system

Abstract

We provide the system. [Solution] A device for acquiring biological information, A communication device for transmitting acquired biometric information to computing resources, A processing device for integrating and analyzing biological information and related activity data to generate optimal instructions, A display device for notifying the user of the generated instructions, A monitoring device for monitoring the user's health status and providing continuous feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to lack of experience and difficulty in grasping their own state during business negotiations, salespersons face the lack of effective sales strategies and instability in performance. There is also a concern that stress and pressure increase health risks. Furthermore, since sales skills and know-how depend on individuals, the improvement of the overall organizational strength is not sufficient, which is also an issue. It is required to solve these problems and provide an environment in which salespersons can continuously achieve stable and high results.

Means for Solving the Problems

[0005] This invention provides a system that uses biometric information to understand the status of sales representatives in real time and provide appropriate advice. Specifically, biometric information is collected by a wearable device and transmitted to a server. On this server, a generating AI integrates and analyzes the biometric information and sales data based on the sales manager's thought patterns to generate effective advice. The generated advice is presented to the user, and the analysis model can be updated using feedback as needed to provide even more accurate advice. This enables both improved sales representative performance and health management, leading to the achievement of sustainable high results.

[0006] "Biometric information" refers to indicators of a user's physical condition, including data such as heart rate and stress level.

[0007] "Measurement means" refers to devices and equipment for collecting biological information in real time, and wearable devices are an example of this.

[0008] "Communication means" refers to means for transmitting biometric information obtained by measurement means to a server, and includes the use of data transfer technology via network communication.

[0009] "Analysis means" refers to a system component that includes a process for integrating and processing collected biometric information and data obtained from sales activities to generate useful advice for the user.

[0010] "Presentation means" refers to a means for conveying advice generated by the analysis means to the user, and may include a display or an audio output device.

[0011] "Learning method" refers to the process of updating the analysis model by incorporating user feedback and improving the quality of advice for future sessions.

[0012] An "optimization tool" is a system component that utilizes the thinking patterns of sales managers and has the function of adjusting the generated advice to suit the individual sales representative. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0016] In the following embodiments, a 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.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system of this invention consists of a wearable device worn by a sales representative and a server that receives and analyzes the data transmitted from it. This system utilizes the sales representative's biometric information to provide real-time feedback to support sales activities.

[0035] First, the user, a sales representative, wears a wearable device. This device continuously measures biometric information such as the user's heart rate and stress level. The wearable device, acting as a terminal, transmits this biometric information to a server in the cloud using communication methods.

[0036] The server integrates and analyzes the received biometric information with previously accumulated sales data. This analysis is performed by a generative AI, using a model based on the thought patterns of sales managers. As a result, the server generates personalized advice in real time, tailored to the user's situation.

[0037] The generated advice is then sent back to the terminal via a communication method. The terminal notifies the user of this advice. The notification can be a message displayed on the screen or voice guidance. For example, if the user's heart rate increases and they become more nervous during a business meeting, the server generates specific advice such as "We recommend taking a few minutes to take deep breaths and relax" and communicates it to the user through the terminal.

[0038] Furthermore, the generating AI continuously updates its model by receiving user feedback from the server. This allows the system to learn from the feedback and analysis results, improving the accuracy of its advice over time. As a result, users receive optimal support tailored to their individual circumstances.

[0039] In this way, the present invention uses biometric information and sales data to support the work of sales representatives and realize health management and improved sales performance.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users begin their daily work by wearing a wearable device. This device has the capability to collect the user's biometric information in real time. Specifically, it continuously records data such as heart rate and stress level.

[0043] Step 2:

[0044] The device (wearable device) transmits collected biometric information to a server using a predetermined communication method. Communication takes place in real time, minimizing data delay.

[0045] Step 3:

[0046] The server receives biometric information transmitted from the terminal and temporarily stores it in a database for integrated analysis with previously collected sales data.

[0047] Step 4:

[0048] The server uses a generation AI to analyze received biometric information and sales data. Leveraging a model based on the sales manager's thought patterns, it evaluates the user's state and generates individually optimized advice.

[0049] Step 5:

[0050] The server sends the generated advice to the terminal. The advice includes specific action suggestions and refresh methods tailored to the user's current state.

[0051] Step 6:

[0052] The device notifies the user of advice sent from the server. This notification is either displayed as text on the device's screen or delivered via audio output. Specific examples include suggestions such as "Take time to take a deep breath."

[0053] Step 7:

[0054] Users adjust their actions based on the advice they receive. If they are in a business negotiation, they can take a short break as suggested.

[0055] Step 8:

[0056] Users provide feedback on the effectiveness of the advice through their devices. This feedback is based on the user's experience.

[0057] Step 9:

[0058] The server receives and analyzes user feedback. Based on this feedback, the generating AI updates its model and further optimizes advice for future use. This improves the accuracy and effectiveness of the advice provided.

[0059] (Example 1)

[0060] 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."

[0061] In sales settings, there is a need for technology that utilizes individual biometric information in real time to manage stress and improve performance. However, conventional systems have not adequately integrated biometric information with sales data, making it difficult to provide personalized advice. Furthermore, efficiently utilizing user feedback to enhance the effectiveness of advice has also been a challenge.

[0062] 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.

[0063] In this invention, the server includes a device for acquiring biometric information, a communication method for transmitting it to an information processing device, and a computation method for analyzing and generating personalized advice. This makes it possible to provide advice in real time based on an individual's biometric information and to update the model based on user feedback.

[0064] "Device" refers to hardware or software used to acquire biometric information and measure the user's health data.

[0065] "Communication method" refers to a means of transmitting acquired biometric information to an information processing device, and includes wireless and wired communication.

[0066] The term "calculation method" refers to the process of analyzing received biometric information and accumulated business information to generate personalized advice.

[0067] "Notification method" refers to the means by which the generated advice is communicated to the individual, including display information and audio guidance.

[0068] "Learning method" refers to the process of improving the accuracy and adaptability of computational models based on feedback from individuals.

[0069] The "optimization method" is a process for individually optimizing the generated advice using a model that has learned the thought patterns of business managers.

[0070] The system of the present invention consists primarily of a wearable device worn by a sales representative and a server for analyzing the data transmitted from it. The user wears a wearable device with built-in biosensors that measure heart rate and stress levels. This device has communication capabilities to transmit biometric information to a server in the cloud via Bluetooth or Wi-Fi.

[0071] The server uses received biometric information and historical sales data to analyze the data using a generative AI model. Machine learning algorithms are applied to the analysis, evaluating the user's state and generating advice based on the sales manager's thought patterns. This model continuously learns from user feedback and is continuously optimized. Specifically, prompts such as "How can the user relax?" are input to the AI, which then generates appropriate advice.

[0072] The generated advice is sent from the server to the wearable device and displayed on the device's screen or communicated to the user via voice. For example, if a user's heart rate increases during work, the server will generate advice such as, "Your heart rate is high; we recommend taking deep breaths for a few minutes."

[0073] This system allows users to receive continuous feedback in their daily work, which can be used to improve work efficiency and manage their health. Over time, the accuracy of advice improves based on the feedback and analysis results, enabling more personalized support.

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

[0075] Step 1:

[0076] The user wears a wearable device. The device measures heart rate and stress level in real time and collects this as biometric data. The input is real-time biometric data from the user, and the output is data that temporarily stores this information. Specifically, the device checks the heart rate every second and immediately records any abnormal values.

[0077] Step 2:

[0078] The wearable device acting as the terminal periodically transmits stored biometric information to a server. The input is temporarily stored biometric information, and the output is data transferred to the server. This transmission is performed using Bluetooth or Wi-Fi, and information is sent at short intervals (e.g., every minute).

[0079] Step 3:

[0080] The server receives biometric data and performs data preprocessing. The input is biometric data sent from the terminal, and the output is cleansed data. Data preprocessing includes imputing missing values ​​and detecting and correcting outliers. If outliers are found, the server corrects them, for example, by using the mean value.

[0081] Step 4:

[0082] The server integrates the received data with existing sales data and performs analysis using a generative AI model. The input consists of cleansed biometric information and sales data, and the output is personalized advice to be provided to the user. Machine learning algorithms are used for the analysis, and the prompt "How can the user relax?" is used as input to the generative AI model.

[0083] Step 5:

[0084] The server packages the generated advice and sends it back to the terminal. The input is the parsed advice, and the output is data formatted for transmission. Specifically, the advice text is converted to the appropriate format and sent via the communication protocol.

[0085] Step 6:

[0086] The terminal notifies the user of the advice it has received. The input is advice data sent from the server, and the output is a displayed message or audio guidance. For example, it may display a message on a visual display device saying, "Your heart rate is high; we recommend taking deep breaths for a few minutes," or it may play instructions as an audio guide.

[0087] Step 7:

[0088] The user follows the advice and sends feedback to the server via their device. The input is the user's thoughts and evaluation of the advice's effectiveness, and the output is feedback information stored on the server. This feedback information is stored on the server and used to improve the generated AI model in the next learning cycle.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern society, the importance of individual health management and performance improvement is increasing, but conventional systems have struggled to provide detailed feedback tailored to individual health conditions in real time. Furthermore, more advanced analysis and personalization are required to provide optimal advice to improve users' productivity in their daily lives. Therefore, the present invention aims to simultaneously achieve health management and productivity improvement by utilizing users' biometric information to generate real-time instructions tailored to their individual living situations and improving their accuracy.

[0092] 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.

[0093] In this invention, the server includes an acquisition device for acquiring biometric information, a communication device for transmitting the acquired biometric information to a computing resource, and a processing device for integrating and analyzing the biometric information and related activity data to generate optimal instructions. This makes it possible to monitor the user's health status in their daily life in real time and provide detailed advice based on that information.

[0094] "Biometric information" refers to data that indicates an individual's health and physiological state, including heart rate and stress levels.

[0095] A "data acquisition device" is a device used to collect a user's biometric information, and it utilizes wearable devices or sensors.

[0096] A "communication device" is a device used to transmit acquired biometric information to a remote server or other computing resources.

[0097] "Computational resources" refer to computers and cloud servers used to process and analyze collected data.

[0098] A "processing device" is a device that uses biological information and related activity data to perform analysis and generate optimal instructions and advice.

[0099] A "display device" is a device used to convey generated instructions or advice to the user, and includes screens, audio devices, and the like.

[0100] A "monitoring device" is a device that continuously monitors the user's health status and provides feedback as needed.

[0101] A "modification device" is a device that updates the model based on user feedback to improve the accuracy of the analysis.

[0102] An "adaptive device" is a device that optimizes instructions according to the individual user's living situation.

[0103] The system for realizing the present invention acquires biometric information using a wearable device worn by the user and analyzes that information to support individualized health management and performance improvement for the user. The embodiments of the system are described in detail below.

[0104] First, the user wears a wearable device that senses heart rate and stress level as a biometric data acquisition device. This device has built-in sensors such as those found in Apple Watch and Fitbit. The acquired biometric data is transmitted to computing resources in the cloud via a communication device.

[0105] Next, the server receives this data and uses a processing unit to integrate and analyze the biometric information and past activity data. This analysis uses a generative AI model such as OpenAI's GPT-4, which generates optimal instructions tailored to the user's health status and stress level.

[0106] The generated instructions are communicated to the user via a display device. The user can receive feedback through the home robot via voice or display. Specifically, if the user feels fatigued from working for a long time, they will be advised to "take a break."

[0107] Furthermore, the server collects user feedback through monitoring devices, and the adjustment device continuously updates the generated AI model. As a result, the accuracy of the analysis improves over time, and the most appropriate instructions for individual situations are provided.

[0108] For example, if a user experiences high levels of stress on a daily basis, the server can send a prompt to an AI model saying, "Your current stress level appears high. Please generate advice to help you calm down." Appropriate advice will then be generated and provided to the user. This allows the user to maintain a healthy lifestyle while improving their productivity.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user wears a wearable device to acquire biometric information. This device senses the user's heart rate and stress level in real time. The input includes the user's physiological data, and the output generates biometric signals.

[0112] Step 2:

[0113] The device transmits the acquired biometric information to a server in the cloud using a communication method. Here, the input is the biometric information signal from the wearable device, and the output is the transmission of data packets to the server.

[0114] Step 3:

[0115] The server uses a generative AI model to integrate and analyze the received biometric information. Inputs include biometric information sent to the server and historical activity data. Through data processing and calculations, it generates advice best suited to the user's current state. The output is individually personalized instructions and advice.

[0116] Step 4:

[0117] The server sends the generated advice to a terminal or home robot. The input is the generated advice, and the output is a notification message to the user. Notifications are delivered via voice commands or display.

[0118] Step 5:

[0119] The user provides feedback based on the advice received. For example, the user inputs into the terminal whether or not they will follow the advice given regarding taking breaks.

[0120] Step 6:

[0121] The server receives feedback from the user and updates the generated AI model using a tuning device. The input is the user's feedback, and the output is the updated model. This improves the accuracy of the analysis and enables the provision of more effective support.

[0122] 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.

[0123] This invention is a system for supporting sales activities based on the biometric information and emotional state of sales representatives. This system comprises a wearable device worn by the user, a server for analyzing data, and communication means to connect them.

[0124] Users can measure their biometric information in real time using wearable devices. In addition to heart rate and stress levels, the device utilizes an emotion engine to infer the user's emotional state. The data acquired in this way is sent to a server as it is collected.

[0125] The server continues to analyze biometric data and sales data in an integrated manner, enabling it to recognize the user's emotions based on biometric information. This emotion engine determines the user's current emotional state, and the analysis tool generates appropriate advice based on that. For example, if it is determined that the user is feeling stressed and their concentration is low, advice will be generated suggesting appropriate relaxation techniques or ways to proceed with business negotiations.

[0126] The generated advice is notified to the user via the terminal from the server. This notification is displayed on the screen or conveyed by voice, allowing the user to adjust their actions accordingly. The results of emotion recognition and the adjustments made to the advice play an important role in improving the efficiency of sales activities.

[0127] Furthermore, users send feedback on the effectiveness of the advice they receive to the server via their device. This feedback is used in the model's learning process on the server, leading to improvements in the quality of the advice generated. In this way, the entire system can provide a more appropriate approach to the user's individual needs.

[0128] This invention enables personalized sales support tailored to the user's emotional state, leading to improved sales performance and reduced user stress. This configuration utilizes an optimized model that learns the thought patterns of sales managers, continuously providing individually optimized feedback to each user.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users begin their sales activities wearing a wearable device. This device has the ability to measure heart rate, stress levels, and even emotional states in real time using an emotion engine.

[0132] Step 2:

[0133] The device transmits measured biometric and emotional data to a server. This data is transmitted in real time using communication methods, and is designed to minimize latency.

[0134] Step 3:

[0135] The server receives the transmitted biometric and emotional data, integrates it with sales data, and stores it temporarily. It then uses analytical tools to prepare to evaluate the user's state based on this data.

[0136] Step 4:

[0137] The emotion engine running on the server analyzes the user's current emotional state from the received data. Based on this analysis, the generative AI builds the foundational data to create optimal advice.

[0138] Step 5:

[0139] The server's analysis method uses a model that has learned the thought patterns of sales managers to generate advice tailored to the user's emotional state. This advice includes immediately actionable improvement suggestions for the sales situation the user is facing.

[0140] Step 6:

[0141] The server sends the generated advice to the terminal. The advice includes specific action suggestions and ways to refresh based on the user's current emotional state.

[0142] Step 7:

[0143] The terminal notifies the user of advice provided by the server. The notification is displayed as text on the terminal's screen or communicated via audio output.

[0144] Step 8:

[0145] Users will follow the advice given in notifications from their devices, for example, trying a suggested method of refreshing themselves during a tense business negotiation. This is expected to help the negotiation proceed more smoothly.

[0146] Step 9:

[0147] Users send feedback to the server via their device regarding the effectiveness of the advice provided. This feedback, based on the user's experience, helps improve future advice.

[0148] Step 10:

[0149] The server analyzes user feedback in conjunction with an emotion engine and updates the model using generative AI. This allows the system to continuously improve the accuracy of the advice it provides, enabling personalized support for each user.

[0150] (Example 2)

[0151] 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".

[0152] This invention relates to a system that utilizes users' biometric data and emotional states in their work activities to provide personalized support. Conventional methods have made it difficult to provide immediate and effective feedback on user stress management and improved work performance. This invention aims to solve this problem, optimizing user efficiency and reducing stress.

[0153] 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.

[0154] In this invention, the server includes a data collection means, a communication device, and an analysis device. This enables real-time emotion analysis and optimal business support based on biometric data.

[0155] "Biometric data" refers to information about a user's physical condition, such as their heart rate and stress level.

[0156] "Collection means" refers to a device for recording and collecting biological data in real time.

[0157] A "communication device" is a device that has the function of transmitting collected data to an information processing device.

[0158] An "information processing device" is a computer system that receives data and performs analysis and processing on it.

[0159] An "analysis device" is a device used to integrate and analyze received biological data and business data.

[0160] "Business data" refers to information related to business activities, including sales results and customer information.

[0161] A "display device" is a device that visualizes the instructions generated for the user.

[0162] A "learning device" is a device that has an algorithm for improving a system model based on feedback.

[0163] An "optimization device" is a device that adjusts the instructions provided to the user to suit their individual needs.

[0164] This invention is a system that supports work activities based on the user's biometric data and emotional state. A wearable device worn by the user acquires biometric data such as heart rate and stress level in real time. This wearable device measures biometric information and transmits it to a server via a communication device. Wireless communication technologies such as Bluetooth and Wi-Fi are commonly used as the means of communication.

[0165] The server integrates received biometric data with data related to business activities and analyzes the data using a generative AI model. This generative AI model has learned from past sales data and user feedback, enabling it to generate highly personalized instructions. Based on the analysis results, the server provides the user with the most suitable instructions in real time. For example, it can suggest relaxation methods if stress levels are high, or offer advice on how to proceed with tasks if concentration levels are low.

[0166] The generated instructions are communicated to the user via the terminal. These notifications are either displayed visually on the screen or transmitted verbally using speech synthesis technology. Based on the content of the notification, the user can immediately adjust their actions in their work activities.

[0167] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[0168] "Please suggest relaxation methods to offer to sales representatives who are feeling stressed."

[0169] Furthermore, users input feedback on their devices and send it to the server. This feedback is used as training data to improve the quality of the generated AI model. This allows the system to provide more appropriate support to the individual needs of each user.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user wears a wearable device that acquires biometric information such as heart rate and stress level in real time. The user's biometric data serves as input, which the wearable device measures and prepares to transmit as output to a built-in communication device. This device uses Bluetooth or similar technologies to enable data transmission.

[0173] Step 2:

[0174] Biometric information acquired from a wearable device is transmitted to a server via a communication method. The input is biometric data from the device, and the output is the arrival of data at the server. Communication is conducted using a secure protocol and has the capability to process large amounts of data instantly.

[0175] Step 3:

[0176] The server processes the received biometric information and integrates it with pre-collected business data. This integrated dataset is then used as input for analysis using a generative AI model. Data processing includes noise reduction and data normalization. The output provides analysis results regarding the user's emotional state. For example, it might indicate that the user is highly stressed.

[0177] Step 4:

[0178] The server generates optimal instructions based on the analysis results. Integrated analysis data and a generative AI model are used as input, and the output is specific business support advice. At this stage, the prompt sentences generated by the generative AI model are used to create relaxation techniques tailored to emotions and advice on how to proceed with work.

[0179] Step 5:

[0180] The generated advice is sent to the terminal and notified to the user. The input is instruction data from the server, and the output is visualized advice on the terminal. Display and voice instructions are executed, and the user can adjust their actions according to the advice.

[0181] Step 6:

[0182] The user evaluates the effectiveness of the advice received and inputs feedback into the device. The input is subjective feedback data from the user, and the output is feedback sent to the server. This feedback will be used as material for training and fine-tuning future generative AI models.

[0183] Step 7:

[0184] The server receives feedback from users and uses it to improve the generated AI model. The feedback is then used as new input data to generate output that updates the model. This improves the accuracy and personalization of the advice provided.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] There is a need to address the challenge that sales representatives and home users cannot receive appropriate advice tailored to their stress levels and emotional states on the spot, making it difficult to improve the efficiency of their activities and their quality of life.

[0188] 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.

[0189] In this invention, the server includes a measuring device for collecting biometric data, a communication device for transmitting the collected biometric data to an information processing device, an analysis device for integrating and analyzing the biometric data and sales data to generate optimal suggestions, and an emotion analysis device for inferring the user's emotional state and providing appropriate support. This makes it possible to understand the user's emotional state in real time and provide personalized advice tailored to individual needs.

[0190] "Biometric data" refers to information that indicates a user's physical condition, including heart rate and stress level.

[0191] A "measuring device" refers to equipment used to acquire biological data in real time, and specifically to wearable devices that are attached to the body to collect data.

[0192] A "communication device" refers to a means of transmitting biological data acquired by a measuring device to an information processing device, and is a device equipped with wireless communication capabilities.

[0193] An "information processing device" refers to a computer device that receives data from servers and other sources and performs processing and analysis.

[0194] An "analysis device" refers to a device with the processing power to integrate and analyze biometric data and sales data to generate optimal proposals.

[0195] A "display device" refers to a device that visually or audibly notifies the user of generated suggestions or advice.

[0196] An "emotion analysis device" refers to a device equipped with the function to infer a user's emotional state from their biometric data and provide appropriate support.

[0197] "Personalized advice" refers to suggestions optimized according to the individual user's needs and emotional state.

[0198] This invention provides a system that supports sales activities and communication within the home. The core of the system lies in analyzing emotional states using biometric data and providing personalized advice to the user.

[0199] First, the user wears a measuring device, such as a wearable device, to acquire biometric data. This device collects biometric data such as heart rate and stress level in real time and transmits it to an information processing device via a communication device. The information processing device, specifically a server, processes the collected data through an analysis device and performs sentiment analysis to estimate the user's current emotional state. This analysis uses a generative AI model that recognizes emotions based on patterns learned from past data and feedback.

[0200] Based on the analysis results, the server generates optimal suggestions and notifies the user through a display device. These notifications are conveyed using visual display devices or audio. For example, if the user is feeling stressed, the server might suggest "relaxation techniques" or "stress-relieving music."

[0201] This system utilizes biometric and emotional data in an integrated manner to provide users with truly personalized advice. This can lead to increased efficiency in sales activities and reduced stress at home.

[0202] As a concrete example, consider a situation where a user experiences daily stress at work. When the system detects that the user's heart rate is elevated, it suggests, "Why not try these three deep breathing techniques to relax?" The system then receives feedback from the user on whether the suggestion was effective, analyzes this feedback using a learning device, and uses it to generate more accurate suggestions in the future.

[0203] An example of a prompt message when using a generative AI model is: "Suggest relaxation techniques for a user who is tired. Heart rate is 90, stress level is high."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user wears a measuring device, and biometric data is acquired. This device acquires biometric data such as heart rate and stress level, converts it into digital signals, and outputs them.

[0207] Step 2:

[0208] The terminal receives biometric data transmitted from the measuring device and sends the data to the information processing device via the communication device. Here, the biometric data is provided as input to the server.

[0209] Step 3:

[0210] The server processes the received biometric data using an analysis device. It analyzes the data using a generative AI model to infer the emotional state. In this process, calculations are performed to estimate emotions based on stress levels and heart rate from the input biometric data, and an output representing the emotional state is obtained.

[0211] Step 4:

[0212] Based on the analysis results, the server generates suggestions tailored to the user. Here, it utilizes prompts and other elements from the AI ​​model to generate data that serves as advice based on the user's state.

[0213] Step 5:

[0214] The server sends the generated proposal to the display device. The terminal outputs the proposal visually or audibly to notify the user. The user can then review and accept the proposal.

[0215] Step 6:

[0216] The user enters feedback on the effectiveness of the suggestion into the device. The device then sends this feedback back to the server.

[0217] Step 7:

[0218] The server uses the received feedback to update the analysis model through the learning device. In this process, user feedback is input, and the model is trained, resulting in output that improves the accuracy of future suggestions.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] The system of this invention consists of wearable devices worn by sales representatives and a server that receives and analyzes the data transmitted from them. This system utilizes the biometric information of sales representatives to provide real-time feedback to support sales activities.

[0236] First, the user, a sales representative, wears a wearable device. This device continuously measures biometric information such as the user's heart rate and stress level. The wearable device, acting as a terminal, transmits this biometric information to a server in the cloud using communication methods.

[0237] The server integrates and analyzes the received biometric information with previously accumulated sales data. This analysis is performed by a generative AI, using a model based on the thought patterns of sales managers. As a result, the server generates personalized advice in real time, tailored to the user's situation.

[0238] The generated advice is then sent back to the terminal via a communication method. The terminal notifies the user of this advice. The notification can be a message displayed on the screen or voice guidance. For example, if the user's heart rate increases and they become more nervous during a business meeting, the server generates specific advice such as "We recommend taking a few minutes to take deep breaths and relax" and communicates it to the user through the terminal.

[0239] Furthermore, the generating AI continuously updates its model by receiving user feedback from the server. This allows the system to learn from the feedback and analysis results, improving the accuracy of its advice over time. As a result, users receive optimal support tailored to their individual circumstances.

[0240] In this way, the present invention uses biometric information and sales data to support the work of sales representatives and realize health management and improved sales performance.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] Users begin their daily work by wearing a wearable device. This device has the capability to collect the user's biometric information in real time. Specifically, it continuously records data such as heart rate and stress level.

[0244] Step 2:

[0245] The device (wearable device) transmits collected biometric information to a server using a predetermined communication method. Communication takes place in real time, minimizing data delay.

[0246] Step 3:

[0247] The server receives biometric information transmitted from the terminal and temporarily stores it in a database for integrated analysis with previously collected sales data.

[0248] Step 4:

[0249] The server uses a generation AI to analyze received biometric information and sales data. Leveraging a model based on the sales manager's thought patterns, it evaluates the user's state and generates individually optimized advice.

[0250] Step 5:

[0251] The server sends the generated advice to the terminal. The advice includes specific action suggestions and refresh methods tailored to the user's current state.

[0252] Step 6:

[0253] The device notifies the user of advice sent from the server. This notification is either displayed as text on the device's screen or delivered via audio output. Specific examples include suggestions such as "Take time to take a deep breath."

[0254] Step 7:

[0255] Users adjust their actions based on the advice they receive. If they are in a business negotiation, they can take a short break as suggested.

[0256] Step 8:

[0257] Users provide feedback on the effectiveness of the advice through their devices. This feedback is based on the user's experience.

[0258] Step 9:

[0259] The server receives and analyzes user feedback. Based on this feedback, the generating AI updates its model and further optimizes advice for future use. This improves the accuracy and effectiveness of the advice provided.

[0260] (Example 1)

[0261] 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."

[0262] In sales settings, there is a need for technology that utilizes individual biometric information in real time to manage stress and improve performance. However, conventional systems have not adequately integrated biometric information with sales data, making it difficult to provide personalized advice. Furthermore, efficiently utilizing user feedback to enhance the effectiveness of advice has also been a challenge.

[0263] 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.

[0264] In this invention, the server includes a device for acquiring biometric information, a communication method for transmitting it to an information processing device, and a computation method for analyzing and generating personalized advice. This makes it possible to provide advice in real time based on an individual's biometric information and to update the model based on user feedback.

[0265] "Device" refers to hardware or software used to acquire biometric information and measure the user's health data.

[0266] "Communication method" refers to a means of transmitting acquired biometric information to an information processing device, and includes wireless and wired communication.

[0267] The term "calculation method" refers to the process of analyzing received biometric information and accumulated business information to generate personalized advice.

[0268] "Notification method" refers to the means by which the generated advice is communicated to the individual, including display information and audio guidance.

[0269] "Learning method" refers to the process of improving the accuracy and adaptability of computational models based on feedback from individuals.

[0270] The "optimization method" is a process for individually optimizing the generated advice using a model that has learned the thought patterns of business managers.

[0271] The system of the present invention consists primarily of a wearable device worn by a sales representative and a server for analyzing the data transmitted from it. The user wears a wearable device with built-in biosensors that measure heart rate and stress levels. This device has communication capabilities to transmit biometric information to a server in the cloud via Bluetooth or Wi-Fi.

[0272] The server uses received biometric information and historical sales data to analyze the data using a generative AI model. Machine learning algorithms are applied to the analysis, evaluating the user's state and generating advice based on the sales manager's thought patterns. This model continuously learns from user feedback and is continuously optimized. Specifically, prompts such as "How can the user relax?" are input to the AI, which then generates appropriate advice.

[0273] The generated advice is sent from the server to the wearable device and displayed on the device's screen or communicated to the user via voice. For example, if a user's heart rate increases during work, the server will generate advice such as, "Your heart rate is high; we recommend taking deep breaths for a few minutes."

[0274] This system allows users to receive continuous feedback in their daily work, which can be used to improve work efficiency and manage their health. Over time, the accuracy of advice improves based on the feedback and analysis results, enabling more personalized support.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user wears a wearable device. The device measures heart rate and stress level in real time and collects this as biometric data. The input is real-time biometric data from the user, and the output is data that temporarily stores this information. Specifically, the device checks the heart rate every second and immediately records any abnormal values.

[0278] Step 2:

[0279] The wearable device acting as the terminal periodically transmits stored biometric information to a server. The input is temporarily stored biometric information, and the output is data transferred to the server. This transmission is performed using Bluetooth or Wi-Fi, and information is sent at short intervals (e.g., every minute).

[0280] Step 3:

[0281] The server receives biometric information and performs preprocessing of the data. The input is the biometric information sent from the terminal, and the output is the cleansed data. The preprocessing of the data includes the complementation of missing values and the detection and correction of outliers. When the server finds an outlier, it corrects it with, for example, the average value.

[0282] Step 4:

[0283] The server integrates the received data and the existing business data and performs analysis using a generative AI model. The input is the cleansed biometric information and business data, and the output is personalized advice for providing to the user. Machine learning algorithms are used for the analysis, and the prompt sentence "How can the user relax?" is used as the input to the generative AI model.

[0284] Step 5:

[0285] The server packages the generated advice and sends it back to the terminal. The input is the analyzed advice, and the output is the data formatted for transmission. Specifically, the advice text is converted into an appropriate format and sent over the communication protocol.

[0286] Step 6:

[0287] The terminal notifies the user of the received advice. The input is the advice data sent from the server, and the output is the displayed message or voice guidance. For example, a message "Your heart rate is high. It is recommended to take deep breaths for a few minutes" is displayed on a visual display device, or the instructions are played as voice guidance.

[0288] Step 7:

[0289] The user follows the advice and sends feedback to the server via their device. The input is the user's thoughts and evaluation of the advice's effectiveness, and the output is feedback information stored on the server. This feedback information is stored on the server and used to improve the generated AI model in the next learning cycle.

[0290] (Application Example 1)

[0291] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0292] In modern society, the importance of individual health management and performance improvement is increasing, but conventional systems have struggled to provide detailed feedback tailored to individual health conditions in real time. Furthermore, more advanced analysis and personalization are required to provide optimal advice to improve users' productivity in their daily lives. Therefore, the present invention aims to simultaneously achieve health management and productivity improvement by utilizing users' biometric information to generate real-time instructions tailored to their individual living situations and improving their accuracy.

[0293] 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.

[0294] In this invention, the server includes an acquisition device for acquiring biometric information, a communication device for transmitting the acquired biometric information to a computing resource, and a processing device for integrating and analyzing the biometric information and related activity data to generate optimal instructions. This makes it possible to monitor the user's health status in their daily life in real time and provide detailed advice based on that information.

[0295] "Biometric information" refers to data that indicates an individual's health and physiological state, including heart rate and stress levels.

[0296] A "data acquisition device" is a device used to collect a user's biometric information, and it utilizes wearable devices or sensors.

[0297] A "communication device" is a device used to transmit acquired biometric information to a remote server or other computing resources.

[0298] "Computational resources" refer to computers and cloud servers used to process and analyze collected data.

[0299] A "processing device" is a device that uses biological information and related activity data to perform analysis and generate optimal instructions and advice.

[0300] A "display device" is a device used to convey generated instructions or advice to the user, and includes screens, audio devices, and the like.

[0301] A "monitoring device" is a device that continuously monitors the user's health status and provides feedback as needed.

[0302] A "modification device" is a device that updates the model based on user feedback to improve the accuracy of the analysis.

[0303] An "adaptive device" is a device that optimizes instructions according to the individual user's living situation.

[0304] The system for realizing the present invention acquires biometric information using a wearable device worn by the user and analyzes that information to support individualized health management and performance improvement for the user. The embodiments of the system are described in detail below.

[0305] First, as a device for acquiring biometric information, the user wears a wearable device for sensing heart rate and stress level. This device incorporates sensors such as Apple Watch and Fitbit. The acquired biometric information is transmitted to the computing resources on the cloud via a communication device.

[0306] Next, the server receives these data and uses a processing device to integrate and analyze the biometric information and past activity data. For this analysis, a generative AI model such as OpenAI's GPT-4 is used to generate optimal instructions according to the user's health condition and stress level.

[0307] The generated instructions are notified to the user by a display device. The user can receive feedback through a home robot via voice or display. Specifically, when the user feels fatigued from long hours of work, advice such as "Let's take a break" is provided.

[0308] Furthermore, the server collects the user's reactions through a monitoring device and continuously updates the generative AI model by an adjustment device. As a result, the accuracy of the analysis improves over time, and instructions most suitable for individual situations are provided.

[0309] For example, for a user who routinely has high stress, the server sends a prompt such as "It seems that your current stress level is high. Please generate advice to calm down" to the AI model, generating appropriate advice and providing it to the user. This enables the user to improve productivity while maintaining a healthy lifestyle.

[0310] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user wears a wearable device to acquire biometric information. This device senses the user's heart rate and stress level in real time. The input includes the user's physiological data, and the output generates biometric signals.

[0313] Step 2:

[0314] The device transmits the acquired biometric information to a server in the cloud using a communication method. Here, the input is the biometric information signal from the wearable device, and the output is the transmission of data packets to the server.

[0315] Step 3:

[0316] The server uses a generative AI model to integrate and analyze the received biometric information. Inputs include biometric information sent to the server and historical activity data. Through data processing and calculations, it generates advice best suited to the user's current state. The output is individually personalized instructions and advice.

[0317] Step 4:

[0318] The server sends the generated advice to a terminal or home robot. The input is the generated advice, and the output is a notification message to the user. Notifications are delivered via voice commands or display.

[0319] Step 5:

[0320] The user provides feedback based on the advice received. For example, the user inputs into the terminal whether or not they will follow the advice given regarding taking breaks.

[0321] Step 6:

[0322] The server receives feedback from the user and updates the generated AI model using a tuning device. The input is the user's feedback, and the output is the updated model. This improves the accuracy of the analysis and enables the provision of more effective support.

[0323] 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.

[0324] This invention is a system for supporting sales activities based on the biometric information and emotional state of sales representatives. This system comprises a wearable device worn by the user, a server for analyzing data, and communication means to connect them.

[0325] Users can measure their biometric information in real time using wearable devices. In addition to heart rate and stress levels, the device utilizes an emotion engine to infer the user's emotional state. The data acquired in this way is sent to a server as it is collected.

[0326] The server continues to analyze biometric data and sales data in an integrated manner, enabling it to recognize the user's emotions based on biometric information. This emotion engine determines the user's current emotional state, and the analysis tool generates appropriate advice based on that. For example, if it is determined that the user is feeling stressed and their concentration is low, advice will be generated suggesting appropriate relaxation techniques or ways to proceed with business negotiations.

[0327] The generated advice is notified to the user via the terminal from the server. This notification is displayed on the screen or conveyed by voice, allowing the user to adjust their actions accordingly. The results of emotion recognition and the adjustments made to the advice play an important role in improving the efficiency of sales activities.

[0328] Furthermore, users send feedback on the effectiveness of the advice they receive to the server via their device. This feedback is used in the model's learning process on the server, leading to improvements in the quality of the advice generated. In this way, the entire system can provide a more appropriate approach to the user's individual needs.

[0329] This invention enables personalized sales support tailored to the user's emotional state, leading to improved sales performance and reduced user stress. This configuration utilizes an optimized model that learns the thought patterns of sales managers, continuously providing individually optimized feedback to each user.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] Users begin their sales activities wearing a wearable device. This device has the ability to measure heart rate, stress levels, and even emotional states in real time using an emotion engine.

[0333] Step 2:

[0334] The device transmits measured biometric and emotional data to a server. This data is transmitted in real time using communication methods, and is designed to minimize latency.

[0335] Step 3:

[0336] The server receives the transmitted biometric and emotional data, integrates it with sales data, and stores it temporarily. It then uses analytical tools to prepare to evaluate the user's state based on this data.

[0337] Step 4:

[0338] The emotion engine running on the server analyzes the user's current emotional state from the received data. Based on this analysis, the generative AI builds the foundational data to create optimal advice.

[0339] Step 5:

[0340] The server's analysis method uses a model that has learned the thought patterns of sales managers to generate advice tailored to the user's emotional state. This advice includes immediately actionable improvement suggestions for the sales situation the user is facing.

[0341] Step 6:

[0342] The server sends the generated advice to the terminal. The advice includes specific action suggestions and ways to refresh based on the user's current emotional state.

[0343] Step 7:

[0344] The terminal notifies the user of advice provided by the server. The notification is displayed as text on the terminal's screen or communicated via audio output.

[0345] Step 8:

[0346] Users will follow the advice given in notifications from their devices, for example, trying a suggested method of refreshing themselves during a tense business negotiation. This is expected to help the negotiation proceed more smoothly.

[0347] Step 9:

[0348] Users send feedback to the server via their device regarding the effectiveness of the advice provided. This feedback, based on the user's experience, helps improve future advice.

[0349] Step 10:

[0350] The server analyzes user feedback in conjunction with an emotion engine and updates the model using generative AI. This allows the system to continuously improve the accuracy of the advice it provides, enabling personalized support for each user.

[0351] (Example 2)

[0352] 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".

[0353] This invention relates to a system that utilizes users' biometric data and emotional states in their work activities to provide personalized support. Conventional methods have made it difficult to provide immediate and effective feedback on user stress management and improved work performance. This invention aims to solve this problem, optimizing user efficiency and reducing stress.

[0354] 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.

[0355] In this invention, the server includes a data collection means, a communication device, and an analysis device. This enables real-time emotion analysis and optimal business support based on biometric data.

[0356] "Biometric data" refers to information about a user's physical condition, such as their heart rate and stress level.

[0357] "Collection means" refers to a device for recording and collecting biological data in real time.

[0358] A "communication device" is a device that has the function of transmitting collected data to an information processing device.

[0359] An "information processing device" is a computer system that receives data and performs analysis and processing on it.

[0360] An "analysis device" is a device used to integrate and analyze received biological data and business data.

[0361] "Business data" refers to information related to business activities, including sales results and customer information.

[0362] A "display device" is a device that visualizes the instructions generated for the user.

[0363] A "learning device" is a device that has an algorithm for improving a system model based on feedback.

[0364] An "optimization device" is a device that adjusts the instructions provided to the user to suit their individual needs.

[0365] This invention is a system that supports work activities based on the user's biometric data and emotional state. A wearable device worn by the user acquires biometric data such as heart rate and stress level in real time. This wearable device measures biometric information and transmits it to a server via a communication device. Wireless communication technologies such as Bluetooth and Wi-Fi are commonly used as the means of communication.

[0366] The server integrates received biometric data with data related to business activities and analyzes the data using a generative AI model. This generative AI model has learned from past sales data and user feedback, enabling it to generate highly personalized instructions. Based on the analysis results, the server provides the user with the most suitable instructions in real time. For example, it can suggest relaxation methods if stress levels are high, or offer advice on how to proceed with tasks if concentration levels are low.

[0367] The generated instructions are communicated to the user via the terminal. These notifications are either displayed visually on the screen or transmitted verbally using speech synthesis technology. Based on the content of the notification, the user can immediately adjust their actions in their work activities.

[0368] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[0369] "Please suggest relaxation methods to offer to sales representatives who are feeling stressed."

[0370] Furthermore, users input feedback on their devices and send it to the server. This feedback is used as training data to improve the quality of the generated AI model. This allows the system to provide more appropriate support to the individual needs of each user.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user wears a wearable device that acquires biometric information such as heart rate and stress level in real time. The user's biometric data serves as input, which the wearable device measures and prepares to transmit as output to a built-in communication device. This device uses Bluetooth or similar technologies to enable data transmission.

[0374] Step 2:

[0375] Biometric information acquired from a wearable device is transmitted to a server via a communication method. The input is biometric data from the device, and the output is the arrival of data at the server. Communication is conducted using a secure protocol and has the capability to process large amounts of data instantly.

[0376] Step 3:

[0377] The server processes the received biometric information and integrates it with pre-collected business data. This integrated dataset is then used as input for analysis using a generative AI model. Data processing includes noise reduction and data normalization. The output provides analysis results regarding the user's emotional state. For example, it might indicate that the user is highly stressed.

[0378] Step 4:

[0379] The server generates optimal instructions based on the analysis results. Integrated analysis data and a generative AI model are used as input, and the output is specific business support advice. At this stage, the prompt sentences generated by the generative AI model are used to create relaxation techniques tailored to emotions and advice on how to proceed with work.

[0380] Step 5:

[0381] The generated advice is sent to the terminal and notified to the user. The input is instruction data from the server, and the output is visualized advice on the terminal. Display and voice instructions are executed, and the user can adjust their actions according to the advice.

[0382] Step 6:

[0383] The user evaluates the effectiveness of the advice received and inputs feedback into the device. The input is subjective feedback data from the user, and the output is feedback sent to the server. This feedback will be used as material for training and fine-tuning future generative AI models.

[0384] Step 7:

[0385] The server receives feedback from users and uses it to improve the generated AI model. The feedback is then used as new input data to generate output that updates the model. This improves the accuracy and personalization of the advice provided.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] There is a need to address the challenge that sales representatives and home users cannot receive appropriate advice tailored to their stress levels and emotional states on the spot, making it difficult to improve the efficiency of their activities and their quality of life.

[0389] 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.

[0390] In this invention, the server includes a measuring device for collecting biometric data, a communication device for transmitting the collected biometric data to an information processing device, an analysis device for integrating and analyzing the biometric data and sales data to generate optimal suggestions, and an emotion analysis device for inferring the user's emotional state and providing appropriate support. This makes it possible to understand the user's emotional state in real time and provide personalized advice tailored to individual needs.

[0391] "Biometric data" refers to information that indicates a user's physical condition, including heart rate and stress level.

[0392] A "measuring device" refers to equipment used to acquire biological data in real time, and specifically to wearable devices that are attached to the body to collect data.

[0393] A "communication device" refers to a means of transmitting biological data acquired by a measuring device to an information processing device, and is a device equipped with wireless communication capabilities.

[0394] An "information processing device" refers to a computer device that receives data from servers and other sources and performs processing and analysis.

[0395] An "analysis device" refers to a device with the processing power to integrate and analyze biometric data and sales data to generate optimal proposals.

[0396] A "display device" refers to a device that visually or audibly notifies the user of generated suggestions or advice.

[0397] An "emotion analysis device" refers to a device equipped with the function to infer a user's emotional state from their biometric data and provide appropriate support.

[0398] "Personalized advice" refers to suggestions optimized according to the individual user's needs and emotional state.

[0399] This invention provides a system that supports sales activities and communication within the home. The core of the system lies in analyzing emotional states using biometric data and providing personalized advice to the user.

[0400] First, the user wears a measuring device, such as a wearable device, to acquire biometric data. This device collects biometric data such as heart rate and stress level in real time and transmits it to an information processing device via a communication device. The information processing device, specifically a server, processes the collected data through an analysis device and performs sentiment analysis to estimate the user's current emotional state. This analysis uses a generative AI model that recognizes emotions based on patterns learned from past data and feedback.

[0401] Based on the analysis results, the server generates optimal suggestions and notifies the user through a display device. These notifications are conveyed using visual display devices or audio. For example, if the user is feeling stressed, the server might suggest "relaxation techniques" or "stress-relieving music."

[0402] This system utilizes biometric and emotional data in an integrated manner to provide users with truly personalized advice. This can lead to increased efficiency in sales activities and reduced stress at home.

[0403] As a concrete example, consider a situation where a user experiences daily stress at work. When the system detects that the user's heart rate is elevated, it suggests, "Why not try these three deep breathing techniques to relax?" The system then receives feedback from the user on whether the suggestion was effective, analyzes this feedback using a learning device, and uses it to generate more accurate suggestions in the future.

[0404] An example of a prompt used with a generative AI model is: "Suggest relaxation techniques for a user who is tired. Heart rate is 90, stress level is high."

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The user wears a measuring device, and biometric data is acquired. This device acquires biometric data such as heart rate and stress level, converts it into digital signals, and outputs them.

[0408] Step 2:

[0409] The terminal receives biometric data transmitted from the measuring device and sends the data to the information processing device via the communication device. Here, the biometric data is provided as input to the server.

[0410] Step 3:

[0411] The server processes the received biometric data using an analysis device. It analyzes the data using a generative AI model to infer the emotional state. In this process, calculations are performed to estimate emotions based on stress levels and heart rate from the input biometric data, and an output representing the emotional state is obtained.

[0412] Step 4:

[0413] Based on the analysis results, the server generates suggestions tailored to the user. Here, it utilizes prompts and other elements from the AI ​​model to generate data that serves as advice based on the user's state.

[0414] Step 5:

[0415] The server sends the generated proposal to the display device. The terminal outputs the proposal visually or audibly to notify the user. The user can then review and accept the proposal.

[0416] Step 6:

[0417] The user enters feedback on the effectiveness of the suggestion into the device. The device then sends this feedback back to the server.

[0418] Step 7:

[0419] The server uses the received feedback to update the analysis model through the learning device. In this process, user feedback is input, and the model is trained, resulting in output that improves the accuracy of future suggestions.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] The system of this invention consists of wearable devices worn by sales representatives and a server that receives and analyzes the data transmitted from them. This system utilizes the biometric information of sales representatives to provide real-time feedback to support sales activities.

[0437] First, the user, a sales representative, wears a wearable device. This device continuously measures biometric information such as the user's heart rate and stress level. The wearable device, acting as a terminal, transmits this biometric information to a server in the cloud using communication methods.

[0438] The server integrates and analyzes the received biometric information with previously accumulated sales data. This analysis is performed by a generative AI, using a model based on the thought patterns of sales managers. As a result, the server generates personalized advice in real time, tailored to the user's situation.

[0439] The generated advice is then sent back to the terminal via a communication method. The terminal notifies the user of this advice. The notification can be a message displayed on the screen or voice guidance. For example, if the user's heart rate increases and they become more nervous during a business meeting, the server generates specific advice such as "We recommend taking a few minutes to take deep breaths and relax" and communicates it to the user through the terminal.

[0440] Furthermore, the generating AI continuously updates its model by receiving user feedback from the server. This allows the system to learn from the feedback and analysis results, improving the accuracy of its advice over time. As a result, users receive optimal support tailored to their individual circumstances.

[0441] In this way, the present invention uses biometric information and sales data to support the work of sales representatives and realize health management and improved sales performance.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] Users begin their daily work by wearing a wearable device. This device has the capability to collect the user's biometric information in real time. Specifically, it continuously records data such as heart rate and stress level.

[0445] Step 2:

[0446] The device (wearable device) transmits collected biometric information to a server using a predetermined communication method. Communication takes place in real time, minimizing data delay.

[0447] Step 3:

[0448] The server receives biometric information transmitted from the terminal and temporarily stores it in a database for integrated analysis with previously collected sales data.

[0449] Step 4:

[0450] The server uses a generation AI to analyze received biometric information and sales data. Leveraging a model based on the sales manager's thought patterns, it evaluates the user's state and generates individually optimized advice.

[0451] Step 5:

[0452] The server sends the generated advice to the terminal. The advice includes specific action suggestions and refresh methods tailored to the user's current state.

[0453] Step 6:

[0454] The device notifies the user of advice sent from the server. This notification is either displayed as text on the device's screen or delivered via audio output. Specific examples include suggestions such as "Take time to take a deep breath."

[0455] Step 7:

[0456] Users adjust their actions based on the advice they receive. If they are in a business negotiation, they can take a short break as suggested.

[0457] Step 8:

[0458] Users provide feedback on the effectiveness of the advice through their devices. This feedback is based on the user's experience.

[0459] Step 9:

[0460] The server receives and analyzes user feedback. Based on this feedback, the generating AI updates its model and further optimizes advice for future use. This improves the accuracy and effectiveness of the advice provided.

[0461] (Example 1)

[0462] 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."

[0463] In sales settings, there is a need for technology that utilizes individual biometric information in real time to manage stress and improve performance. However, conventional systems have not adequately integrated biometric information with sales data, making it difficult to provide personalized advice. Furthermore, efficiently utilizing user feedback to enhance the effectiveness of advice has also been a challenge.

[0464] 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.

[0465] In this invention, the server includes a device for acquiring biometric information, a communication method for transmitting it to an information processing device, and a computation method for analyzing and generating personalized advice. This makes it possible to provide advice in real time based on an individual's biometric information and to update the model based on user feedback.

[0466] "Device" refers to hardware or software used to acquire biometric information and measure the user's health data.

[0467] "Communication method" refers to a means of transmitting acquired biometric information to an information processing device, and includes wireless and wired communication.

[0468] The term "calculation method" refers to the process of analyzing received biometric information and accumulated business information to generate personalized advice.

[0469] "Notification method" refers to the means by which the generated advice is communicated to the individual, including display information and audio guidance.

[0470] "Learning method" refers to the process of improving the accuracy and adaptability of computational models based on feedback from individuals.

[0471] The "optimization method" is a process for individually optimizing the generated advice using a model that has learned the thought patterns of business managers.

[0472] The system of the present invention consists primarily of a wearable device worn by a sales representative and a server for analyzing the data transmitted from it. The user wears a wearable device with built-in biosensors that measure heart rate and stress levels. This device has communication capabilities to transmit biometric information to a server in the cloud via Bluetooth or Wi-Fi.

[0473] The server uses received biometric information and historical sales data to analyze the data using a generative AI model. Machine learning algorithms are applied to the analysis, evaluating the user's state and generating advice based on the sales manager's thought patterns. This model continuously learns from user feedback and is continuously optimized. Specifically, prompts such as "How can the user relax?" are input to the AI, which then generates appropriate advice.

[0474] The generated advice is sent from the server to the wearable device and displayed on the device's screen or communicated to the user via voice. For example, if a user's heart rate increases during work, the server will generate advice such as, "Your heart rate is high; we recommend taking deep breaths for a few minutes."

[0475] This system allows users to receive continuous feedback in their daily work, which can be used to improve work efficiency and manage their health. Over time, the accuracy of advice improves based on the feedback and analysis results, enabling more personalized support.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The user wears a wearable device. The device measures heart rate and stress level in real time and collects this as biometric data. The input is real-time biometric data from the user, and the output is data that temporarily stores this information. Specifically, the device checks the heart rate every second and immediately records any abnormal values.

[0479] Step 2:

[0480] The wearable device acting as the terminal periodically transmits stored biometric information to a server. The input is temporarily stored biometric information, and the output is data transferred to the server. This transmission is performed using Bluetooth or Wi-Fi, and information is sent at short intervals (e.g., every minute).

[0481] Step 3:

[0482] The server receives biometric data and performs data preprocessing. The input is biometric data sent from the terminal, and the output is cleansed data. Data preprocessing includes imputing missing values ​​and detecting and correcting outliers. If outliers are found, the server corrects them, for example, by using the mean value.

[0483] Step 4:

[0484] The server integrates the received data with existing sales data and performs analysis using a generative AI model. The input consists of cleansed biometric information and sales data, and the output is personalized advice to be provided to the user. Machine learning algorithms are used for the analysis, and the prompt "How can the user relax?" is used as input to the generative AI model.

[0485] Step 5:

[0486] The server packages the generated advice and sends it back to the terminal. The input is the parsed advice, and the output is data formatted for transmission. Specifically, the advice text is converted to the appropriate format and transmitted via the communication protocol.

[0487] Step 6:

[0488] The terminal notifies the user of the advice it has received. The input is advice data sent from the server, and the output is a displayed message or audio guidance. For example, it might display a message on a visual display device saying, "Your heart rate is high; we recommend taking deep breaths for a few minutes," or play instructions as an audio guide.

[0489] Step 7:

[0490] The user follows the advice and sends feedback to the server via their device. The input is the user's thoughts and evaluation of the advice's effectiveness, and the output is feedback information stored on the server. This feedback information is stored on the server and used to improve the generated AI model in the next learning cycle.

[0491] (Application Example 1)

[0492] 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."

[0493] In modern society, the importance of individual health management and performance improvement is increasing, but conventional systems have struggled to provide detailed feedback tailored to individual health conditions in real time. Furthermore, more advanced analysis and personalization are required to provide optimal advice to improve users' productivity in their daily lives. Therefore, the present invention aims to simultaneously achieve health management and productivity improvement by utilizing users' biometric information to generate real-time instructions tailored to their individual living situations and improving their accuracy.

[0494] 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.

[0495] In this invention, the server includes an acquisition device for acquiring biometric information, a communication device for transmitting the acquired biometric information to a computing resource, and a processing device for integrating and analyzing the biometric information and related activity data to generate optimal instructions. This makes it possible to monitor the user's health status in their daily life in real time and provide detailed advice based on that information.

[0496] "Biometric information" refers to data that indicates an individual's health and physiological state, including heart rate and stress levels.

[0497] A "data acquisition device" is a device used to collect a user's biometric information, and it utilizes wearable devices or sensors.

[0498] A "communication device" is a device used to transmit acquired biometric information to a remote server or other computing resources.

[0499] "Computational resources" refer to computers and cloud servers used to process and analyze collected data.

[0500] A "processing device" is a device that uses biological information and related activity data to perform analysis and generate optimal instructions and advice.

[0501] A "display device" is a device used to convey generated instructions or advice to the user, and includes screens, audio devices, and the like.

[0502] A "monitoring device" is a device that continuously monitors the user's health status and provides feedback as needed.

[0503] A "modification device" is a device that updates the model based on user feedback to improve the accuracy of the analysis.

[0504] An "adaptive device" is a device that optimizes instructions according to the individual user's living situation.

[0505] The system for realizing the present invention acquires biometric information using a wearable device worn by the user and analyzes that information to support individualized health management and performance improvement for the user. The embodiments of the system are described in detail below.

[0506] First, the user wears a wearable device that senses heart rate and stress level as a biometric data acquisition device. This device has built-in sensors such as those found in Apple Watch and Fitbit. The acquired biometric data is transmitted to computing resources in the cloud via a communication device.

[0507] Next, the server receives this data and uses a processing unit to integrate and analyze the biometric information and past activity data. This analysis uses a generative AI model such as OpenAI's GPT-4 to generate optimal instructions tailored to the user's health status and stress level.

[0508] The generated instructions are communicated to the user via a display device. The user can receive feedback through the home robot via voice or display. Specifically, if the user feels fatigued from working for a long time, they will be advised to "take a break."

[0509] Furthermore, the server collects user feedback through monitoring devices, and the adjustment device continuously updates the generated AI model. As a result, the accuracy of the analysis improves over time, and the most appropriate instructions for individual situations are provided.

[0510] For example, if a user experiences high levels of stress on a daily basis, the server can send a prompt to an AI model saying, "Your current stress level appears high. Please generate advice to help you calm down." Appropriate advice will then be generated and provided to the user. This allows the user to maintain a healthy lifestyle while improving their productivity.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The user wears a wearable device to acquire biometric information. This device senses the user's heart rate and stress level in real time. The input includes the user's physiological data, and the output generates biometric signals.

[0514] Step 2:

[0515] The device transmits the acquired biometric information to a server in the cloud using a communication method. Here, the input is the biometric information signal from the wearable device, and the output is the transmission of data packets to the server.

[0516] Step 3:

[0517] The server uses a generative AI model to integrate and analyze the received biometric information. Inputs include biometric information sent to the server and historical activity data. Through data processing and calculations, it generates advice best suited to the user's current state. The output is individually personalized instructions and advice.

[0518] Step 4:

[0519] The server sends the generated advice to a terminal or home robot. The input is the generated advice, and the output is a notification message to the user. Notifications are delivered via voice commands or display.

[0520] Step 5:

[0521] The user provides feedback based on the advice received. For example, the user inputs into the terminal whether or not they will follow the advice given regarding taking breaks.

[0522] Step 6:

[0523] The server receives feedback from the user and updates the generated AI model using a tuning device. The input is the user's feedback, and the output is the updated model. This improves the accuracy of the analysis and enables the provision of more effective support.

[0524] 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.

[0525] This invention is a system for supporting sales activities based on the biometric information and emotional state of sales representatives. This system comprises a wearable device worn by the user, a server for analyzing data, and communication means to connect them.

[0526] Users can measure their biometric information in real time using wearable devices. In addition to heart rate and stress levels, the device utilizes an emotion engine to infer the user's emotional state. The data acquired in this way is sent to a server as it is collected.

[0527] The server continues to analyze biometric data and sales data in an integrated manner, enabling it to recognize the user's emotions based on biometric information. This emotion engine determines the user's current emotional state, and the analysis tool generates appropriate advice based on that. For example, if it is determined that the user is feeling stressed and their concentration is low, advice will be generated suggesting appropriate relaxation techniques or ways to proceed with business negotiations.

[0528] The generated advice is notified to the user via the terminal from the server. This notification is displayed on the screen or conveyed by voice, allowing the user to adjust their actions accordingly. The results of emotion recognition and the adjustments made to the advice play an important role in improving the efficiency of sales activities.

[0529] Furthermore, users send feedback on the effectiveness of the advice they receive to the server via their device. This feedback is used in the model's learning process on the server, leading to improvements in the quality of the advice generated. In this way, the entire system can provide a more appropriate approach to the user's individual needs.

[0530] This invention enables personalized sales support tailored to the user's emotional state, leading to improved sales performance and reduced user stress. This configuration utilizes an optimized model that learns the thought patterns of sales managers, continuously providing individually optimized feedback to each user.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] Users begin their sales activities wearing a wearable device. This device has the ability to measure heart rate, stress levels, and even emotional states in real time using an emotion engine.

[0534] Step 2:

[0535] The device transmits measured biometric and emotional data to a server. This data is transmitted in real time using communication methods, and is designed to minimize latency.

[0536] Step 3:

[0537] The server receives the transmitted biometric and emotional data, integrates it with sales data, and stores it temporarily. It then uses analytical tools to prepare to evaluate the user's state based on this data.

[0538] Step 4:

[0539] The emotion engine running on the server analyzes the user's current emotional state from the received data. Based on this analysis, the generative AI builds the foundational data to create optimal advice.

[0540] Step 5:

[0541] The server's analysis method uses a model that has learned the thought patterns of sales managers to generate advice tailored to the user's emotional state. This advice includes immediately actionable improvement suggestions for the sales situation the user is facing.

[0542] Step 6:

[0543] The server sends the generated advice to the terminal. The advice includes specific action suggestions and ways to refresh based on the user's current emotional state.

[0544] Step 7:

[0545] The terminal notifies the user of advice provided by the server. The notification is displayed as text on the terminal's screen or communicated via audio output.

[0546] Step 8:

[0547] Users will follow the advice given in notifications from their devices, for example, trying a suggested method of refreshing themselves during a tense business negotiation. This is expected to help the negotiation proceed more smoothly.

[0548] Step 9:

[0549] Users send feedback to the server via their device regarding the effectiveness of the advice provided. This feedback, based on the user's experience, helps improve future advice.

[0550] Step 10:

[0551] The server analyzes user feedback in conjunction with an emotion engine and updates the model using generative AI. This allows the system to continuously improve the accuracy of the advice it provides, enabling personalized support for each user.

[0552] (Example 2)

[0553] 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."

[0554] This invention relates to a system that utilizes users' biometric data and emotional states in their work activities to provide personalized support. Conventional methods have made it difficult to provide immediate and effective feedback on user stress management and improved work performance. This invention aims to solve this problem, optimizing user efficiency and reducing stress.

[0555] 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.

[0556] In this invention, the server includes a data collection means, a communication device, and an analysis device. This enables real-time emotion analysis and optimal business support based on biometric data.

[0557] "Biometric data" refers to information about a user's physical condition, such as their heart rate and stress level.

[0558] "Collection means" refers to a device for recording and collecting biological data in real time.

[0559] A "communication device" is a device that has the function of transmitting collected data to an information processing device.

[0560] An "information processing device" is a computer system that receives data and performs analysis and processing on it.

[0561] An "analysis device" is a device used to integrate and analyze received biological data and business data.

[0562] "Business data" refers to information related to business activities, including sales results and customer information.

[0563] A "display device" is a device that visualizes the instructions generated for the user.

[0564] A "learning device" is a device that has an algorithm for improving a system model based on feedback.

[0565] An "optimization device" is a device that adjusts the instructions provided to the user to suit their individual needs.

[0566] This invention is a system that supports work activities based on the user's biometric data and emotional state. A wearable device worn by the user acquires biometric data such as heart rate and stress level in real time. This wearable device measures biometric information and transmits it to a server via a communication device. Wireless communication technologies such as Bluetooth and Wi-Fi are commonly used as the means of communication.

[0567] The server integrates received biometric data with data related to business activities and analyzes the data using a generative AI model. This generative AI model has learned from past sales data and user feedback, enabling it to generate highly personalized instructions. Based on the analysis results, the server provides the user with the most suitable instructions in real time. For example, it can suggest relaxation methods if stress levels are high, or offer advice on how to proceed with tasks if concentration levels are low.

[0568] The generated instructions are communicated to the user via the terminal. These notifications are either displayed visually on the screen or transmitted verbally using speech synthesis technology. Based on the content of the notification, the user can immediately adjust their actions in their work activities.

[0569] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[0570] "Please suggest relaxation methods to offer to sales representatives who are feeling stressed."

[0571] Furthermore, users input feedback on their devices and send it to the server. This feedback is used as training data to improve the quality of the generated AI model. This allows the system to provide more appropriate support to the individual needs of each user.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The user wears a wearable device that acquires biometric information such as heart rate and stress level in real time. The user's biometric data serves as input, which the wearable device measures and prepares to transmit as output to a built-in communication device. This device uses Bluetooth or similar technologies to enable data transmission.

[0575] Step 2:

[0576] Biometric information acquired from a wearable device is transmitted to a server via a communication method. The input is biometric data from the device, and the output is the arrival of data at the server. Communication is conducted using a secure protocol and has the capability to process large amounts of data instantly.

[0577] Step 3:

[0578] The server processes the received biometric information and integrates it with pre-collected business data. This integrated dataset is then used as input for analysis using a generative AI model. Data processing includes noise reduction and data normalization. The output provides analysis results regarding the user's emotional state. For example, it might indicate that the user is highly stressed.

[0579] Step 4:

[0580] The server generates optimal instructions based on the analysis results. Integrated analysis data and a generative AI model are used as input, and the output is specific business support advice. At this stage, the prompt sentences generated by the generative AI model are used to create relaxation techniques tailored to emotions and advice on how to proceed with work.

[0581] Step 5:

[0582] The generated advice is sent to the terminal and notified to the user. The input is instruction data from the server, and the output is visualized advice on the terminal. Display and voice instructions are executed, and the user can adjust their actions according to the advice.

[0583] Step 6:

[0584] The user evaluates the effectiveness of the advice received and inputs feedback into the device. The input is subjective feedback data from the user, and the output is feedback sent to the server. This feedback will be used as material for training and fine-tuning future generative AI models.

[0585] Step 7:

[0586] The server receives feedback from users and uses it to improve the generated AI model. The feedback is then used as new input data to generate output that updates the model. This improves the accuracy and personalization of the advice provided.

[0587] (Application Example 2)

[0588] 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."

[0589] There is a need to address the challenge that sales representatives and home users cannot receive appropriate advice tailored to their stress levels and emotional states on the spot, making it difficult to improve the efficiency of their activities and their quality of life.

[0590] 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.

[0591] In this invention, the server includes a measuring device for collecting biometric data, a communication device for transmitting the collected biometric data to an information processing device, an analysis device for integrating and analyzing the biometric data and sales data to generate optimal suggestions, and an emotion analysis device for inferring the user's emotional state and providing appropriate support. This makes it possible to understand the user's emotional state in real time and provide personalized advice tailored to individual needs.

[0592] "Biometric data" refers to information that indicates a user's physical condition, including heart rate and stress level.

[0593] A "measuring device" refers to equipment used to acquire biological data in real time, and specifically to wearable devices that are attached to the body to collect data.

[0594] A "communication device" refers to a means of transmitting biological data acquired by a measuring device to an information processing device, and is a device equipped with wireless communication capabilities.

[0595] An "information processing device" refers to a computer device that receives data from servers and other sources and performs processing and analysis.

[0596] An "analysis device" refers to a device with the processing power to integrate and analyze biometric data and sales data to generate optimal proposals.

[0597] A "display device" refers to a device that visually or audibly notifies the user of generated suggestions or advice.

[0598] An "emotion analysis device" refers to a device equipped with the function to infer a user's emotional state from their biometric data and provide appropriate support.

[0599] "Personalized advice" refers to suggestions optimized according to the individual user's needs and emotional state.

[0600] This invention provides a system that supports sales activities and communication within the home. The core of the system lies in analyzing emotional states using biometric data and providing personalized advice to the user.

[0601] First, the user wears a measuring device, such as a wearable device, to acquire biometric data. This device collects biometric data such as heart rate and stress level in real time and transmits it to an information processing device via a communication device. The information processing device, specifically a server, processes the collected data through an analysis device and performs sentiment analysis to estimate the user's current emotional state. This analysis uses a generative AI model that recognizes emotions based on patterns learned from past data and feedback.

[0602] Based on the analysis results, the server generates optimal suggestions and notifies the user through a display device. These notifications are conveyed using visual display devices or audio. For example, if the user is feeling stressed, the server might suggest "relaxation techniques" or "stress-relieving music."

[0603] This system utilizes biometric and emotional data in an integrated manner to provide users with truly personalized advice. This can lead to increased efficiency in sales activities and reduced stress at home.

[0604] As a concrete example, consider a situation where a user experiences daily stress at work. When the system detects that the user's heart rate is elevated, it suggests, "Why not try these three deep breathing techniques to relax?" The system then receives feedback from the user on whether the suggestion was effective, analyzes this feedback using a learning device, and uses it to generate more accurate suggestions in the future.

[0605] An example of a prompt used with a generative AI model is: "Suggest relaxation techniques for a user who is tired. Heart rate is 90, stress level is high."

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The user wears a measuring device, and biometric data is acquired. This device acquires biometric data such as heart rate and stress level, converts it into digital signals, and outputs them.

[0609] Step 2:

[0610] The terminal receives biometric data transmitted from the measuring device and sends the data to the information processing device via the communication device. Here, the biometric data is provided as input to the server.

[0611] Step 3:

[0612] The server processes the received biometric data using an analysis device. It analyzes the data using a generative AI model to infer the emotional state. In this process, calculations are performed to estimate emotions based on stress levels and heart rate from the input biometric data, and an output representing the emotional state is obtained.

[0613] Step 4:

[0614] Based on the analysis results, the server generates suggestions tailored to the user. Here, it utilizes prompts and other elements from the AI ​​model to generate data that serves as advice based on the user's state.

[0615] Step 5:

[0616] The server sends the generated proposal to the display device. The terminal outputs the proposal visually or audibly to notify the user. The user can then review and accept the proposal.

[0617] Step 6:

[0618] The user enters feedback on the effectiveness of the suggestion into the device. The device then sends this feedback back to the server.

[0619] Step 7:

[0620] The server uses the received feedback to update the analysis model through the learning device. In this process, user feedback is input, and the model is trained, resulting in output that improves the accuracy of future suggestions.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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".

[0638] The system of this invention consists of wearable devices worn by sales representatives and a server that receives and analyzes the data transmitted from them. This system utilizes the biometric information of sales representatives to provide real-time feedback to support sales activities.

[0639] First, the user, a sales representative, wears a wearable device. This device continuously measures biometric information such as the user's heart rate and stress level. The wearable device, acting as a terminal, transmits this biometric information to a server in the cloud using communication methods.

[0640] The server integrates and analyzes the received biometric information with previously accumulated sales data. This analysis is performed by a generative AI, using a model based on the thought patterns of sales managers. As a result, the server generates personalized advice in real time, tailored to the user's situation.

[0641] The generated advice is then sent back to the terminal via a communication method. The terminal notifies the user of this advice. The notification can be a message displayed on the screen or voice guidance. For example, if the user's heart rate increases and they become more nervous during a business meeting, the server generates specific advice such as "We recommend taking a few minutes to take deep breaths and relax" and communicates it to the user through the terminal.

[0642] Furthermore, the generating AI continuously updates its model by receiving user feedback from the server. This allows the system to learn from the feedback and analysis results, improving the accuracy of its advice over time. As a result, users receive optimal support tailored to their individual circumstances.

[0643] In this way, the present invention uses biometric information and sales data to support the work of sales representatives and realize health management and improved sales performance.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] Users begin their daily work by wearing a wearable device. This device has the capability to collect the user's biometric information in real time. Specifically, it continuously records data such as heart rate and stress level.

[0647] Step 2:

[0648] The device (wearable device) transmits collected biometric information to a server using a predetermined communication method. Communication takes place in real time, minimizing data delay.

[0649] Step 3:

[0650] The server receives biometric information transmitted from the terminal and temporarily stores it in a database for integrated analysis with previously collected sales data.

[0651] Step 4:

[0652] The server uses a generation AI to analyze received biometric information and sales data. Leveraging a model based on the sales manager's thought patterns, it evaluates the user's state and generates individually optimized advice.

[0653] Step 5:

[0654] The server sends the generated advice to the terminal. The advice includes specific action suggestions and refresh methods tailored to the user's current state.

[0655] Step 6:

[0656] The device notifies the user of advice sent from the server. This notification is either displayed as text on the device's screen or delivered via audio output. Specific examples include suggestions such as "Take time to take a deep breath."

[0657] Step 7:

[0658] Users adjust their actions based on the advice they receive. If they are in a business negotiation, they can take a short break as suggested.

[0659] Step 8:

[0660] Users provide feedback on the effectiveness of the advice through their devices. This feedback is based on the user's experience.

[0661] Step 9:

[0662] The server receives and analyzes user feedback. Based on this feedback, the generating AI updates its model and further optimizes advice for future use. This improves the accuracy and effectiveness of the advice provided.

[0663] (Example 1)

[0664] 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".

[0665] In sales settings, there is a need for technology that utilizes individual biometric information in real time to manage stress and improve performance. However, conventional systems have not adequately integrated biometric information with sales data, making it difficult to provide personalized advice. Furthermore, efficiently utilizing user feedback to enhance the effectiveness of advice has also been a challenge.

[0666] 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.

[0667] In this invention, the server includes a device for acquiring biometric information, a communication method for transmitting it to an information processing device, and a computation method for analyzing and generating personalized advice. This makes it possible to provide advice in real time based on an individual's biometric information and to update the model based on user feedback.

[0668] "Device" refers to hardware or software used to acquire biometric information and measure the user's health data.

[0669] "Communication method" refers to a means of transmitting acquired biometric information to an information processing device, and includes wireless and wired communication.

[0670] The term "calculation method" refers to the process of analyzing received biometric information and accumulated business information to generate personalized advice.

[0671] "Notification method" refers to the means by which the generated advice is communicated to the individual, including display information and audio guidance.

[0672] "Learning method" refers to the process of improving the accuracy and adaptability of computational models based on feedback from individuals.

[0673] The "optimization method" is a process for individually optimizing the generated advice using a model that has learned the thought patterns of business managers.

[0674] The system of the present invention consists primarily of a wearable device worn by a sales representative and a server for analyzing the data transmitted from it. The user wears a wearable device with built-in biosensors that measure heart rate and stress levels. This device has communication capabilities to transmit biometric information to a server in the cloud via Bluetooth or Wi-Fi.

[0675] The server uses received biometric information and historical sales data to analyze the data using a generative AI model. Machine learning algorithms are applied to the analysis, evaluating the user's state and generating advice based on the sales manager's thought patterns. This model continuously learns from user feedback and is continuously optimized. Specifically, prompts such as "How can the user relax?" are input to the AI, which then generates appropriate advice.

[0676] The generated advice is sent from the server to the wearable device and displayed on the device's screen or communicated to the user via voice. For example, if a user's heart rate increases during work, the server will generate advice such as, "Your heart rate is high; we recommend taking deep breaths for a few minutes."

[0677] This system allows users to receive continuous feedback in their daily work, which can be used to improve work efficiency and manage their health. Over time, the accuracy of advice improves based on the feedback and analysis results, enabling more personalized support.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The user wears a wearable device. The device measures heart rate and stress level in real time and collects this as biometric data. The input is real-time biometric data from the user, and the output is data that temporarily stores this information. Specifically, the device checks the heart rate every second and immediately records any abnormal values.

[0681] Step 2:

[0682] The wearable device acting as the terminal periodically transmits stored biometric information to a server. The input is temporarily stored biometric information, and the output is data transferred to the server. This transmission is performed using Bluetooth or Wi-Fi, and information is sent at short intervals (e.g., every minute).

[0683] Step 3:

[0684] The server receives biometric data and performs data preprocessing. The input is biometric data sent from the terminal, and the output is cleansed data. Data preprocessing includes imputing missing values ​​and detecting and correcting outliers. If outliers are found, the server corrects them, for example, by using the mean value.

[0685] Step 4:

[0686] The server integrates the received data with existing sales data and performs analysis using a generative AI model. The input consists of cleansed biometric information and sales data, and the output is personalized advice to be provided to the user. Machine learning algorithms are used for the analysis, and the prompt "How can the user relax?" is used as input to the generative AI model.

[0687] Step 5:

[0688] The server packages the generated advice and sends it back to the terminal. The input is the parsed advice, and the output is data formatted for transmission. Specifically, the advice text is converted to the appropriate format and transmitted via the communication protocol.

[0689] Step 6:

[0690] The terminal notifies the user of the advice it has received. The input is advice data sent from the server, and the output is a displayed message or audio guidance. For example, it might display a message on a visual display device saying, "Your heart rate is high; we recommend taking deep breaths for a few minutes," or play instructions as an audio guide.

[0691] Step 7:

[0692] The user follows the advice and sends feedback to the server via their device. The input is the user's thoughts and evaluation of the advice's effectiveness, and the output is feedback information stored on the server. This feedback information is stored on the server and used to improve the generated AI model in the next learning cycle.

[0693] (Application Example 1)

[0694] 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".

[0695] In modern society, the importance of individual health management and performance improvement is increasing, but conventional systems have struggled to provide detailed feedback tailored to individual health conditions in real time. Furthermore, more advanced analysis and personalization are required to provide optimal advice to improve users' productivity in their daily lives. Therefore, the present invention aims to simultaneously achieve health management and productivity improvement by utilizing users' biometric information to generate real-time instructions tailored to their individual living situations and improving their accuracy.

[0696] 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.

[0697] In this invention, the server includes an acquisition device for acquiring biometric information, a communication device for transmitting the acquired biometric information to a computing resource, and a processing device for integrating and analyzing the biometric information and related activity data to generate optimal instructions. This makes it possible to monitor the user's health status in their daily life in real time and provide detailed advice based on that information.

[0698] "Biometric information" refers to data that indicates an individual's health and physiological state, including heart rate and stress levels.

[0699] A "data acquisition device" is a device used to collect a user's biometric information, and it utilizes wearable devices or sensors.

[0700] A "communication device" is a device used to transmit acquired biometric information to a remote server or other computing resources.

[0701] "Computational resources" refer to computers and cloud servers used to process and analyze collected data.

[0702] A "processing device" is a device that uses biological information and related activity data to perform analysis and generate optimal instructions and advice.

[0703] A "display device" is a device used to convey generated instructions or advice to the user, and includes screens, audio devices, and the like.

[0704] A "monitoring device" is a device that continuously monitors the user's health status and provides feedback as needed.

[0705] A "modification device" is a device that updates the model based on user feedback to improve the accuracy of the analysis.

[0706] An "adaptive device" is a device that optimizes instructions according to the individual user's living situation.

[0707] The system for realizing the present invention acquires biometric information using a wearable device worn by the user and analyzes that information to support individualized health management and performance improvement for the user. The embodiments of the system are described in detail below.

[0708] First, the user wears a wearable device that senses heart rate and stress level as a biometric data acquisition device. This device has built-in sensors such as those found in Apple Watch and Fitbit. The acquired biometric data is transmitted to computing resources in the cloud via a communication device.

[0709] Next, the server receives this data and uses a processing unit to integrate and analyze the biometric information and past activity data. This analysis uses a generative AI model such as OpenAI's GPT-4 to generate optimal instructions tailored to the user's health status and stress level.

[0710] The generated instructions are communicated to the user via a display device. The user can receive feedback through the home robot via voice or display. Specifically, if the user feels fatigued from working for a long time, they will be advised to "take a break."

[0711] Furthermore, the server collects user feedback through monitoring devices, and the adjustment device continuously updates the generated AI model. As a result, the accuracy of the analysis improves over time, and the most appropriate instructions for individual situations are provided.

[0712] For example, if a user experiences high levels of stress on a daily basis, the server can send a prompt to an AI model saying, "Your current stress level appears high. Please generate advice to help you calm down." Appropriate advice will then be generated and provided to the user. This allows the user to maintain a healthy lifestyle while improving their productivity.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The user wears a wearable device to acquire biometric information. This device senses the user's heart rate and stress level in real time. The input includes the user's physiological data, and the output generates biometric signals.

[0716] Step 2:

[0717] The device transmits the acquired biometric information to a server in the cloud using a communication method. Here, the input is the biometric information signal from the wearable device, and the output is the transmission of data packets to the server.

[0718] Step 3:

[0719] The server uses a generative AI model to integrate and analyze the received biometric information. Inputs include biometric information sent to the server and historical activity data. Through data processing and calculations, it generates advice best suited to the user's current state. The output is individually personalized instructions and advice.

[0720] Step 4:

[0721] The server sends the generated advice to a terminal or home robot. The input is the generated advice, and the output is a notification message to the user. Notifications are delivered via voice commands or display.

[0722] Step 5:

[0723] The user provides feedback based on the advice received. For example, the user inputs into the terminal whether or not they will follow the advice given regarding taking breaks.

[0724] Step 6:

[0725] The server receives feedback from the user and updates the generated AI model using a tuning device. The input is the user's feedback, and the output is the updated model. This improves the accuracy of the analysis and enables the provision of more effective support.

[0726] 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.

[0727] This invention is a system for supporting sales activities based on the biometric information and emotional state of sales representatives. This system comprises a wearable device worn by the user, a server for analyzing data, and communication means to connect them.

[0728] Users can measure their biometric information in real time using wearable devices. In addition to heart rate and stress levels, the device utilizes an emotion engine to infer the user's emotional state. The data acquired in this way is sent to a server as it is collected.

[0729] The server continues to analyze biometric data and sales data in an integrated manner, enabling it to recognize the user's emotions based on biometric information. This emotion engine determines the user's current emotional state, and the analysis tool generates appropriate advice based on that. For example, if it is determined that the user is feeling stressed and their concentration is low, advice will be generated suggesting appropriate relaxation techniques or ways to proceed with business negotiations.

[0730] The generated advice is notified to the user via the terminal from the server. This notification is displayed on the screen or conveyed by voice, allowing the user to adjust their actions accordingly. The results of emotion recognition and the adjustments made to the advice play an important role in improving the efficiency of sales activities.

[0731] Furthermore, users send feedback on the effectiveness of the advice they receive to the server via their device. This feedback is used in the model's learning process on the server, leading to improvements in the quality of the advice generated. In this way, the entire system can provide a more appropriate approach to the user's individual needs.

[0732] This invention enables personalized sales support tailored to the user's emotional state, leading to improved sales performance and reduced user stress. This configuration utilizes an optimized model that learns the thought patterns of sales managers, continuously providing individually optimized feedback to each user.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] Users begin their sales activities wearing a wearable device. This device has the ability to measure heart rate, stress levels, and even emotional states in real time using an emotion engine.

[0736] Step 2:

[0737] The device transmits measured biometric and emotional data to a server. This data is transmitted in real time using communication methods, and is designed to minimize latency.

[0738] Step 3:

[0739] The server receives the transmitted biometric and emotional data, integrates it with sales data, and stores it temporarily. It then uses analytical tools to prepare to evaluate the user's state based on this data.

[0740] Step 4:

[0741] The emotion engine running on the server analyzes the user's current emotional state from the received data. Based on this analysis, the generative AI builds the foundational data to create optimal advice.

[0742] Step 5:

[0743] The server's analysis method uses a model that has learned the thought patterns of sales managers to generate advice tailored to the user's emotional state. This advice includes immediately actionable improvement suggestions for the sales situation the user is facing.

[0744] Step 6:

[0745] The server sends the generated advice to the terminal. The advice includes specific action suggestions and ways to refresh based on the user's current emotional state.

[0746] Step 7:

[0747] The terminal notifies the user of advice provided by the server. The notification is displayed as text on the terminal's screen or communicated via audio output.

[0748] Step 8:

[0749] Users will follow the advice given in notifications from their devices, for example, trying a suggested method of refreshing themselves during a tense business negotiation. This is expected to help the negotiation proceed more smoothly.

[0750] Step 9:

[0751] Users send feedback to the server via their device regarding the effectiveness of the advice provided. This feedback, based on the user's experience, helps improve future advice.

[0752] Step 10:

[0753] The server analyzes user feedback in conjunction with an emotion engine and updates the model using generative AI. This allows the system to continuously improve the accuracy of the advice it provides, enabling personalized support for each user.

[0754] (Example 2)

[0755] 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".

[0756] This invention relates to a system that utilizes users' biometric data and emotional states in their work activities to provide personalized support. Conventional methods have made it difficult to provide immediate and effective feedback on user stress management and improved work performance. This invention aims to solve this problem, optimizing user efficiency and reducing stress.

[0757] 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.

[0758] In this invention, the server includes a data collection means, a communication device, and an analysis device. This enables real-time emotion analysis and optimal business support based on biometric data.

[0759] "Biometric data" refers to information about a user's physical condition, such as their heart rate and stress level.

[0760] "Collection means" refers to a device for recording and collecting biological data in real time.

[0761] A "communication device" is a device that has the function of transmitting collected data to an information processing device.

[0762] An "information processing device" is a computer system that receives data and performs analysis and processing on it.

[0763] An "analysis device" is a device used to integrate and analyze received biological data and business data.

[0764] "Business data" refers to information related to business activities, including sales results and customer information.

[0765] A "display device" is a device that visualizes the instructions generated for the user.

[0766] A "learning device" is a device that has an algorithm for improving a system model based on feedback.

[0767] An "optimization device" is a device that adjusts the instructions provided to the user to suit their individual needs.

[0768] This invention is a system that supports work activities based on the user's biometric data and emotional state. A wearable device worn by the user acquires biometric data such as heart rate and stress level in real time. This wearable device measures biometric information and transmits it to a server via a communication device. Wireless communication technologies such as Bluetooth and Wi-Fi are commonly used as the means of communication.

[0769] The server integrates received biometric data with data related to business activities and analyzes the data using a generative AI model. This generative AI model has learned from past sales data and user feedback, enabling it to generate highly personalized instructions. Based on the analysis results, the server provides the user with the most suitable instructions in real time. For example, it can suggest relaxation methods if stress levels are high, or offer advice on how to proceed with tasks if concentration levels are low.

[0770] The generated instructions are communicated to the user via the terminal. These notifications are either displayed visually on the screen or transmitted verbally using speech synthesis technology. Based on the content of the notification, the user can immediately adjust their actions in their work activities.

[0771] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[0772] "Please suggest relaxation methods to offer to sales representatives who are feeling stressed."

[0773] Furthermore, users input feedback on their devices and send it to the server. This feedback is used as training data to improve the quality of the generated AI model. This allows the system to provide more appropriate support to the individual needs of each user.

[0774] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0775] Step 1:

[0776] The user wears a wearable device that acquires biometric information such as heart rate and stress level in real time. The user's biometric data serves as input, which the wearable device measures and prepares to transmit as output to a built-in communication device. This device uses Bluetooth or similar technologies to enable data transmission.

[0777] Step 2:

[0778] Biometric information acquired from a wearable device is transmitted to a server via a communication method. The input is biometric data from the device, and the output is the arrival of data at the server. Communication is conducted using a secure protocol and has the capability to process large amounts of data instantly.

[0779] Step 3:

[0780] The server processes the received biometric information and integrates it with pre-collected business data. This integrated dataset is then used as input for analysis using a generative AI model. Data processing includes noise reduction and data normalization. The output provides analysis results regarding the user's emotional state. For example, it might indicate that the user is highly stressed.

[0781] Step 4:

[0782] The server generates optimal instructions based on the analysis results. Integrated analysis data and a generative AI model are used as input, and the output is specific business support advice. At this stage, the prompt sentences generated by the generative AI model are used to create relaxation techniques tailored to emotions and advice on how to proceed with work.

[0783] Step 5:

[0784] The generated advice is sent to the terminal and notified to the user. The input is instruction data from the server, and the output is visualized advice on the terminal. Display and voice instructions are executed, and the user can adjust their actions according to the advice.

[0785] Step 6:

[0786] The user evaluates the effectiveness of the advice received and inputs feedback into the device. The input is subjective feedback data from the user, and the output is feedback sent to the server. This feedback will be used as material for training and fine-tuning future generative AI models.

[0787] Step 7:

[0788] The server receives feedback from users and uses it to improve the generated AI model. The feedback is then used as new input data to generate output that updates the model. This improves the accuracy and personalization of the advice provided.

[0789] (Application Example 2)

[0790] 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".

[0791] There is a need to address the challenge that sales representatives and home users cannot receive appropriate advice tailored to their stress levels and emotional states on the spot, making it difficult to improve the efficiency of their activities and their quality of life.

[0792] 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.

[0793] In this invention, the server includes a measuring device for collecting biometric data, a communication device for transmitting the collected biometric data to an information processing device, an analysis device for integrating and analyzing the biometric data and sales data to generate optimal suggestions, and an emotion analysis device for inferring the user's emotional state and providing appropriate support. This makes it possible to understand the user's emotional state in real time and provide personalized advice tailored to individual needs.

[0794] "Biometric data" refers to information that indicates a user's physical condition, including heart rate and stress level.

[0795] A "measuring device" refers to equipment used to acquire biological data in real time, and specifically to wearable devices that are attached to the body to collect data.

[0796] A "communication device" refers to a means of transmitting biological data acquired by a measuring device to an information processing device, and is a device equipped with wireless communication capabilities.

[0797] An "information processing device" refers to a computer device that receives data from servers and other sources and performs processing and analysis.

[0798] An "analysis device" refers to a device with the processing power to integrate and analyze biometric data and sales data to generate optimal proposals.

[0799] A "display device" refers to a device that visually or audibly notifies the user of generated suggestions or advice.

[0800] An "emotion analysis device" refers to a device equipped with the function to infer a user's emotional state from their biometric data and provide appropriate support.

[0801] "Personalized advice" refers to suggestions optimized according to the individual user's needs and emotional state.

[0802] This invention provides a system that supports sales activities and communication within the home. The core of the system lies in analyzing emotional states using biometric data and providing personalized advice to the user.

[0803] First, the user wears a measuring device, such as a wearable device, to acquire biometric data. This device collects biometric data such as heart rate and stress level in real time and transmits it to an information processing device via a communication device. The information processing device, specifically a server, processes the collected data through an analysis device and performs sentiment analysis to estimate the user's current emotional state. This analysis uses a generative AI model that recognizes emotions based on patterns learned from past data and feedback.

[0804] Based on the analysis results, the server generates optimal suggestions and notifies the user through a display device. These notifications are conveyed using visual display devices or audio. For example, if the user is feeling stressed, the server might suggest "relaxation techniques" or "stress-relieving music."

[0805] This system utilizes biometric and emotional data in an integrated manner to provide users with truly personalized advice. This can lead to increased efficiency in sales activities and reduced stress at home.

[0806] As a concrete example, consider a situation where a user experiences daily stress at work. When the system detects that the user's heart rate is elevated, it suggests, "Why not try these three deep breathing techniques to relax?" The system then receives feedback from the user on whether the suggestion was effective, analyzes this feedback using a learning device, and uses it to generate more accurate suggestions in the future.

[0807] An example of a prompt used with a generative AI model is: "Suggest relaxation techniques for a user who is tired. Heart rate is 90, stress level is high."

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The user wears a measuring device, and biometric data is acquired. This device acquires biometric data such as heart rate and stress level, converts it into digital signals, and outputs them.

[0811] Step 2:

[0812] The terminal receives biometric data transmitted from the measuring device and sends the data to the information processing device via the communication device. Here, the biometric data is provided as input to the server.

[0813] Step 3:

[0814] The server processes the received biometric data using an analysis device. It analyzes the data using a generative AI model to infer the emotional state. In this process, calculations are performed to estimate emotions based on stress levels and heart rate from the input biometric data, and an output representing the emotional state is obtained.

[0815] Step 4:

[0816] Based on the analysis results, the server generates suggestions tailored to the user. Here, it utilizes prompts and other elements from the AI ​​model to generate data that serves as advice based on the user's state.

[0817] Step 5:

[0818] The server sends the generated proposal to the display device. The terminal outputs the proposal visually or audibly to notify the user. The user can then review and accept the proposal.

[0819] Step 6:

[0820] The user enters feedback on the effectiveness of the suggestion into the device. The device then sends this feedback back to the server.

[0821] Step 7:

[0822] The server uses the received feedback to update the analysis model through the learning device. In this process, user feedback is input, and the model is trained, resulting in output that improves the accuracy of future suggestions.

[0823] 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.

[0824] 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.

[0825] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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."

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0844] The following is further disclosed regarding the embodiments described above.

[0845] (Claim 1)

[0846] Measurement means for collecting biological information,

[0847] A communication means for transmitting measured biometric information to a server,

[0848] An analytical method for integrating and analyzing biometric information and sales data to generate optimal advice,

[0849] A means of notifying the user of the generated advice,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, further comprising a learning means for receiving user feedback and updating the model of the analysis means based on that feedback.

[0853] (Claim 3)

[0854] The system according to claim 1, comprising an optimization means for individually optimizing the generated advice using a model that has learned the thought patterns of sales managers.

[0855] "Example 1"

[0856] (Claim 1)

[0857] A device for acquiring biological information,

[0858] A communication method for transmitting acquired biometric information to an information processing device,

[0859] A calculation method for generating personalized advice by analyzing received biometric information and accumulated business information,

[0860] A notification method for informing individuals of the generated advice,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising a learning method for receiving opinions from individuals and improving the model of the calculation method based on those opinions.

[0864] (Claim 3)

[0865] The system according to claim 1, comprising an optimization method for individually optimizing the generated advice using a model that has learned the thought patterns of business managers.

[0866] "Application Example 1"

[0867] (Claim 1)

[0868] A device for acquiring biological information,

[0869] A communication device for transmitting acquired biometric information to computing resources,

[0870] A processing device for integrating and analyzing biological information and related activity data to generate optimal instructions,

[0871] A display device for notifying the user of the generated instructions,

[0872] A monitoring device for monitoring the user's health status and providing continuous feedback,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising an adjustment device for receiving user responses and updating the model of the processing device based on those responses.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising an adaptive device for individualizing instructions generated according to individual living circumstances.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] A means of collecting biometric data,

[0881] A communication device for transmitting collected biometric data to an information processing device,

[0882] An analysis device that integrates and processes biometric data and business data to generate appropriate instructions,

[0883] A display device for informing the user of the generated instructions,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, further comprising a learning device for receiving user feedback and improving the model of the analysis device based on that feedback.

[0887] (Claim 3)

[0888] The system according to claim 1, further comprising an optimization device for individually optimizing generated instructions using a model that has learned the thought patterns of business managers.

[0889] "Application example 2 when combining with an emotional engine"

[0890] (Claim 1)

[0891] A measuring device for collecting biological data,

[0892] A communication device for transmitting collected biometric data to an information processing device,

[0893] An analytical device that integrates and analyzes biometric data and sales data to generate optimal proposals,

[0894] A display device for notifying the user of the generated suggestions,

[0895] An emotion analysis device for inferring the emotional state of users and providing appropriate support,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising a learning device for receiving user feedback and updating the model of the analysis device based on that feedback.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising an optimization device for individually optimizing generated proposals using a model that has learned the thought patterns of sales managers. [Explanation of Symbols]

[0901] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for acquiring biological information, A communication device for transmitting acquired biometric information to computing resources, A processing device for integrating and analyzing biological information and related activity data to generate optimal instructions, A display device for notifying the user of the generated instructions, A monitoring device for monitoring the user's health status and providing continuous feedback, A system that includes this.

2. The system according to claim 1, further comprising an adjustment device for receiving user responses and updating the model of the processing device based on those responses.

3. The system according to claim 1, further comprising an adaptive device for individualizing instructions generated according to individual living circumstances.

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