers to a process or device that collects responses from users and uses them to improve the accuracy of suggestions.

[0386] "Presentation means" refers to methods or devices that allow the user to concretely perform the actions provided by the proposal generation means.

[0387] In this embodiment of the invention, a system is used that collects and analyzes a user's biometric information and provides appropriate action suggestions. The server receives data acquired from a terminal equipped with multiple sensor means and performs emotion analysis using a machine learning algorithm. Based on this analysis, the user's current emotional state is determined, and the suggestion generation means constructs specific actions.

[0388] Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the device continuously collect biometric information and transmit this data wirelessly to a server. The server processes this data using Python and utilizes an emotion engine to recognize the user's emotions with high accuracy. Emotional fluctuation trends are analyzed by referring to long-term data history.

[0389] Based on the analysis results, the suggestion generation means generates appropriate actions according to the user's state. Subsequently, the action suggestion is provided to the user via a notification means, either verbally or visually. The user acts according to this suggestion and inputs the result as feedback into the terminal, allowing the feedback processing means to improve the accuracy of future suggestions.

[0390] For example, if the server determines that a customer is feeling stressed, it generates a suggestion such as "Take a short break in the relaxation area" and notifies the user via their device. When the user actually takes action and provides feedback on their experience, the suggestions for future visits become even more personalized.

[0391] An example of a prompt to input into the generating AI model is, "What action suggestions would you come up with to help a customer find a way to relax in the store?" In this way, the system can improve the user experience in physical stores.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The device collects biometric information. The device's heart rate sensor, temperature sensor, camera, and microphone continuously acquire the user's heart rate, body temperature, facial expressions, and voice, and this data is stored as primary data.

[0395] Step 2:

[0396] The device transmits the collected biometric information to the server. Using wireless communication, the primary data is transferred to the server in real time. During this process, the data format is converted to a format suitable for processing on the server.

[0397] Step 3:

[0398] The server analyzes biometric information. It applies machine learning algorithms to the received data to analyze the user's emotional state. Specifically, a generative AI model is used to convert facial expressions and voice data into emotion labels, and changes in heart rate and body temperature are evaluated as stress levels.

[0399] Step 4:

[0400] The server generates suggestions based on the analysis results. Based on emotional state and stress levels, the suggestion generation system constructs actions such as relaxation techniques or guidance to specific areas within a store. In this process, past data history is referenced to personalize the suggestions.

[0401] Step 5:

[0402] The server notifies the terminal of the generated suggestion. It sends the suggestion content to the terminal as a text or voice message. The terminal prompts the user to take action by presenting the message to the user through a voice output device or display.

[0403] Step 6:

[0404] Users act according to the suggestions they receive. They perform the suggested actions in a physical store and provide feedback by entering the results of their experience into a terminal.

[0405] Step 7:

[0406] The device sends feedback to the server. The feedback data received from the user is transferred to the server, conveying the effectiveness of the suggestion and the user's evaluation. This data is analyzed on the server and reflected in future suggestions.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention is a comprehensive system for maintaining the mental health of users, and it continuously collects the user's biometric information using multiple sensors. Specifically, the terminal is equipped with various sensors for acquiring heart rate, body temperature, facial expressions, and voice, and these sensors acquire the user's biometric information in real time.

[0424] The acquired biometric information is temporarily stored on the device and simultaneously encrypted and securely transmitted to the server. The server decrypts the received data and uses various processing methods to analyze the emotional state. Here, an AI algorithm is used to identify fluctuations in heart rate, voice tone, and changes in facial expression to evaluate the user's mental state.

[0425] For example, if the server's analysis detects that a user is showing significant stress during a specific time period or situation, the suggestion generation system will create specific action suggestions for the user, such as "It would be good to take a short break and drink some water." These suggestions are then communicated to the user via an audio output device on the terminal.

[0426] Furthermore, user feedback is collected via voice or the device interface and analyzed by a feedback processing system. This allows the system to continuously adjust to provide more optimized suggestions for individual users. This process effectively supports the prevention and early detection of users' mental health issues.

[0427] As a concrete example demonstrating the advantages of this system, if a user feels stressed after a meeting, fluctuations in their heart rate are detected, and the server suggests a 5-minute break. After the break, improved productivity is confirmed, and the user submits feedback on its effectiveness. This feedback is then used to improve future suggestions, enabling more personalized responses.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The device acquires the user's biometric information using various sensors. The heart rate sensor continuously monitors the heart rate, the camera periodically captures the user's facial expressions, and the microphone records the tone and content of their voice. This data is collected at regular intervals and stored in storage.

[0431] Step 2:

[0432] The terminal preprocesses the collected biometric information, performing noise reduction and data formatting, then encrypts it using a security protocol before sending it to the server. To achieve real-time data transfer, the data is delivered to the server in streaming format.

[0433] Step 3:

[0434] The server receives data transmitted from the terminal and performs analysis using processing tools. Utilizing AI algorithms, it extracts patterns indicating emotional states from biometric data and evaluates the user's current mental state. It also compares this data with past data to identify signs of stress or abnormalities.

[0435] Step 4:

[0436] Based on the processing results, the server generates action suggestions for the user using a suggestion generation mechanism. These suggestions include specific actions that help improve the user's mental state and are customizable.

[0437] Step 5:

[0438] The terminal receives suggestions from the server and notifies the user using an audio output device. The suggestions are adjusted according to the time and situation and converted into audio in a format that is easy for the user to understand.

[0439] Step 6:

[0440] The user chooses whether or not to act on the suggestion and inputs the result as feedback into the device. Feedback can be entered using voice input or touch gestures and sent back to the system.

[0441] Step 7:

[0442] The server analyzes user feedback and uses it to improve future suggestions through feedback processing mechanisms. User behavior history and feedback data are used to provide individually optimized solutions and improve the accuracy of the system.

[0443] (Example 1)

[0444] 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."

[0445] In modern society, maintaining individual mental health is a crucial issue, and stress management in daily life is a central theme. However, there is no system that provides individualized approaches tailored to each user, offers appropriate behavioral suggestions in real time, and continuously improves their effectiveness through feedback. This invention aims to provide comprehensive and flexible support for improving and maintaining users' mental health.

[0446] 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.

[0447] In this invention, the server includes processing means using artificial intelligence to decode information and analyze emotional states, suggestion generation means that propose specific actions to the user based on the analysis results, and feedback improvement means that collect user feedback and optimize the suggestions based on that feedback. This makes it possible to provide personalized, real-time action suggestions and improve the quality of the suggestions based on user feedback.

[0448] A "sensor unit" is a hardware component used to acquire a user's biometric data, and it has the function of detecting heart rate, temperature, facial expressions, voice, etc., in real time.

[0449] A "data management means" is a part of a system configuration that has the function of temporarily storing acquired biometric information and then encrypting and securely storing that information.

[0450] "Communication methods" refer to a set of infrastructure and protocols used to transmit encrypted data from a terminal to a server, enabling secure and efficient data transfer.

[0451] "Processing methods using artificial intelligence" refers to algorithms and their implementations used to analyze decoded data on a server and evaluate the user's emotional state.

[0452] A "proposal generation means" is a part of a system configuration that has the function of automatically generating specific action suggestions for the user based on the results of the analyzed emotional state.

[0453] "Notification means" refers to devices or methods that transmit generated action suggestions to the user via voice or other means, enabling the user to receive the suggestions immediately.

[0454] "Feedback improvement methods" are technical elements that improve the accuracy and effectiveness of proposals by analyzing feedback collected from users and reflecting the results in future proposals.

[0455] This invention is an information processing system for maintaining and improving the mental health of users. It acquires and analyzes the user's biometric data using various sensor units and provides individually adapted behavioral suggestions.

[0456] The device is equipped with multiple sensor units that capture heart rate, body temperature, facial expressions, and voice. These sensors collect the user's real-time biometric data, and this information is temporarily stored and encrypted by a data management system. For example, AES encryption technology is used in data management.

[0457] The device securely transmits encrypted biometric data to the server via a communication method. HTTPS is used as the communication protocol to ensure data integrity and privacy.

[0458] The server decodes the received data and evaluates the emotional state using artificial intelligence processing. Specifically, it utilizes a generative AI model to analyze heart rate variability, voice tone, and changes in facial expressions to determine the user's stress level and other factors.

[0459] Based on the analysis results, the server automatically generates appropriate action suggestions for the user using a suggestion generation mechanism. These suggestions are sent to the terminal via a notification mechanism, and the terminal notifies the user through an audio output device.

[0460] After implementing a suggested action, the user provides feedback on its effectiveness. This feedback is collected via voice or a terminal interface and analyzed by feedback improvement tools. This improves the accuracy of future suggestions.

[0461] For example, if a user experiences stress after a meeting and fluctuations in their heart rate are detected, the server suggests taking a 5-minute break. If the user then finds the break effective, this feedback is used to inform future suggestions.

[0462] An example of a prompt is, "What should be suggested when the user's heart rate is higher than normal?" This prompts the generative AI model to analyze and suggest a response based on the user's state.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The device uses a sensor unit to collect the user's heart rate, body temperature, facial expressions, and voice data in real time. The input is the user's biometric data, and the output is this data transmitted to the device through the sensor interface.

[0466] Step 2:

[0467] The device temporarily stores the collected biometric data in its internal storage and then encrypts the data using AES encryption technology. The input is the collected biometric data, and the output is the encrypted data, which is prepared for the next step.

[0468] Step 3:

[0469] The device sends encrypted data to the server via a secure communication protocol (HTTPS). The input is encrypted biometric data, and the output is data sent to the server.

[0470] Step 4:

[0471] The server receives the data and decrypts it using a pre-configured key. The input is encrypted data, and the output is decrypted biometric data. Based on this, the server is ready to perform the next processing step.

[0472] Step 5:

[0473] The server applies a generative AI model to the decoded biometric data, analyzing heart rate, voice tone, and facial expression changes to evaluate the user's emotional state. The input is the decoded biometric data, and the output is an indicator of the analyzed emotional state.

[0474] Step 6:

[0475] The server generates appropriate action suggestions for the user using a suggestion generation mechanism based on the analyzed data. The input is an indicator of the user's emotional state, and the output is the action suggestion.

[0476] Step 7:

[0477] The terminal notifies the user of action suggestions received from the server via an audio output device. The input is the action suggestion, and the output is the suggestion notification to the user.

[0478] Step 8:

[0479] The user takes action based on the suggestion and provides feedback on the results via voice or an interface. The input is the user's response to the suggestion, and the output is the feedback information.

[0480] Step 9:

[0481] The server analyzes user feedback and incorporates it into future suggestions through feedback improvement mechanisms. The input is feedback information, and the output is the improved suggestion.

[0482] (Application Example 1)

[0483] 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."

[0484] In today's consumer environment, there is a demand for real-time understanding of consumers' emotional needs and stress levels, and the provision of individually optimized customer service. However, conventional systems have struggled to provide such individualized support, resulting in challenges in fully increasing consumer satisfaction. In particular, in virtual stores, it is difficult to grasp consumers' emotions and stress levels because their faces are not visible, and a new approach is needed to improve the quality of the consumer experience.

[0485] 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.

[0486] In this invention, the server includes at least one detection means for collecting biometric information, an information processing means for processing the data collected by the detection means and analyzing the user's emotional state, and a service adjustment means for adjusting the service method using the analyzed data. This makes it possible to provide personalized service that responds to the user's real-time stress level and emotional state.

[0487] "Biometric information" refers to data about the user's physical and mental state, particularly circulatory activity, body temperature, facial expression changes, and acoustics.

[0488] "Detection means" refers to a device or sensor for acquiring biometric information from a user.

[0489] "Information processing means" refers to a device or system that has the function of analyzing biometric information collected by detection means and evaluating the emotional state of the user.

[0490] A "proposal formation means" refers to a system that, based on the analysis results of information processing means, presents the most appropriate action for the user.

[0491] "Information transmission means" refers to a system that transmits information generated by proposal formation means to users.

[0492] "Adjustment processing means" refers to a system that collects responses from users and adjusts them while considering the feedback in order to generate more optimized suggestions.

[0493] "Customer service adjustment means" refers to a function that uses analytical data from information processing means to appropriately improve and adjust customer service methods.

[0494] A system for implementing this invention comprises a detection means for collecting biological information, an information processing means for analyzing the collected information, a proposal formation means for generating proposals based on the analysis results, an information transmission means for communicating the generated proposals to the user, an adjustment processing means for collecting user feedback and optimizing the next proposal, and a customer service adjustment means for adjusting the customer service method.

[0495] The server receives circulatory activity, body temperature, facial expression changes, and acoustic data transmitted from the detection means, and analyzes this data using information processing means. The analysis is performed using AI algorithms such as TensorFlow and PyTorch to evaluate the user's emotional state.

[0496] After the analysis results are obtained, the suggestion generation system generates optimal action instructions for the user. For example, if the system determines that the user is in a stressful state, it might suggest "relaxing activities." This suggestion is then communicated to the user using an acoustic information transmission system.

[0497] Furthermore, the terminal collects feedback from users and sends it to the server. The server analyzes this feedback using an adjustment processing mechanism and makes adjustments to help generate future suggestions. At the same time, the customer service adjustment mechanism improves the customer service method by reflecting the analyzed data.

[0498] As a concrete example, consider a situation where a user who visited a virtual store on a holiday experiences increased cardiovascular activity and stress is detected. In this case, the AI ​​system can suggest "relaxing products that might be good to try" along with calming music.

[0499] An example of a prompt to input into the generation AI model is: "Create a prompt that generates relaxation suggestions when the user's heart rate is elevated and their facial expression shows signs of tension."

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The device collects biometric information from the user in real time. Specifically, it uses a heart rate sensor, temperature sensor, camera, and microphone to detect heart rate, body temperature, facial expressions, and voice. The input is raw data from each sensor, and the output is digital data compiled from this data.

[0503] Step 2:

[0504] The terminal temporarily stores the collected digital data locally and then encrypts and transmits it to the server via a secure communication protocol. The input is the digital data of biometric information generated in step 1, and the output is the secure transmission of data to the server.

[0505] Step 3:

[0506] The server receives the incoming data and analyzes it using information processing tools. Here, generative AI models such as TensorFlow and PyTorch are used to determine the user's emotional state. The input is encrypted digital data, and the output is the analysis result representing the user's emotional state.

[0507] Step 4:

[0508] Based on the analysis results, the server generates optimal suggestions for the user using a suggestion formation mechanism. Prompt messages are set according to the analysis results, and the AI ​​generation model generates specific action guidelines such as "suggest relaxation activities." The input is the analysis results of the emotional state, and the output is specific action suggestions.

[0509] Step 5:

[0510] The terminal receives a suggestion sent from the server and notifies the user using an information transmission method. The operation here involves transmitting the notification content to the user using an audio output device or similar. The input is the suggestion content from the server, and the output is an audio notification to the user.

[0511] Step 6:

[0512] Users send feedback on suggestions to the server via their device. This feedback is collected through voice and the user interface. The input is user feedback, and the output is data sent to the server.

[0513] Step 7:

[0514] The server analyzes the received feedback and uses adjustment processing to improve the algorithm for future proposal generation. Simultaneously, it uses customer service adjustment to improve customer service methods. The input is user feedback, and the output is the optimized proposal generation process.

[0515] 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.

[0516] This invention is a system that effectively maintains and manages a user's mental health, and proposes the optimal action for the user by comprehensively analyzing biometric information and emotions. This system acquires biometric information via a terminal equipped with multiple sensor means. Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the terminal continuously collect heart rate, body temperature, facial expression, and voice information.

[0517] By incorporating an emotion engine, the server performs detailed emotional analysis on this biometric information. The emotion engine utilizes machine learning algorithms to accurately recognize the user's emotions from acquired voice and facial expression data. In this process, it also refers to the user's long-term data history and performs trend analysis to understand the trends in emotional fluctuations.

[0518] For example, if a user shows signs of stress more frequently than usual in their daily activities, the emotion engine quickly detects this change and collects data to provide appropriate behavioral suggestions. These suggestions are then specifically customized depending on the situation, such as "try a particular relaxation technique" or "practice a short meditation."

[0519] The suggestion generation system automatically creates useful and specific action suggestions for the user based on information from analysis and the emotion engine. These suggestions are communicated to the user via the terminal's voice output device. The user selects an action based on the suggestion and provides feedback to the terminal regarding the result and the effectiveness of the suggestion. This feedback is analyzed by the server and used by the feedback processing system to improve the accuracy of future suggestions.

[0520] Thus, the present invention provides support for users to actively engage in managing their emotions and improving their mental health, and the combination of emotion engines enables more accurate and personalized services.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] The device uses multiple sensors to collect the user's biometric information. Specifically, a heart rate sensor records heart rate, a temperature sensor records body temperature, a camera captures facial expressions, and a microphone records voice, and this data is collected in real time.

[0524] Step 2:

[0525] The terminal temporarily stores the collected biometric information and performs noise reduction as a preprocessing step. After preprocessing, the data is encrypted and transmitted to the server using a secure communication method.

[0526] Step 3:

[0527] The server receives data sent from the terminal and analyzes the emotional state using an emotion engine. Voice and facial expression data are labeled with emotions using machine learning algorithms, and indicators of high stress or anxiety are identified.

[0528] Step 4:

[0529] The server investigates the user's long-term data history and analyzes trends in their current emotional state. This makes it possible to monitor increasing stress levels and abnormal emotional fluctuations in real time.

[0530] Step 5:

[0531] The suggestion generation mechanism proposes appropriate actions to the user based on the aforementioned analysis results. The suggested content is customized according to the emotional state recognized by the engine and includes specific actions such as "take a deep breath" or "take a 30-minute break."

[0532] Step 6:

[0533] The terminal notifies the user of suggestions generated by the suggestion generation mechanism via an audio output device. The suggestions are delivered appropriately at a time that does not interrupt the user's focus.

[0534] Step 7:

[0535] Users evaluate the effectiveness of the actions taken based on the received suggestions and provide feedback to their device. This feedback is provided via voice input or touch gestures and is used for subsequent analysis and suggestion generation.

[0536] Step 8:

[0537] The server analyzes user feedback and uses it in feedback processing to improve the accuracy of future suggestions. This process ensures that suggestions provided to users become increasingly tailored to their individual needs.

[0538] (Example 2)

[0539] 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."

[0540] In modern society, maintaining and improving users' mental health is a crucial issue, but there are limited systems capable of comprehensively analyzing users' biometric data and suggesting appropriate actions. Existing methods have the problem of difficulty in grasping subtle fluctuations in users' emotions and physical condition in real time and providing appropriate feedback. Therefore, there is a need to analyze users' biometric information and emotions with high accuracy and provide personalized suggestions that meet individual needs.

[0541] 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.

[0542] In this invention, the server includes a measuring device for collecting biometric data, a processing device for processing the information collected by the measuring device and analyzing the user's emotional state with high accuracy, and a suggestion generation device for suggesting actions based on the analysis and the user's long-term data history. This makes it possible to analyze the user's biometric information in real time and to quickly and appropriately suggest specific actions necessary to maintain and improve the user's mental health.

[0543] "Biometric data" refers to information that indicates the user's physical condition, including heart rate, body temperature, changes in facial expression, and voice information.

[0544] A "measuring device" is a device used to collect biological data and is equipped with various sensors.

[0545] A "processing device" is a device that analyzes biological data obtained from measuring devices and makes highly accurate judgments about the user's emotional state.

[0546] A "proposal generation device" is a device that proposes actions to users based on the analysis results from the processing device and the user's long-term data history.

[0547] An "output device" is a device that notifies the user of suggestions from the suggestion generation device, and conveys information through visual or auditory means.

[0548] A "feedback processing device" is a device that collects feedback from users, analyzes it, and uses that feedback to improve the accuracy of future suggestions.

[0549] This invention is a system that comprehensively analyzes a user's biometric information and emotional state and provides appropriate behavioral suggestions. The system comprises multiple measuring devices, processing devices, suggestion generation devices, feedback processing devices, and output devices.

[0550] Hardware and software

[0551] The device continuously collects biometric data such as heart rate, body temperature, facial expression changes, and voice information through measuring devices such as smartwatches and smartphones. This data is analyzed by a processing unit, and sentiment analysis is performed using machine learning algorithms. This analysis accurately determines the user's emotional state and also takes into account the long-term history of the data. Based on these analysis results, the server configures a suggestion generation device to propose the most appropriate action to the user.

[0552] Specific example

[0553] For example, if a user experiences a lot of stress in their daily life, the server will suggest relaxation methods or meditation based on the results of an emotion analysis. The suggestions are notified to the user via an audio output device, so the user can receive notifications on their smartphone or smart speaker. By inputting a prompt message such as, "Please suggest effective relaxation methods when the user wants to relax. Please refer to the current heart rate and voice tone," into the AI ​​model, the suggestion generator will derive specific actions based on that message.

[0554] Furthermore, user feedback is collected by a feedback processing device and used to improve the accuracy of future suggestions. This allows the system to provide more personalized and suitable suggestions to the user over time, thereby contributing to the user's mental well-being.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] The device collects biometric data such as the user's heart rate, body temperature, facial expression changes, and voice information through measuring devices. This allows for the accumulation of real-time biometric data. The input biometric data is temporarily stored within the device for further processing. Specifically, the smartwatch measures the heart rate, and the smartphone captures audio and video.

[0558] Step 2:

[0559] The device transmits the collected biometric data to the server. This involves encrypting the data using a secure communication protocol and transmitting it to the server over the network. The input is the biometric data transmitted from the device, and the server generates output to receive it. Specifically, the data is uploaded to the server in real time using Wi-Fi or mobile data communication.

[0560] Step 3:

[0561] The server analyzes the received biometric data using a processing unit. The emotion engine uses machine learning algorithms to analyze the user's emotional state from the biometric data with high accuracy. The input is the biometric data sent to the server, and the output is the analysis result indicating the user's emotional state. Specifically, it performs voice tone analysis and facial expression recognition to determine stress levels and relaxation levels.

[0562] Step 4:

[0563] The server uses a suggestion generation device to create action suggestions for the user based on the analysis results. Considering the analysis results and the user's long-term history, it inputs appropriate prompt sentences into a generation AI model to create personalized suggestions. The input is the analysis results and user history, and the output is specific action suggestions. For example, a suggestion such as "You can reduce stress by taking deep breaths" might be generated.

[0564] Step 5:

[0565] Action suggestions generated by the suggestion generation device are communicated to the user via an output device on the terminal. Notifications are made via voice or display, presented in a way that is easily understandable to the user. The input is the generated action suggestion, and the output is the notification to the user. For example, a smart speaker might announce, "Try a short meditation session."

[0566] Step 6:

[0567] The user performs the suggested action and provides feedback on the result to the device. This feedback is provided via button presses or voice input and sent to the server by a feedback processing unit. The input is the user's feedback, and the output is the feedback data. Specifically, feedback might include statements like, "The suggested meditation helped reduce stress."

[0568] Step 7:

[0569] The server uses a feedback processing unit to analyze user feedback. This analysis enables a learning process that improves the accuracy of future suggestions. The input is user feedback data, and the output is an updated suggestion model and analysis algorithm. This improves the system so that it can provide suggestions that are more suitable for the user.

[0570] (Application Example 2)

[0571] 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."

[0572] In today's world, consumer shopping experiences are diversifying, and improving customer experience in this environment is essential. Especially in physical stores, personalized service based on the customer's emotions and mental state is crucial, but achieving this is difficult. Therefore, the challenge lies in providing a better shopping experience by utilizing the customer's biometric information to offer personalized suggestions.

[0573] 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.

[0574] In this invention, the server includes a plurality of sensor means for collecting biometric information, a processing means for processing the data collected by the sensor means and analyzing the user's emotional state, a suggestion generation means for proposing appropriate actions to the user based on the analysis, a notification means for notifying the user of the suggestions from the suggestion generation means, a feedback processing means for collecting user feedback and improving the next suggestion considering the feedback, and a presentation means for providing the user with an action to execute the suggestion generated by the suggestion generation means. This enables personalized suggestions in real time according to the biometric information of store visitors, providing a comfortable shopping experience in physical stores.

[0575] "Biometric information" refers to data that indicates a person's physical and emotional state, such as heart rate, body temperature, facial expressions, and voice.

[0576] "Sensor means" refers to devices or equipment used to acquire biological information.

[0577] "Processing means" refers to software or hardware functions for analyzing the user's emotional state from collected biometric information.

[0578] "Proposal generation means" refers to a device that handles the process of constructing and generating actions and choices suitable for the user based on the analyzed emotional state.

[0579] "Notification means" refers to an output device in the form of audio, visual, or other means that informs the user of the proposal generated by the proposal generation means.

[0580] "Feedback processing means" refers to a process or device that collects responses from users and uses them to improve the accuracy of suggestions.

[0581] "Presentation means" refers to methods or devices that allow the user to concretely perform the actions provided by the proposal generation means.

[0582] In this embodiment of the invention, a system is used that collects and analyzes a user's biometric information and provides appropriate action suggestions. The server receives data acquired from a terminal equipped with multiple sensor means and performs emotion analysis using a machine learning algorithm. Based on this analysis, the user's current emotional state is determined, and the suggestion generation means constructs specific actions.

[0583] Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the device continuously collect biometric information and transmit this data wirelessly to a server. The server processes this data using Python and utilizes an emotion engine to recognize the user's emotions with high accuracy. Emotional fluctuation trends are analyzed by referring to long-term data history.

[0584] Based on the analysis results, the suggestion generation means generates appropriate actions according to the user's state. Subsequently, the action suggestion is provided to the user via a notification means, either verbally or visually. The user acts according to this suggestion and inputs the result as feedback into the terminal, allowing the feedback processing means to improve the accuracy of future suggestions.

[0585] For example, if the server determines that a customer is feeling stressed, it generates a suggestion such as "Take a short break in the relaxation area" and notifies the user via their device. When the user actually takes action and provides feedback on their experience, the suggestions for future visits become even more personalized.

[0586] An example of a prompt to input into the generating AI model is, "What action suggestions would you come up with to help a customer find a way to relax in the store?" In this way, the system can improve the user experience in physical stores.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The device collects biometric information. The device's heart rate sensor, temperature sensor, camera, and microphone continuously acquire the user's heart rate, body temperature, facial expressions, and voice, and this data is stored as primary data.

[0590] Step 2:

[0591] The device transmits the collected biometric information to the server. Using wireless communication, the primary data is transferred to the server in real time. During this process, the data format is converted to a format suitable for processing on the server.

[0592] Step 3:

[0593] The server analyzes biometric information. It applies machine learning algorithms to the received data to analyze the user's emotional state. Specifically, a generative AI model is used to convert facial expressions and voice data into emotion labels, and changes in heart rate and body temperature are evaluated as stress levels.

[0594] Step 4:

[0595] The server generates suggestions based on the analysis results. Based on emotional state and stress levels, the suggestion generation system constructs actions such as relaxation techniques or guidance to specific areas within a store. In this process, past data history is referenced to personalize the suggestions.

[0596] Step 5:

[0597] The server notifies the terminal of the generated suggestion. It sends the suggestion content to the terminal as a text or voice message. The terminal prompts the user to take action by presenting the message to the user through a voice output device or display.

[0598] Step 6:

[0599] Users act according to the suggestions they receive. They perform the suggested actions in a physical store and provide feedback by entering the results of their experience into a terminal.

[0600] Step 7:

[0601] The device sends feedback to the server. The feedback data received from the user is transferred to the server, conveying the effectiveness of the suggestion and the user's evaluation. This data is analyzed on the server and reflected in future suggestions.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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".

[0619] This invention is a comprehensive system for maintaining the mental health of users, and it continuously collects the user's biometric information using multiple sensors. Specifically, the terminal is equipped with various sensors for acquiring heart rate, body temperature, facial expressions, and voice, and these sensors acquire the user's biometric information in real time.

[0620] The acquired biometric information is temporarily stored on the device and simultaneously encrypted and securely transmitted to the server. The server decrypts the received data and uses various processing methods to analyze the emotional state. Here, an AI algorithm is used to identify fluctuations in heart rate, voice tone, and changes in facial expression to evaluate the user's mental state.

[0621] For example, if the server's analysis detects that a user is showing significant stress during a specific time period or situation, the suggestion generation system will create specific action suggestions for the user, such as "It would be good to take a short break and drink some water." These suggestions are then communicated to the user via an audio output device on the terminal.

[0622] Furthermore, user feedback is collected via voice or the device interface and analyzed by a feedback processing system. This allows the system to continuously adjust to provide more optimized suggestions for individual users. This process effectively supports the prevention and early detection of users' mental health issues.

[0623] As a concrete example demonstrating the advantages of this system, if a user feels stressed after a meeting, fluctuations in their heart rate are detected, and the server suggests a 5-minute break. After the break, improved productivity is confirmed, and the user submits feedback on its effectiveness. This feedback is then used to improve future suggestions, enabling more personalized responses.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The device acquires the user's biometric information using various sensors. The heart rate sensor continuously monitors the heart rate, the camera periodically captures the user's facial expressions, and the microphone records the tone and content of their voice. This data is collected at regular intervals and stored in storage.

[0627] Step 2:

[0628] The terminal preprocesses the collected biometric information, performing noise reduction and data formatting, then encrypts it using a security protocol before sending it to the server. To achieve real-time data transfer, the data is delivered to the server in streaming format.

[0629] Step 3:

[0630] The server receives data transmitted from the terminal and performs analysis using processing tools. Utilizing AI algorithms, it extracts patterns indicating emotional states from biometric data and evaluates the user's current mental state. It also compares this data with past data to identify signs of stress or abnormalities.

[0631] Step 4:

[0632] Based on the processing results, the server generates action suggestions for the user using a suggestion generation mechanism. These suggestions include specific actions that help improve the user's mental state and are customizable.

[0633] Step 5:

[0634] The terminal receives suggestions from the server and notifies the user using an audio output device. The suggestions are adjusted according to the time and situation and converted into audio in a format that is easy for the user to understand.

[0635] Step 6:

[0636] The user chooses whether or not to act on the suggestion and inputs the result as feedback into the device. Feedback can be entered using voice input or touch gestures and sent back to the system.

[0637] Step 7:

[0638] The server analyzes user feedback and uses it to improve future suggestions through feedback processing mechanisms. User behavior history and feedback data are used to provide individually optimized solutions and improve the accuracy of the system.

[0639] (Example 1)

[0640] 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".

[0641] In modern society, maintaining individual mental health is a crucial issue, and stress management in daily life is a central theme. However, there is no system that provides individualized approaches tailored to each user, offers appropriate behavioral suggestions in real time, and continuously improves their effectiveness through feedback. This invention aims to provide comprehensive and flexible support for improving and maintaining users' mental health.

[0642] 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.

[0643] In this invention, the server includes processing means using artificial intelligence to decode information and analyze emotional states, suggestion generation means that propose specific actions to the user based on the analysis results, and feedback improvement means that collect user feedback and optimize the suggestions based on that feedback. This makes it possible to provide personalized, real-time action suggestions and improve the quality of the suggestions based on user feedback.

[0644] A "sensor unit" is a hardware component used to acquire a user's biometric data, and it has the function of detecting heart rate, temperature, facial expressions, voice, etc., in real time.

[0645] A "data management means" is a part of a system configuration that has the function of temporarily storing acquired biometric information and then encrypting and securely storing that information.

[0646] "Communication methods" refer to a set of infrastructure and protocols used to transmit encrypted data from a terminal to a server, enabling secure and efficient data transfer.

[0647] "Processing methods using artificial intelligence" refers to algorithms and their implementations used to analyze decoded data on a server and evaluate the user's emotional state.

[0648] A "proposal generation means" is a part of a system configuration that has the function of automatically generating specific action suggestions for the user based on the results of the analyzed emotional state.

[0649] "Notification means" refers to devices or methods that transmit generated action suggestions to the user via voice or other means, enabling the user to receive the suggestions immediately.

[0650] "Feedback improvement methods" are technical elements that improve the accuracy and effectiveness of proposals by analyzing feedback collected from users and reflecting the results in future proposals.

[0651] This invention is an information processing system for maintaining and improving the mental health of users. It acquires and analyzes the user's biometric data using various sensor units and provides individually adapted behavioral suggestions.

[0652] The device is equipped with multiple sensor units that capture heart rate, body temperature, facial expressions, and voice. These sensors collect the user's real-time biometric data, and this information is temporarily stored and encrypted by a data management system. For example, AES encryption technology is used in data management.

[0653] The device securely transmits encrypted biometric data to the server via a communication method. HTTPS is used as the communication protocol to ensure data integrity and privacy.

[0654] The server decodes the received data and evaluates the emotional state using artificial intelligence processing. Specifically, it utilizes a generative AI model to analyze heart rate variability, voice tone, and changes in facial expressions to determine the user's stress level and other factors.

[0655] Based on the analysis results, the server automatically generates appropriate action suggestions for the user using a suggestion generation mechanism. These suggestions are sent to the terminal via a notification mechanism, and the terminal notifies the user through an audio output device.

[0656] After implementing a suggested action, the user provides feedback on its effectiveness. This feedback is collected via voice or a terminal interface and analyzed by feedback improvement tools. This improves the accuracy of future suggestions.

[0657] For example, if a user experiences stress after a meeting and fluctuations in their heart rate are detected, the server suggests taking a 5-minute break. If the user then finds the break effective, this feedback is used to inform future suggestions.

[0658] An example of a prompt is, "What should be suggested when the user's heart rate is higher than normal?" This prompts the generative AI model to analyze and suggest a response based on the user's state.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The device uses a sensor unit to collect the user's heart rate, body temperature, facial expressions, and voice data in real time. The input is the user's biometric data, and the output is this data transmitted to the device through the sensor interface.

[0662] Step 2:

[0663] The device temporarily stores the collected biometric data in its internal storage and then encrypts the data using AES encryption technology. The input is the collected biometric data, and the output is the encrypted data, which is prepared for the next step.

[0664] Step 3:

[0665] The device sends encrypted data to the server via a secure communication protocol (HTTPS). The input is encrypted biometric data, and the output is data sent to the server.

[0666] Step 4:

[0667] The server receives the data and decrypts it using a pre-configured key. The input is encrypted data, and the output is decrypted biometric data. Based on this, the server is ready to perform the next processing step.

[0668] Step 5:

[0669] The server applies a generative AI model to the decoded biometric data, analyzing heart rate, voice tone, and facial expression changes to evaluate the user's emotional state. The input is the decoded biometric data, and the output is an indicator of the analyzed emotional state.

[0670] Step 6:

[0671] The server generates appropriate action suggestions for the user using a suggestion generation mechanism based on the analyzed data. The input is an indicator of the user's emotional state, and the output is the action suggestion.

[0672] Step 7:

[0673] The terminal notifies the user of action suggestions received from the server via an audio output device. The input is the action suggestion, and the output is the suggestion notification to the user.

[0674] Step 8:

[0675] The user takes action based on the suggestion and provides feedback on the results via voice or an interface. The input is the user's response to the suggestion, and the output is the feedback information.

[0676] Step 9:

[0677] The server analyzes user feedback and incorporates it into future suggestions through feedback improvement mechanisms. The input is feedback information, and the output is the improved suggestion.

[0678] (Application Example 1)

[0679] 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".

[0680] In today's consumer environment, there is a demand for real-time understanding of consumers' emotional needs and stress levels, and the provision of individually optimized customer service. However, conventional systems have struggled to provide such individualized support, resulting in challenges in fully increasing consumer satisfaction. In particular, in virtual stores, it is difficult to grasp consumers' emotions and stress levels because their faces are not visible, and a new approach is needed to improve the quality of the consumer experience.

[0681] 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.

[0682] In this invention, the server includes at least one detection means for collecting biometric information, an information processing means for processing the data collected by the detection means and analyzing the user's emotional state, and a service adjustment means for adjusting the service method using the analyzed data. This makes it possible to provide personalized service that responds to the user's real-time stress level and emotional state.

[0683] "Biometric information" refers to data about the user's physical and mental state, particularly circulatory activity, body temperature, facial expression changes, and acoustics.

[0684] "Detection means" refers to a device or sensor for acquiring biometric information from a user.

[0685] "Information processing means" refers to a device or system that has the function of analyzing biometric information collected by detection means and evaluating the emotional state of the user.

[0686] A "proposal formation means" refers to a system that, based on the analysis results of information processing means, presents the most appropriate action for the user.

[0687] "Information transmission means" refers to a system that transmits information generated by proposal formation means to users.

[0688] "Adjustment processing means" refers to a system that collects responses from users and adjusts them while considering the feedback in order to generate more optimized suggestions.

[0689] "Customer service adjustment means" refers to a function that uses analytical data from information processing means to appropriately improve and adjust customer service methods.

[0690] A system for implementing this invention comprises a detection means for collecting biological information, an information processing means for analyzing the collected information, a proposal formation means for generating proposals based on the analysis results, an information transmission means for communicating the generated proposals to the user, an adjustment processing means for collecting user feedback and optimizing the next proposal, and a customer service adjustment means for adjusting the customer service method.

[0691] The server receives circulatory activity, body temperature, facial expression changes, and acoustic data transmitted from the detection means, and analyzes this data using information processing means. The analysis is performed using AI algorithms such as TensorFlow and PyTorch to evaluate the user's emotional state.

[0692] After the analysis results are obtained, the suggestion generation system generates optimal action instructions for the user. For example, if the system determines that the user is in a stressful state, it might suggest "relaxing activities." This suggestion is then communicated to the user using an acoustic information transmission system.

[0693] Furthermore, the terminal collects feedback from users and sends it to the server. The server analyzes this feedback using an adjustment processing mechanism and makes adjustments to help generate future suggestions. At the same time, the customer service adjustment mechanism improves the customer service method by reflecting the analyzed data.

[0694] As a concrete example, consider a situation where a user who visited a virtual store on a holiday experiences increased cardiovascular activity and stress is detected. In this case, the AI ​​system can suggest "relaxing products that might be good to try" along with calming music.

[0695] An example of a prompt to input into the generation AI model is: "Create a prompt that generates relaxation suggestions when the user's heart rate is elevated and their facial expression shows signs of tension."

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The device collects biometric information from the user in real time. Specifically, it uses a heart rate sensor, temperature sensor, camera, and microphone to detect heart rate, body temperature, facial expressions, and voice. The input is raw data from each sensor, and the output is digital data compiled from this data.

[0699] Step 2:

[0700] The terminal temporarily stores the collected digital data locally and then encrypts and transmits it to the server via a secure communication protocol. The input is the digital data of biometric information generated in step 1, and the output is the secure transmission of data to the server.

[0701] Step 3:

[0702] The server receives the incoming data and analyzes it using information processing tools. Here, generative AI models such as TensorFlow and PyTorch are used to determine the user's emotional state. The input is encrypted digital data, and the output is the analysis result representing the user's emotional state.

[0703] Step 4:

[0704] Based on the analysis results, the server generates optimal suggestions for the user using a suggestion formation mechanism. Prompt messages are set according to the analysis results, and the AI ​​generation model generates specific action guidelines such as "suggest relaxation activities." The input is the analysis results of the emotional state, and the output is specific action suggestions.

[0705] Step 5:

[0706] The terminal receives a suggestion sent from the server and notifies the user using an information transmission method. The operation here involves transmitting the notification content to the user using an audio output device or similar. The input is the suggestion content from the server, and the output is an audio notification to the user.

[0707] Step 6:

[0708] Users send feedback on suggestions to the server via their device. This feedback is collected through voice and the user interface. The input is user feedback, and the output is data sent to the server.

[0709] Step 7:

[0710] The server analyzes the received feedback and uses adjustment processing to improve the algorithm for future proposal generation. Simultaneously, it uses customer service adjustment to improve customer service methods. The input is user feedback, and the output is the optimized proposal generation process.

[0711] 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.

[0712] This invention is a system that effectively maintains and manages a user's mental health, and proposes the optimal action for the user by comprehensively analyzing biometric information and emotions. This system acquires biometric information via a terminal equipped with multiple sensor means. Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the terminal continuously collect heart rate, body temperature, facial expression, and voice information.

[0713] By incorporating an emotion engine, the server performs detailed emotional analysis on this biometric information. The emotion engine utilizes machine learning algorithms to accurately recognize the user's emotions from acquired voice and facial expression data. In this process, it also refers to the user's long-term data history and performs trend analysis to understand the trends in emotional fluctuations.

[0714] For example, if a user shows signs of stress more frequently than usual in their daily activities, the emotion engine quickly detects this change and collects data to provide appropriate behavioral suggestions. These suggestions are then specifically customized depending on the situation, such as "try a particular relaxation technique" or "practice a short meditation."

[0715] The suggestion generation system automatically creates useful and specific action suggestions for the user based on information from analysis and the emotion engine. These suggestions are communicated to the user via the terminal's voice output device. The user selects an action based on the suggestion and provides feedback to the terminal regarding the result and the effectiveness of the suggestion. This feedback is analyzed by the server and used by the feedback processing system to improve the accuracy of future suggestions.

[0716] Thus, the present invention provides support for users to actively engage in managing their emotions and improving their mental health, and the combination of emotion engines enables more accurate and personalized services.

[0717] The following describes the processing flow.

[0718] Step 1:

[0719] The device uses multiple sensors to collect the user's biometric information. Specifically, a heart rate sensor records heart rate, a temperature sensor records body temperature, a camera captures facial expressions, and a microphone records voice, and this data is collected in real time.

[0720] Step 2:

[0721] The terminal temporarily stores the collected biometric information and performs noise reduction as a preprocessing step. After preprocessing, the data is encrypted and transmitted to the server using a secure communication method.

[0722] Step 3:

[0723] The server receives data sent from the terminal and analyzes the emotional state using an emotion engine. Voice and facial expression data are labeled with emotions using machine learning algorithms, and indicators of high stress or anxiety are identified.

[0724] Step 4:

[0725] The server investigates the user's long-term data history and analyzes trends in their current emotional state. This makes it possible to monitor increasing stress levels and abnormal emotional fluctuations in real time.

[0726] Step 5:

[0727] The suggestion generation mechanism proposes appropriate actions to the user based on the aforementioned analysis results. The suggested content is customized according to the emotional state recognized by the engine and includes specific actions such as "take a deep breath" or "take a 30-minute break."

[0728] Step 6:

[0729] The terminal notifies the user of suggestions generated by the suggestion generation mechanism via an audio output device. The suggestions are delivered appropriately at a time that does not interrupt the user's focus.

[0730] Step 7:

[0731] Users evaluate the effectiveness of the actions taken based on the received suggestions and provide feedback to their device. This feedback is provided via voice input or touch gestures and is used for subsequent analysis and suggestion generation.

[0732] Step 8:

[0733] The server analyzes user feedback and uses it in feedback processing to improve the accuracy of future suggestions. This process ensures that suggestions provided to users become increasingly tailored to their individual needs.

[0734] (Example 2)

[0735] 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".

[0736] In modern society, maintaining and improving users' mental health is a crucial issue, but there are limited systems capable of comprehensively analyzing users' biometric data and suggesting appropriate actions. Existing methods have the problem of difficulty in grasping subtle fluctuations in users' emotions and physical condition in real time and providing appropriate feedback. Therefore, there is a need to analyze users' biometric information and emotions with high accuracy and provide personalized suggestions that meet individual needs.

[0737] 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.

[0738] In this invention, the server includes a measuring device for collecting biometric data, a processing device for processing the information collected by the measuring device and analyzing the user's emotional state with high accuracy, and a suggestion generation device for suggesting actions based on the analysis and the user's long-term data history. This makes it possible to analyze the user's biometric information in real time and to quickly and appropriately suggest specific actions necessary to maintain and improve the user's mental health.

[0739] "Biometric data" refers to information that indicates the user's physical condition, including heart rate, body temperature, changes in facial expression, and voice information.

[0740] A "measuring device" is a device used to collect biological data and is equipped with various sensors.

[0741] A "processing device" is a device that analyzes biological data obtained from measuring devices and makes highly accurate judgments about the user's emotional state.

[0742] A "proposal generation device" is a device that proposes actions to users based on the analysis results from the processing device and the user's long-term data history.

[0743] An "output device" is a device that notifies the user of suggestions from the suggestion generation device, and conveys information through visual or auditory means.

[0744] A "feedback processing device" is a device that collects feedback from users, analyzes it, and uses that feedback to improve the accuracy of future suggestions.

[0745] This invention is a system that comprehensively analyzes a user's biometric information and emotional state and provides appropriate behavioral suggestions. The system comprises multiple measuring devices, processing devices, suggestion generation devices, feedback processing devices, and output devices.

[0746] Hardware and software

[0747] The device continuously collects biometric data such as heart rate, body temperature, facial expression changes, and voice information through measuring devices such as smartwatches and smartphones. This data is analyzed by a processing unit, and sentiment analysis is performed using machine learning algorithms. This analysis accurately determines the user's emotional state and also takes into account the long-term history of the data. Based on these analysis results, the server configures a suggestion generation device to propose the most appropriate action to the user.

[0748] Specific example

[0749] For example, if a user experiences a lot of stress in their daily life, the server will suggest relaxation methods or meditation based on the results of an emotion analysis. The suggestions are notified to the user via an audio output device, so the user can receive notifications on their smartphone or smart speaker. By inputting a prompt message such as, "Please suggest effective relaxation methods when the user wants to relax. Please refer to the current heart rate and voice tone," into the AI ​​model, the suggestion generator will derive specific actions based on that message.

[0750] Furthermore, user feedback is collected by a feedback processing device and used to improve the accuracy of future suggestions. This allows the system to provide more personalized and suitable suggestions to the user over time, thereby contributing to the user's mental well-being.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The device collects biometric data such as the user's heart rate, body temperature, facial expression changes, and voice information through measuring devices. This allows for the accumulation of real-time biometric data. The input biometric data is temporarily stored within the device for further processing. Specifically, the smartwatch measures the heart rate, and the smartphone captures audio and video.

[0754] Step 2:

[0755] The device transmits the collected biometric data to the server. This involves encrypting the data using a secure communication protocol and transmitting it to the server over the network. The input is the biometric data transmitted from the device, and the server generates output to receive it. Specifically, the data is uploaded to the server in real time using Wi-Fi or mobile data communication.

[0756] Step 3:

[0757] The server analyzes the received biometric data using a processing unit. The emotion engine uses machine learning algorithms to analyze the user's emotional state from the biometric data with high accuracy. The input is the biometric data sent to the server, and the output is the analysis result indicating the user's emotional state. Specifically, it performs voice tone analysis and facial expression recognition to determine stress levels and relaxation levels.

[0758] Step 4:

[0759] The server uses a suggestion generation device to create action suggestions for the user based on the analysis results. Considering the analysis results and the user's long-term history, it inputs appropriate prompt sentences into a generation AI model to create personalized suggestions. The input is the analysis results and user history, and the output is specific action suggestions. For example, a suggestion such as "You can reduce stress by taking deep breaths" might be generated.

[0760] Step 5:

[0761] Action suggestions generated by the suggestion generation device are communicated to the user via an output device on the terminal. Notifications are made via voice or display, presented in a way that is easily understandable to the user. The input is the generated action suggestion, and the output is the notification to the user. For example, a smart speaker might announce, "Try a short meditation session."

[0762] Step 6:

[0763] The user performs the suggested action and provides feedback on the result to the device. This feedback is provided via button presses or voice input and sent to the server by a feedback processing unit. The input is the user's feedback, and the output is the feedback data. Specifically, feedback might include statements like, "The suggested meditation helped reduce stress."

[0764] Step 7:

[0765] The server uses a feedback processing unit to analyze user feedback. This analysis enables a learning process that improves the accuracy of future suggestions. The input is user feedback data, and the output is an updated suggestion model and analysis algorithm. This improves the system so that it can provide suggestions that are more suitable for the user.

[0766] (Application Example 2)

[0767] 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".

[0768] In today's world, consumer shopping experiences are diversifying, and improving customer experience in this environment is essential. Especially in physical stores, personalized service based on the customer's emotions and mental state is crucial, but achieving this is difficult. Therefore, the challenge lies in providing a better shopping experience by utilizing the customer's biometric information to offer personalized suggestions.

[0769] 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.

[0770] In this invention, the server includes a plurality of sensor means for collecting biometric information, a processing means for processing the data collected by the sensor means and analyzing the user's emotional state, a suggestion generation means for proposing appropriate actions to the user based on the analysis, a notification means for notifying the user of the suggestions from the suggestion generation means, a feedback processing means for collecting user feedback and improving the next suggestion considering the feedback, and a presentation means for providing the user with an action to execute the suggestion generated by the suggestion generation means. This enables personalized suggestions in real time according to the biometric information of store visitors, providing a comfortable shopping experience in physical stores.

[0771] "Biometric information" refers to data that indicates a person's physical and emotional state, such as heart rate, body temperature, facial expressions, and voice.

[0772] "Sensor means" refers to devices or equipment used to acquire biological information.

[0773] "Processing means" refers to software or hardware functions for analyzing the user's emotional state from collected biometric information.

[0774] "Proposal generation means" refers to a device that handles the process of constructing and generating actions and choices suitable for the user based on the analyzed emotional state.

[0775] "Notification means" refers to an output device in the form of audio, visual, or other means that informs the user of the proposal generated by the proposal generation means.

[0776] "Feedback processing means" refers to a process or device that collects responses from users and uses them to improve the accuracy of suggestions.

[0777] "Presentation means" refers to methods or devices that allow the user to concretely perform the actions provided by the proposal generation means.

[0778] In this embodiment of the invention, a system is used that collects and analyzes a user's biometric information and provides appropriate action suggestions. The server receives data acquired from a terminal equipped with multiple sensor means and performs emotion analysis using a machine learning algorithm. Based on this analysis, the user's current emotional state is determined, and the suggestion generation means constructs specific actions.

[0779] Specifically, a heart rate sensor, temperature sensor, camera, and microphone installed on the device continuously collect biometric information and transmit this data wirelessly to a server. The server processes this data using Python and utilizes an emotion engine to recognize the user's emotions with high accuracy. Emotional fluctuation trends are analyzed by referring to long-term data history.

[0780] Based on the analysis results, the suggestion generation means generates appropriate actions according to the user's state. Subsequently, the action suggestion is provided to the user via a notification means, either verbally or visually. The user acts according to this suggestion and inputs the result as feedback into the terminal, allowing the feedback processing means to improve the accuracy of future suggestions.

[0781] For example, if the server determines that a customer is feeling stressed, it generates a suggestion such as "Take a short break in the relaxation area" and notifies the user via their device. When the user actually takes action and provides feedback on their experience, the suggestions for future visits become even more personalized.

[0782] An example of a prompt to input into the generating AI model is, "What action suggestions would you come up with to help a customer find a way to relax in the store?" In this way, the system can improve the user experience in physical stores.

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The device collects biometric information. The device's heart rate sensor, temperature sensor, camera, and microphone continuously acquire the user's heart rate, body temperature, facial expressions, and voice, and this data is stored as primary data.

[0786] Step 2:

[0787] The device transmits the collected biometric information to the server. Using wireless communication, the primary data is transferred to the server in real time. During this process, the data format is converted to a format suitable for processing on the server.

[0788] Step 3:

[0789] The server analyzes biometric information. It applies machine learning algorithms to the received data to analyze the user's emotional state. Specifically, a generative AI model is used to convert facial expressions and voice data into emotion labels, and changes in heart rate and body temperature are evaluated as stress levels.

[0790] Step 4:

[0791] The server generates suggestions based on the analysis results. Based on emotional state and stress levels, the suggestion generation system constructs actions such as relaxation techniques or guidance to specific areas within a store. In this process, past data history is referenced to personalize the suggestions.

[0792] Step 5:

[0793] The server notifies the terminal of the generated suggestion. It sends the suggestion content to the terminal as a text or voice message. The terminal prompts the user to take action by presenting the message to the user through a voice output device or display.

[0794] Step 6:

[0795] Users act according to the suggestions they receive. They perform the suggested actions in a physical store and provide feedback by entering the results of their experience into a terminal.

[0796] Step 7:

[0797] The device sends feedback to the server. The feedback data received from the user is transferred to the server, conveying the effectiveness of the suggestion and the user's evaluation. This data is analyzed on the server and reflected in future suggestions.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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."

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] The following is further disclosed regarding the embodiments described above.

[0820] (Claim 1)

[0821] Multiple sensor means for collecting biological information,

[0822] Processing means for processing data collected by the aforementioned sensor means and analyzing the user's emotional state,

[0823] A proposal generation means that proposes appropriate actions to the user based on the aforementioned analysis,

[0824] A notification means for notifying the user of the proposals from the proposal generation means,

[0825] A feedback processing means for collecting user feedback and improving the next proposal by taking said feedback into consideration,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the biometric information is heart rate, body temperature, facial expression, and voice.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the notification means uses an audio output device.

[0831] "Example 1"

[0832] (Claim 1)

[0833] Multiple sensor units that acquire biometric data,

[0834] A data management means for temporarily storing and encrypting information acquired by the sensor unit,

[0835] A communication means for transmitting the encrypted information to the server,

[0836] The aforementioned server decodes the information and uses artificial intelligence to process it and analyze the emotional state.

[0837] A proposal generation means that proposes specific actions to the user based on the analysis results,

[0838] A notification means for notifying the user of the action suggestion from the suggestion generation means by voice,

[0839] A feedback improvement method that collects user feedback and optimizes suggestions based on that feedback,

[0840] An information processing system that includes this.

[0841] (Claim 2)

[0842] The information processing system according to claim 1, wherein the biometric data includes heart rate, temperature, facial expression, and voice.

[0843] (Claim 3)

[0844] The information processing system according to claim 1, wherein the notification means conveys a suggestion to the user via an audio output device.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] A detection means for collecting biological information,

[0848] Information processing means for processing data collected by the detection means and analyzing the user's emotional state,

[0849] A proposal formation means that proposes the optimal action for the user based on the aforementioned analysis,

[0850] Information transmission means for notifying the user of the proposal from the proposal formation means,

[0851] A means for adjusting the next proposal by collecting user feedback and taking that feedback into consideration,

[0852] A customer service adjustment means that adjusts the customer service method using the aforementioned analysis data,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the biological information is circulatory activity, body temperature, facial expression changes, and sound.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the information transmission means uses an acoustic output device.

[0858] "Example 2 of combining an emotion engine"

[0859] (Claim 1)

[0860] A measuring device for collecting biological data,

[0861] A processing device that processes information collected by the aforementioned measuring device and analyzes the user's emotional state with high accuracy,

[0862] A proposal generation device that proposes actions based on the aforementioned analysis and the user's long-term data history,

[0863] An output device that notifies the user of the proposals from the proposal generation device visually or audibly,

[0864] A feedback processing device that collects user feedback, analyzes the feedback, and improves future proposals,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the biometric data includes heart rate, body temperature, changes in facial expression, and voice information.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the output device uses an audio output device.

[0870] "Application example 2 of combining emotional engines"

[0871] (Claim 1)

[0872] Multiple sensor means for collecting biological information,

[0873] Processing means for processing data collected by the aforementioned sensor means and analyzing the user's emotional state,

[0874] A proposal generation means that proposes appropriate actions to the user based on the aforementioned analysis,

[0875] A notification means for notifying the user of the proposals from the proposal generation means,

[0876] A feedback processing means for collecting user feedback and improving the next proposal by taking said feedback into consideration,

[0877] A presentation means that provides the user with an action to carry out the proposal generated by the proposal generation means,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the biological information comprises physiological data and emotional expressions.

[0881] (Claim 3)

[0882] The system according to claim 1, wherein the notification means uses an audio output device and the presentation means uses a visual output device. [Explanation of Symbols]

[0883] 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. Multiple sensor means for collecting biological information, Processing means for processing data collected by the aforementioned sensor means and analyzing the user's emotional state, A proposal generation means that proposes appropriate actions to the user based on the aforementioned analysis, A notification means for notifying the user of a proposal from the proposal generation means, A feedback processing means for collecting user feedback and improving the next proposal by taking said feedback into consideration, A system that includes this.

2. The system according to claim 1, wherein the biometric information is heart rate, body temperature, facial expression, and voice.

3. The system according to claim 1, wherein the notification means uses an audio output device.

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