System

Abstract

A system is provided.SOLUTION: A system including a display device worn by a user, a mobile terminal capable of communicating with the display device, a generation artificial intelligence platform connected to a network via the mobile terminal, means for predicting an action of the user based on schedule information of the user acquired from the mobile terminal and visual information acquired from a camera of the display device, means for generating an appropriate suggestion for the user based on the prediction, means for providing the suggestion to the user via the display device, and feedback means for receiving a response of the user and generating a next suggestion again using the generation artificial intelligence platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, users are overwhelmed with numerous tasks and schedules every day, and are required to efficiently manage and execute them. However, conventional methods require users to check their schedules and plan appropriate actions themselves, which takes time and effort and carries the risk of mistakes and forgetting things. While some devices have reminder and notification functions, they are limited to one-way information transmission, making it difficult to provide personalized responses tailored to each user's individual situation. Therefore, the purpose of this invention is to provide efficient and personalized support for daily life by providing appropriate action suggestions in real time that are tailored to the user's lifestyle. [Means for solving the problem]

[0005] The present invention provides a display device worn by a user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Specifically, the system includes means for predicting a user's behavior based on the user's schedule information acquired from the mobile device and visual information acquired from the display device's camera, and means for generating appropriate suggestions for the user based on the prediction. The system also includes means for providing the suggestions to the user via the display device and receiving the user's responses via voice or gestures. The user's responses are processed by a feedback means that again uses the generative artificial intelligence platform to generate the next suggestion. In this way, a system is realized that provides personalized and appropriate suggestions tailored to the user's situation in real time.

[0006] "User" refers to an individual who uses the system of the present invention by using a display device and a mobile terminal.

[0007] "Display device" refers to a device worn by a user that acquires visual information and provides suggestions.

[0008] "Mobile terminal" refers to a device that can communicate with a display device and is connected to the generative artificial intelligence platform via a network.

[0009] "Network" refers to the communications infrastructure for exchanging data with external generative artificial intelligence platforms via mobile devices.

[0010] A "generative artificial intelligence platform" refers to an artificial intelligence system that analyzes a user's schedule information and visual information and generates appropriate suggestions.

[0011] "Schedule information" refers to information about a user's schedule and tasks.

[0012] "Visual information" refers to images and environmental information surrounding the user acquired through the camera of the display device.

[0013] "Means for predicting behavior" refers to algorithms and processes that predict a user's next behavior based on schedule information and visual information.

[0014] The "means for generating suggestions" refers to a process for generating optimal action suggestions or notifications for a user based on behavioral predictions.

[0015] "Means for providing suggestions" refers to the method and device for communicating the generated suggestions to the user through the display device.

[0016] "Means for receiving a response" refers to the technology and devices that capture and analyze voice and gesture input from the user.

[0017] "Feedback means" refers to the process of again utilizing the generative artificial intelligence platform to generate next action suggestions based on the user's response. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by the user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Below, we will explain the specific program processing of this system and its explanation in natural language. Specific examples will also be provided.

[0040] System Configuration

[0041] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0042] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0043] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule and visual information.

[0044] Program processing explanation

[0045] Step 1: Start up and connect

[0046] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0047] Step 2: Get user information

[0048] The device retrieves the user's schedule information from the mobile device's scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[0049] Step 3: Data synthesis and analysis

[0050] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[0051] Step 4: Generate and deliver proposals

[0052] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[0053] Step 5: Process the user's response

[0054] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[0055] Step 6: Feedback Loop

[0056] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[0057] Specific examples

[0058] For example, if a user has an important presentation coming up, the system will determine that they need to start preparing based on their schedule and current status. The system will then provide a voice notification on the display device saying, "30 minutes until the presentation. Please make a final check of your materials," efficiently supporting the user's busy daily life. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation in real time.

[0059] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for making users' daily lives more efficient and meeting their individual needs.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[0063] Step 2:

[0064] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[0065] Step 3:

[0066] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[0067] Step 4:

[0068] The server receives schedule information and visual information sent from the device and sends it to the generative AI platform, which analyzes this data and predicts user behavior.

[0069] Step 5:

[0070] Based on the analysis results, the generative AI generates optimal suggestions for the user. For example, if it determines that the user has a meeting at 9:00 a.m., it will generate a suggestion such as, "The meeting starts in 10 minutes. Please start getting ready."

[0071] Step 6:

[0072] The server transmits the generated proposal to the display device via the mobile terminal, and the terminal notifies the user of the proposal by voice through the speaker of the display device.

[0073] Step 7:

[0074] The user responds to the suggestions with voice or gesture, for example by saying "I get it" or nodding their head to indicate agreement.

[0075] Step 8:

[0076] The device captures the user's response with a microphone and camera and sends the data to the server, which analyzes the user's response and then requests the generative AI platform to take the next action.

[0077] Step 9:

[0078] The generative AI generates next action suggestions based on the user's response. For example, if it determines that the user is about to start preparing for a meeting, it will generate the next suggestion, such as "Get up immediately and wash your face."

[0079] Step 10:

[0080] The server then transmits the generated next proposal to the display device via the mobile terminal, which then announces the content of the proposal to the user by voice.

[0081] This completes the process of providing personalized, relevant suggestions in real time, tailored to the user's situation. By repeating this cycle, a system that efficiently supports the user's daily life is realized.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] Conventional AI-based proposal systems have had difficulty efficiently integrating and analyzing users' schedule information and real-time visual information to provide prompt and appropriate proposals. Furthermore, they lacked a feedback function that analyzes users' real-time responses and reflects them in the next proposal. This resulted in issues such as inaccurate prediction of user behavior and inadequate timing of proposals.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes a means for acquiring and analyzing the user's schedule information and visual information, a means for providing the generated proposals, a means for receiving and analyzing the user's responses, and a feedback means for generating the next proposal based on the analysis results, thereby enabling the server to accurately predict the user's behavior and continue to provide appropriate proposals in real time.

[0087] A "display device" is a device worn by a user to visually display information. Examples include head-mounted displays and smart glasses.

[0088] A "mobile terminal" is a mobile communication device that can be carried by a user, such as a smartphone or tablet.

[0089] The "generative artificial intelligence platform" is a cloud-based artificial intelligence system that generates behavioral predictions and suggestions based on a user's schedule information and visual information.

[0090] "Schedule information" refers to information about a user's schedule and tasks, including, for example, events and reminders registered in a calendar application.

[0091] "Visual information" refers to video data of the user's surroundings captured by the camera of the display device.

[0092] "Behavior prediction" is the process of predicting future behavior based on a user's current situation and schedule information.

[0093] "Suggestions" refer to instructions or advice provided to users by the generating AI.

[0094] "Feedback" is a series of processes that analyzes user responses and generates new suggestions.

[0095] A "response" is a user's reaction or reply to a suggestion, such as a voice command or gesture.

[0096] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by a user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Specific embodiments of this system are described below.

[0097] System Configuration

[0098] 1. Display device

[0099] It is a device such as a head-mounted display or smart glasses worn by the user, and is equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0100] 2. Mobile devices

[0101] It is a mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0102] 3. Generative AI Platform

[0103] It is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information and visual information.

[0104] System Operation

[0105] Startup and Connection

[0106] When a user puts on a display device (e.g., AR glasses) and turns on a mobile device (e.g., a smartphone), the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0107] Retrieving User Information

[0108] The device retrieves the user's schedule information from the scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[0109] Data integration and analysis

[0110] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[0111] Proposal generation and distribution

[0112] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[0113] User response processing

[0114] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[0115] Feedback Loop

[0116] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[0117] Specific examples

[0118] For example, if a user has an important presentation coming up at 2 p.m., the system will provide voice notifications in the morning with important information, such as "Please make a final check of the presentation materials." Furthermore, if the camera detects that the user has not checked the materials, it will send an alert saying, "You don't have time to check the materials. Please start checking them quickly," providing thorough support for the user's actions.

[0119] Prompt Sentence Examples

[0120] "It's currently 8:30 AM and a user is in bed with an important meeting at 9 AM. Let's use this information to have a generative AI model suggest an action."

[0121] The above is an embodiment of the present invention. This system can make users' daily lives more efficient and provide appropriate suggestions in real time that meet individual needs.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1: Start up and connect

[0124] 1.1. The system starts up when a user wears a display device (e.g., AR glasses) and turns on a mobile device (e.g., smartphone). The inputs are the power status of the AR glasses and the smartphone. The output is the system startup.

[0125] 1.2. The terminal automatically pairs with the display device via Bluetooth and checks the connection status. The input is the Bluetooth pairing request from the display device and the mobile terminal, and the output is a connection establishment notification.

[0126] 1.3. The server connects to the network via the mobile device and begins accessing the generative AI platform. The input is the mobile device's network connection request, and the output is the establishment of a connection to the generative AI platform. Specifically, the server accesses the generative AI platform on the cloud via the network and begins monitoring the status of the entire system.

[0127] Step 2: Get user information

[0128] 2.1. The terminal obtains the user's schedule information from the scheduler application. The input is a database query of the scheduler application, and the output is the user's schedule information.

[0129] The camera of the display device captures the visual information of the user's surroundings. The input is the real-time image acquisition request of the camera, and the output is the visual information data.

[0130] 2.3. The terminal transmits the acquired visual information data to the server. The input is visual information data, and the output is data transmission to the server.

[0131] 2.4. The server sends this schedule information and visual information to the generative AI platform and begins analysis. The input is the schedule information and visual information, and the output is the analysis results by the generative AI. Specifically, the server integrates the user's schedule information with the visual information from the camera and sends it to the generative AI.

[0132] Step 3: Data synthesis and analysis

[0133] 3.1. The server integrates schedule information and visual information to grasp the user's current situation. The input is schedule information and visual information, and the output is the integrated user situation data.

[0134] 3.2. The server issues an instruction to the generative AI model to start analysis. The input is the integrated user situation data, and the output is an instruction to start analysis.

[0135] 3.3. The Generative AI receives information that the user has a meeting at 9:00 AM and is currently still in bed. Based on this information, it predicts that the user needs to get up and start getting ready. The input is the integrated situation data, and the output is the behavior prediction result. Specifically, the Generative AI analyzes the schedule and visual information to predict the user's next action.

[0136] Step 4: Generate and distribute proposals

[0137] 4.1. Generative AI generates proposals based on behavioral prediction. The input is the behavioral prediction result, and the output is the proposal data.

[0138] 4.2. The server sends the generated proposal to the display device via the mobile device. The input is the proposal data, and the output is the proposal notification data.

[0139] 4.3. The device converts the proposal into voice and notifies the user through the AR glasses' speaker. The input is the proposal notification data, and the output is a voice notification. Specifically, the device uses voice conversion software to convert the proposal into voice and notify the user, "The meeting will start in 10 minutes. Please start getting ready."

[0140] Step 5: Process the user's response

[0141] 5.1. The user responds by saying "OK" or by nodding their head. The input is the user's response to the proposal, and the output is the response data.

[0142] 5.2. The device captures the response and sends it to the server. The input is the user response data, and the output is sending the response data to the server. Specifically, the device uses a microphone and camera to capture the user's voice and actions.

[0143] 5.3. The server analyzes the user's response data and requests the generation AI to take the next action. The input is the user's response data, and the output is a request to generate the next action.

[0144] Step 6: Feedback Loop

[0145] 6.1. The generation AI generates the next action proposal based on the user's response. The input is a request to generate the next action, and the output is the next proposal data.

[0146] 6.2. The server sends this new proposal to the mobile device. The input is the next proposal data, and the output is the proposal notification data.

[0147] 6.3. The device converts the suggestion into voice and communicates it to the user through the AR glasses. The input is the suggestion notification data, and the output is a voice notification. Specifically, the device again uses the voice conversion software to convert the suggestion into voice and notify the user, "Get up immediately and go to the bathroom to wash your face."

[0148] (Application example 1)

[0149] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0150] There is a growing need for systems that improve the efficiency of users' daily lives and work and provide appropriate support tailored to their individual needs. However, conventional systems have limited ability to predict user behavior and make personalized suggestions in real time. Furthermore, it has been difficult to communicate with users interactively through natural interfaces such as voice and gaze. In particular, there are very few systems that can make suggestions that appropriately reflect a user's preferences and purchasing history when it comes to product recommendations and purchasing procedures on online shopping sites.

[0151] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0152] In this invention, the server includes a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from a camera of the display device, means for generating appropriate suggestions for the user based on the prediction, means for providing the suggestions to the user through the display device, feedback means for receiving a user's response and again using the generative artificial intelligence platform to generate a next suggestion, means for recommending products based on the user's purchase history and visual information, means for displaying the recommended product information on the display device, and means for receiving a user's voice or gaze response and generating a next action. This makes it possible to accurately predict user behavior and provide personalized product recommendations and suggestions in real time.

[0153] "User" refers to an individual or corporation that uses the system.

[0154] A "display device" is a device worn by a user that can provide information visually or audibly, and includes smart glasses and head-mounted displays.

[0155] "Mobile terminal" refers to a mobile device such as a smartphone or tablet that has a network connection function and can communicate with a display device.

[0156] A "generative artificial intelligence platform" refers to an AI system that resides in the cloud and is connected via a network to analyze and make predictions on user data.

[0157] "Schedule information" refers to data related to a user's plans and tasks, and is obtained from a mobile device.

[0158] "Visual information" refers to image data about the user's surroundings and objects captured by the display device's camera.

[0159] The term "behavior prediction means" refers to a method and device for predicting a user's next behavior based on schedule information and visual information.

[0160] "Proposal generator" refers to a method and apparatus for generating appropriate suggestions for a user based on behavioral predictions.

[0161] "Feedback means" refers to methods and devices for receiving user responses and generating suggestions again.

[0162] "Purchase history" refers to data on products purchased by a user in the past.

[0163] "Visual information" refers to image data about the objects and environment the user is currently viewing.

[0164] "Product recommendation means" refers to a method and device for recommending suitable products to a user based on purchase history and visual information.

[0165] "Voice response means" refers to a method and apparatus that receives and acts upon a user's voice input.

[0166] "Gaze responsive means" refers to methods and devices that detect and use a user's gaze as a response.

[0167] "Action generation means" refers to a method and apparatus for directing the next action based on the user's response.

[0168] This invention is a system that recommends appropriate products in real time based on a user's purchasing history and visual information, using a display device worn by the user, a mobile terminal capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile terminal.

[0169] System Configuration

[0170] 1. Display device: A device such as smart glasses or a head-mounted display worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0171] 2. Mobile device: A mobile device such as a smartphone or tablet with a shopping application installed.

[0172] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and recommends products based on users' purchasing history and visual information.

[0173] Program processing explanation

[0174] In the present invention, the server, terminal, and user operate the system using the following hardware and software.

[0175] 1. The display device (smart glasses) is equipped with a camera, microphone, speaker, and Bluetooth communication function. The camera is used to capture the user's visual information, the microphone is used to receive audio input, and the speaker is used to provide audio feedback.

[0176] 2. A shopping app with network connectivity is installed on the mobile device (smartphone), and the user's purchase history and schedule information can be obtained through this app. The mobile device communicates with the display device via Bluetooth, collecting the necessary data and sending it to the server.

[0177] 3. The generative AI platform is operated using a cloud-based AI system (generative AI model). This platform analyzes purchase history and visual information sent from mobile devices, predicts user behavior, and recommends appropriate products. The recommendation results are sent to the display device via the mobile device.

[0178] The server uses a generative AI model to predict user behavior and recommend appropriate products to the user based on purchase history and visual information. For example, when a user wears smart glasses and looks at a store shelf, the smart glasses display shows, "We recommend a new smartphone." In this case, an example of a prompt to input into the generative AI model is, "Please generate the next product to recommend based on the product the user is viewing and their past purchase history."

[0179] This allows the server to provide personalized product recommendations and suggestions in real time, allowing users to efficiently find the products they want and complete the purchase process, making online shopping more convenient and providing a satisfying experience for users.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] The user puts on the smart glasses and launches a shopping app on their smartphone. The smartphone automatically connects to the smart glasses via Bluetooth and begins accessing the generative artificial intelligence platform via the network. The server monitors the overall system status via the smartphone and begins acquiring the necessary data.

[0183] Input: Wearing smart glasses, turning on smartphone

[0184] Output: Establishing a Bluetooth connection between smart glasses and a smartphone

[0185] Step 2:

[0186] The server retrieves the user's purchase history and current schedule information from the smartphone's shopping app. The camera in the smart glasses captures the visual information the user is looking at and sends the data in real time to the server. The server then sends this data to the artificial intelligence platform for analysis.

[0187] Input: Purchase history, schedule information, visual information

[0188] Output: Dataset for analysis

[0189] Step 3:

[0190] The server sends a prompt to the generative AI model based on the acquired purchase history and visual information to predict the user's behavior. An example of this prompt is, "Based on the products the user is viewing and their past purchase history, please generate the next product recommendation." The generative AI model analyzes the input data and predicts the user's behavior.

[0191] Input: purchase history, visual information, prompt text

[0192] Output: Behavior prediction results

[0193] Step 4:

[0194] The generative AI model recommends appropriate products based on behavioral predictions. The server receives the recommendation results and sends them to the smart glasses, which then display the message "We recommend a new smartphone."

[0195] Input: Behavior prediction result

[0196] Output: Recommended product information

[0197] Step 5:

[0198] The user responds with a voice command such as "Learn more" or "Buy" or by using their gaze. The smart glasses' microphone and eye tracking capture this response and send it to the server, which analyzes it and asks the generative AI model to generate the next action.

[0199] Input: Voice command or gaze information

[0200] Output: Next action instructions

[0201] Step 6:

[0202] The generative AI model generates the next action (e.g., start a checkout or recommend other products) based on the user's response. The server sends this information to the smart glasses, which then initiates the next step.

[0203] Input: Next action instructions

[0204] Output: Result of the next action

[0205] This process allows users to receive personalized product recommendations in real time and easily complete the purchase process, evolving online shopping into a more convenient and engaging experience.

[0206] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0207] The present invention is a system that predicts a user's behavior and provides appropriate suggestions according to their emotional state using a display device worn by the user, a mobile device capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile device, and an emotion engine that recognizes the user's emotions. Below, we will show the specific program processing of this system and explain it in natural language. Specific examples will also be provided.

[0208] System Configuration

[0209] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0210] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0211] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[0212] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[0213] Program processing explanation

[0214] Step 1: Start up and connect

[0215] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0216] Step 2: Get user information

[0217] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[0218] Step 3: Visual Acquisition

[0219] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[0220] Step 4: Obtaining emotional information

[0221] Using the device's microphone and camera, the emotion engine extracts emotional information from the user's voice and facial expressions. The emotion engine then quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends this information to the server.

[0222] Step 5: Data synthesis and analysis

[0223] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[0224] Step 6: Generate and deliver proposals

[0225] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[0226] Step 7: Process the user's response

[0227] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[0228] Step 8: Feedback Loop

[0229] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[0230] Specific examples

[0231] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify the user, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation and emotions in real time.

[0232] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for streamlining the user's daily life and meeting individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping the user live a more comfortable life.

[0233] The processing flow will be explained below.

[0234] Step 1:

[0235] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[0236] Step 2:

[0237] The device retrieves the user's schedule information from the mobile device's scheduler application, for example, information about an important meeting scheduled for 9:00 AM.

[0238] Step 3:

[0239] The terminal activates the camera of the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[0240] Step 4:

[0241] The device uses an emotion engine to analyze the user's facial expressions and voice via the display device's camera and microphone to recognize their emotional state, for example, determining whether they are feeling stressed.

[0242] Step 5:

[0243] The server sends the schedule information, visual information, and emotional information sent from the device to the AI ​​platform to predict the user's behavior. For example, if the user is feeling stressed, it will determine that they need time to relax.

[0244] Step 6:

[0245] Based on the prediction results, the generative AI generates optimal suggestions for the user, such as "There are still 30 minutes until the meeting, so please take a break."

[0246] Step 7:

[0247] The server sends the generated suggestion to the display device via the mobile device, which then announces through the display device's speaker, "You have 30 minutes until the meeting. Please take a short break and refresh yourself."

[0248] Step 8:

[0249] The user responds by saying "OK" or nodding their head, and the device's microphone or camera captures this response and sends the information to the server.

[0250] Step 9:

[0251] The server analyzes the user's response information and further requests the generation artificial intelligence platform to take the next action, for example, determining that appropriate music should be provided while the user is refreshing.

[0252] Step 10:

[0253] The generation AI generates a suggestion for the next action. For example, it may generate a suggestion such as "Play music for relaxation." The server then sends this to the display device via the mobile device. The device then conveys the suggestion to the user via voice.

[0254] This completes the process of providing personalized suggestions in real time based on the user's situation and emotional state, allowing the cycle to be repeated to help users live their daily lives efficiently and comfortably.

[0255] Example 2

[0256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0257] In recent years, there has been a demand for systems that can predict user behavior and provide appropriate suggestions. However, few existing systems take the user's emotional state into account, making it difficult to provide more highly personalized suggestions. Furthermore, they lack sufficient feedback functionality to analyze user responses in real time and reflect them in future suggestions. There is a need to solve these problems and provide systems that are more beneficial and efficient for users.

[0258] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0259] In this invention, the server includes an emotion engine that recognizes the user's emotions, means for predicting the user's behavior based on the user's schedule information acquired from the mobile terminal, visual information acquired from the camera of the display device, and emotion information acquired from the emotion engine, and means for generating appropriate suggestions for the user based on the prediction, thereby making it possible to predict the user's behavior and generate suggestions that take the user's emotional state into consideration.

[0260] A "display device" is a device worn by the user that is equipped with a camera, microphone, speaker, and Bluetooth communication capabilities.

[0261] A "mobile terminal" is a mobile device with network connectivity and an application that manages the user's schedule information.

[0262] The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[0263] The "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[0264] The "means for predicting user behavior" is a means having a function for predicting the user's next behavior or state based on the user's schedule information, visual information, and emotional information.

[0265] The "means for generating appropriate suggestions for the user" refers to a means having a function for automatically generating optimal suggestions for the user based on the predicted behavior and state of the user.

[0266] The "means for providing the user with the proposal" refers to a means having a function of notifying the user of the generated proposal and presenting its contents.

[0267] A "feedback means" is a means that has the function of receiving a user's response and generating further suggestions based on that information.

[0268] The present invention is a system that predicts a user's behavior and provides appropriate suggestions based on their emotional state using a display device worn by the user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[0269] System Configuration

[0270] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0271] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0272] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[0273] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[0274] Specific configuration and operation of the present invention

[0275] When the user wears the display device and turns on the mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0276] The device retrieves the user's schedule information from the mobile device's scheduler application, including the day's events and tasks. For example, it retrieves specific information such as "I have one meeting scheduled for 9 a.m. today and one presentation scheduled for 3 p.m."

[0277] Next, the device activates the display device's camera and captures visual information around the user in real time. The captured video information is sent to the server. Furthermore, the emotion engine extracts emotional information from the user's voice and facial expressions using the device's microphone and camera. The emotion engine quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends that information to the server.

[0278] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[0279] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[0280] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[0281] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[0282] Specific examples

[0283] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify them, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the system can provide personalized and appropriate suggestions in real time that are tailored to the user's situation and emotions.

[0284] The above is a specific embodiment of the present invention. This invention functions as a powerful tool for streamlining users' daily lives and meeting their individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping users live more comfortably.

[0285] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0286] Step 1: Start up and connect

[0287] Terminal behavior:

[0288] The user wears the display device and turns on the power of the mobile terminal.

[0289] Input: User-initiated activation of display devices and mobile devices

[0290] Data processing: The terminal connects to the display device via Bluetooth and checks various sensor information (camera, microphone, etc.).

[0291] Output: Confirm the connection between the display device and the mobile device and check that it works properly.

[0292] Specific operation: The mobile device automatically connects to the display device via Bluetooth, and then checks the operation of the display device's camera, microphone, and speaker.

[0293] Server behavior:

[0294] Input: Device is connected to the network

[0295] Data processing: The server initiates access to the generative AI platform and monitors the overall system status.

[0296] Output: System status information and start of data acquisition

[0297] Specific operation: The server monitors the status of data sent from the display device and emotion engine via the mobile device.

[0298] Step 2: Get user information

[0299] Terminal behavior:

[0300] Input: Scheduler application on mobile device

[0301] Data processing: The device obtains the user's schedule information and temporarily stores it.

[0302] Output: User's schedule information

[0303] Specific operation: The device retrieves information from the scheduler application, such as "You have one meeting scheduled for 9:00 AM today and one presentation scheduled for 3:00 PM today."

[0304] Server behavior:

[0305] Input: User schedule information sent from the device

[0306] Data processing: The server receives the schedule information and stores it for the next processing step.

[0307] Output: Schedule information storage

[0308] What happens: The server saves the schedule information to a database.

[0309] Step 3: Visual acquisition

[0310] Terminal behavior:

[0311] Input: Display camera

[0312] Data processing: The device activates the camera to capture visual information around the user in real time and temporarily store it.

[0313] Output: Visual information

[0314] Specific operation: The display device's camera captures objects and scenery within the user's field of view and sends the video information to the device.

[0315] Server behavior:

[0316] Input: Visual information sent from the device

[0317] Data processing: The server receives the visual information and stores it for the next processing step.

[0318] Output: Visual information storage

[0319] What happens: The server stores the visual information in a database.

[0320] Step 4: Acquiring emotional information

[0321] Terminal behavior:

[0322] Input: Display device microphone and camera

[0323] Data processing: The device records the user's voice and facial expressions in real time and temporarily stores them.

[0324] Output: Audio and facial expression data

[0325] Specific operation: The display device's microphone captures the user's voice and the camera captures the user's facial expressions.

[0326] Emotion Engine in action:

[0327] Input: Voice and facial expression data sent from the device

[0328] Data processing: The emotion engine quantifies the user's stress level and emotional state and generates information about them.

[0329] Output: Emotional information

[0330] How it works: The emotion engine analyzes the user's real-time emotional state and quantifies stress levels, joy, anger, sadness, and happiness.

[0331] Server behavior:

[0332] Input: Emotion information sent from the emotion engine

[0333] Data processing: The server receives the emotion information and stores it for the next processing step.

[0334] Output: Emotional information storage

[0335] Specific operation: The server stores the emotion information in a database.

[0336] Step 5: Data synthesis and analysis

[0337] Server behavior:

[0338] Input: Schedule information, visual information, emotional information

[0339] Data processing: The server integrates this information and sends it to the Generative AI Platform, which then uses it to predict user behavior.

[0340] Output: Behavior prediction results

[0341] Specific operation: The server sends schedule information, visual information, and emotional information to the generative artificial intelligence platform, and the AI ​​predicts the user's next action.

[0342] Step 6: Generate and deliver proposals

[0343] Generative AI behavior:

[0344] Input: Behavior prediction results and emotion information

[0345] Data processing: The generative artificial intelligence platform generates optimal suggestions for users based on behavioral predictions and emotional information.

[0346] Output: Proposal

[0347] What it does: The AI ​​generates suggestions like, "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[0348] Server behavior:

[0349] Input: Suggestions from the generative AI

[0350] Data processing: The server sends the proposal to the mobile device.

[0351] Output: Send proposal

[0352] Specific operation: The server sends the proposal content to the terminal, and the terminal transfers it to the display device.

[0353] Terminal behavior:

[0354] Input: Proposal sent from the server

[0355] Data processing: The device begins generating the suggestions as voice output.

[0356] Output: Audio notification

[0357] What it does: The device speaker will announce to the user, "You have 30 minutes until your meeting. Please take a short break and refresh yourself."

[0358] Step 7: Process the user's response

[0359] User Action:

[0360] Input: Proposal notification from device

[0361] Data processing: The user responds by saying "Got it" or nodding their head.

[0362] Output: User's voice or gesture response

[0363] Specific Action: The user responds verbally with "Got it" or nods their head in response.

[0364] Terminal behavior:

[0365] Input: The user's voice or gesture response

[0366] Data processing: The device's microphone and camera capture the user's responses and send them to the server.

[0367] Output: Sending user response

[0368] What it does: The device captures the user's response (voice or gesture) and sends it to the server.

[0369] Server behavior:

[0370] Input: User Response

[0371] Data processing: The server analyzes the user's response and requests the generative AI platform to take the next action.

[0372] Output: Next action request

[0373] Specific operation: The server sends the user's response data to the generation artificial intelligence platform and requests a suggestion for the next action.

[0374] Step 8: Feedback Loop

[0375] Generative AI behavior:

[0376] Input: Analysis results based on user responses

[0377] Data processing: A generative AI platform generates next action suggestions based on user responses.

[0378] Output: Suggested next action

[0379] Specific behavior: The AI ​​generates the next suggestion, such as "Wash your face and start getting ready."

[0380] Server behavior:

[0381] Input: Next suggestion from the generative AI

[0382] Data processing: The server sends the next proposal to the mobile device.

[0383] Output: Send next proposal

[0384] Specific operation: The server sends the next proposal to the terminal, which then transfers it to the display device.

[0385] Terminal behavior:

[0386] Input: Next suggestion sent by the server

[0387] Data processing: The device begins generating the next suggestion as a voice output.

[0388] Output: Audio notification

[0389] What it does: The device speaker will announce, "Wash your face and get ready."

[0390] The above is the specific flow of the program processing of this system. This system is a highly functional tool that takes into account the user's situation and emotions and provides optimal suggestions.

[0391] (Application example 2)

[0392] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0393] Conventional security systems have difficulty predicting users' behavior and providing safety suggestions based on their emotional state, which has limited their effectiveness, especially in the areas of stress management and personal security. Furthermore, they are unable to properly reflect the user's real-time emotional state, resulting in suboptimal security suggestions.

[0394] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a mobile terminal capable of communicating with the display device worn by the user, a generative artificial intelligence platform connected to the network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from the display device's camera, means for generating appropriate suggestions for the user based on the prediction, means for providing suggestions to the user through the display device, an emotion engine for recognizing the user's emotional state from voice and facial expressions, means for generating security-related suggestions and action instructions for the user based on the emotion information acquired from the emotion engine, and feedback means for receiving the user's response and again using the generative artificial intelligence platform to generate the next suggestion. This makes it possible to make security suggestions based on the user's real-time emotional state and behavioral predictions.

[0395] 1. A "display device" is a device that can be worn by a user and has a camera, speaker, microphone, and Bluetooth communication capabilities.

[0396] 2. "Mobile terminal" means a mobile device, such as a smartphone or tablet, that can communicate with a display device and has network connectivity.

[0397] 3. The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[0398] 4. "Schedule information" refers to information about the user's daily schedule, tasks, and other time allocation.

[0399] 5. "Visual information" refers to images and image data of the user's surroundings acquired through the camera of the display device.

[0400] 6. "Means for predicting behavior" is a system for predicting a user's next behavior based on schedule information and visual information.

[0401] 7. "Means for generating suggestions" refers to a system that creates optimal action suggestions for users based on behavioral prediction and emotional information.

[0402] 8. An "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates that information.

[0403] 9. A "feedback mechanism" is a system that receives user responses and regenerates subsequent suggestions based on them.

[0404] System Configuration

[0405] This invention is a system that uses a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[0406] Hardware and software used

[0407] 1. Display devices: Smart glasses (e.g., Google® Glass®) and head-mounted displays (e.g., Oculus Quest), which are equipped with cameras, microphones, speakers, and Bluetooth communication capabilities.

[0408] 2. Mobile device: A smartphone (e.g., iPhone (registered trademark), Android (registered trademark) device) with network connectivity.

[0409] 3. Generative AI platform: An artificial intelligence system on the cloud (e.g., Google Cloud AI Platform, AWS (registered trademark) SageMaker).

[0410] 4. Emotion engine: Facial expression recognition and speech analysis systems (e.g., Amazon Rekognition, IBM Watson®).

[0411] Program processing flow

[0412] 1. Startup and connection: When a user puts on smart glasses or a head-mounted display and launches the smartphone app, the mobile device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. It monitors the overall system status and obtains the necessary data.

[0413] 2. Acquisition of visual and emotional information:

[0414] The camera on the display device captures visual information in real time and transmits it to a server via the mobile device.

[0415] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, and also transmits this to the server.

[0416] 3. Data synthesis and analysis:

[0417] The server sends schedule information, visual information, and emotional information to the artificial intelligence platform to predict user behavior.

[0418] For example, if a user has a meeting at 9 a.m. but is currently feeling very tired, the AI ​​will predict that the user needs to relax.

[0419] 4. Proposal generation and delivery:

[0420] Based on behavioral predictions and emotional information, generative AI generates optimal suggestions for users, such as "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[0421] The suggestions are transmitted to the display device via the mobile device and announced to the user via an audio speaker.

[0422] 5. User response processing and feedback loop:

[0423] The user's voice and gesture responses are captured by the emotion engine and sent to the server.

[0424] The server analyzes the user's response and again uses the generative artificial intelligence platform to generate the next suggestion.

[0425] If the user is in the midst of a refreshing time, the system will generate and notify the next suggestion, such as "Wash your face and start getting ready."

[0426] Specific examples

[0427] For example, if the emotion engine detects that a user working in an office is feeling stressed, the application will notify them, "Your stress level is high. Take a break and play some music to help you relax," and then play some music.

[0428] Prompt Sentence Examples

[0429] Analyze voice and facial expression data to detect if the user has an important presentation coming up and is experiencing high stress levels, then play relaxing music as an appropriate action suggestion.

[0430] Audio data: "I'm worried about whether my presentation will go well."

[0431] Facial expression data: Images containing facial tension, lip biting, etc.

[0432] This allows the system to provide appropriate security suggestions and personal support based on the user's real-time emotional state and behavioral predictions.

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] The user puts on the display device and launches the application on the mobile device. The mobile device connects to the display device via Bluetooth. The mobile device then connects to the server via the network and begins accessing the generative artificial intelligence platform. At this point, the server begins monitoring the overall system status and acquiring data.

[0436] Input: Attaching a display device, launching an app on a mobile device

[0437] Output: Device connection status, system readiness status

[0438] Specific operation: The mobile terminal establishes a Bluetooth connection with the display device and starts communication with the server over the network.

[0439] Step 2:

[0440] The device retrieves the user's daily schedule information from the mobile device's schedule app, including the events and tasks for that day.

[0441] Input: Schedule data on your mobile device

[0442] Output: Retrieved schedule information

[0443] Specific operation: Extract time and task information from the mobile device's schedule app and send it to the server.

[0444] Step 3:

[0445] The camera on the display device is activated to capture visual information around the user in real time, and the captured video information is sent to a server via the mobile device.

[0446] Input: Visual data from a camera

[0447] Output: Visual information sent to the server

[0448] Specific operation: The camera on the display device captures surrounding images and transmits them to the server in real time.

[0449] Step 4:

[0450] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, which is also sent to the server.

[0451] Input: Voice data, facial expression data

[0452] Output: Emotion information extracted from the emotion engine

[0453] Specific operation: The emotion engine analyzes voice and facial expressions, quantifies stress levels and emotional states, and sends the results to the server.

[0454] Step 5:

[0455] The server sends schedule information, visual information, and emotional information to a generative artificial intelligence platform to predict user behavior.

[0456] Input: Schedule information, visual information, emotional information

[0457] Output: Behavioral prediction data generated by a generative AI platform

[0458] Specific operation: The generative artificial intelligence platform predicts user behavior based on the data received.

[0459] Step 6:

[0460] The generative AI generates optimal suggestions for users based on behavioral predictions and emotional information. For example, it might generate a suggestion such as, "You have 30 minutes until your meeting. Take a short break and refresh yourself." The suggestion is sent to the display device via the mobile device and notified to the user via the audio speaker.

[0461] Input: Behavioral prediction data, emotional information

[0462] Output: Generated action suggestions

[0463] What happens: The generated suggestions are announced to the user via audio and visual notification.

[0464] Step 7:

[0465] The user's response (voice or gestures) is captured by the display device's microphone or camera and sent to the server.

[0466] Input: Voice response, gesture response

[0467] Output: Response data sent to the server

[0468] Specific operation: The user's voice and gestures are detected and sent to the server.

[0469] Step 8:

[0470] The server analyzes the user's response and uses the AI ​​platform to generate the next suggestion and send it to the display device. For example, if the user is in the middle of a refreshing time, the server will notify the user to "wash their face and get ready."

[0471] Input: User response data

[0472] Output: Suggested next action

[0473] Specific operation: The generative artificial intelligence platform analyzes the user's response data, generates next action suggestions, and notifies them via voice.

[0474] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0475] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0476] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0477] [Second embodiment]

[0478] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0479] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0480] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0481] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0482] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0483] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0484] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0485] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0486] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0487] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0488] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0489] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0490] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by the user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Below, we will explain the specific program processing of this system and its explanation in natural language. Specific examples will also be provided.

[0491] System Configuration

[0492] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0493] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0494] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule and visual information.

[0495] Program processing explanation

[0496] Step 1: Start up and connect

[0497] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0498] Step 2: Get user information

[0499] The device retrieves the user's schedule information from the mobile device's scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[0500] Step 3: Data synthesis and analysis

[0501] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[0502] Step 4: Generate and deliver proposals

[0503] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[0504] Step 5: Process the user's response

[0505] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[0506] Step 6: Feedback Loop

[0507] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[0508] Specific examples

[0509] For example, if a user has an important presentation coming up, the system will determine that they need to start preparing based on their schedule and current status. The system will then provide a voice notification on the display device saying, "30 minutes until the presentation. Please make a final check of your materials," efficiently supporting the user's busy daily life. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation in real time.

[0510] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for making users' daily lives more efficient and meeting their individual needs.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[0514] Step 2:

[0515] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[0516] Step 3:

[0517] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[0518] Step 4:

[0519] The server receives schedule information and visual information sent from the device and sends it to the generative AI platform, which analyzes this data and predicts user behavior.

[0520] Step 5:

[0521] Based on the analysis results, the generative AI generates optimal suggestions for the user. For example, if it determines that the user has a meeting at 9:00 a.m., it will generate a suggestion such as, "The meeting starts in 10 minutes. Please start getting ready."

[0522] Step 6:

[0523] The server transmits the generated proposal to the display device via the mobile terminal, and the terminal notifies the user of the proposal by voice through the speaker of the display device.

[0524] Step 7:

[0525] The user responds to the suggestions with voice or gesture, for example by saying "I get it" or nodding their head to indicate agreement.

[0526] Step 8:

[0527] The device captures the user's response with a microphone and camera and sends the data to the server, which analyzes the user's response and then requests the generative AI platform to take the next action.

[0528] Step 9:

[0529] The generative AI generates next action suggestions based on the user's response. For example, if it determines that the user is about to start preparing for a meeting, it will generate the next suggestion, such as "Get up immediately and wash your face."

[0530] Step 10:

[0531] The server then transmits the generated next proposal to the display device via the mobile terminal, which then announces the content of the proposal to the user by voice.

[0532] This completes the process of providing personalized, relevant suggestions in real time, tailored to the user's situation. By repeating this cycle, a system that efficiently supports the user's daily life is realized.

[0533] Example 1

[0534] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0535] Conventional AI-based proposal systems have had difficulty efficiently integrating and analyzing users' schedule information and real-time visual information to provide prompt and appropriate proposals. Furthermore, they lacked a feedback function that analyzes users' real-time responses and reflects them in the next proposal. This resulted in issues such as inaccurate prediction of user behavior and inadequate timing of proposals.

[0536] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0537] In this invention, the server includes a means for acquiring and analyzing the user's schedule information and visual information, a means for providing the generated proposals, a means for receiving and analyzing the user's responses, and a feedback means for generating the next proposal based on the analysis results, thereby enabling the server to accurately predict the user's behavior and continue to provide appropriate proposals in real time.

[0538] A "display device" is a device worn by a user to visually display information. Examples include head-mounted displays and smart glasses.

[0539] A "mobile terminal" is a mobile communication device that can be carried by a user, such as a smartphone or tablet.

[0540] The "generative artificial intelligence platform" is a cloud-based artificial intelligence system that generates behavioral predictions and suggestions based on a user's schedule information and visual information.

[0541] "Schedule information" refers to information about a user's schedule and tasks, including, for example, events and reminders registered in a calendar application.

[0542] "Visual information" refers to video data of the user's surroundings captured by the camera of the display device.

[0543] "Behavior prediction" is the process of predicting future behavior based on a user's current situation and schedule information.

[0544] "Suggestions" refer to instructions or advice provided to users by the generating AI.

[0545] "Feedback" is a series of processes that analyzes user responses and generates new suggestions.

[0546] A "response" is a user's reaction or reply to a suggestion, such as a voice command or gesture.

[0547] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by a user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Specific embodiments of this system are described below.

[0548] System Configuration

[0549] 1. Display device

[0550] It is a device such as a head-mounted display or smart glasses worn by the user, and is equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0551] 2. Mobile devices

[0552] It is a mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0553] 3. Generative AI Platform

[0554] It is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information and visual information.

[0555] System Operation

[0556] Startup and Connection

[0557] When a user puts on a display device (e.g., AR glasses) and turns on a mobile device (e.g., a smartphone), the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0558] Retrieving User Information

[0559] The device retrieves the user's schedule information from the scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[0560] Data integration and analysis

[0561] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[0562] Proposal generation and distribution

[0563] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[0564] User response processing

[0565] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[0566] Feedback Loop

[0567] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[0568] Specific examples

[0569] For example, if a user has an important presentation coming up at 2 p.m., the system will provide voice notifications in the morning with important information, such as "Please make a final check of the presentation materials." Furthermore, if the camera detects that the user has not checked the materials, it will send an alert saying, "You don't have time to check the materials. Please start checking them quickly," providing thorough support for the user's actions.

[0570] Prompt Sentence Examples

[0571] "It's currently 8:30 AM and a user is in bed with an important meeting at 9 AM. Let's use this information to have a generative AI model suggest an action."

[0572] The above is an embodiment of the present invention. This system can make users' daily lives more efficient and provide appropriate suggestions in real time that meet individual needs.

[0573] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0574] Step 1: Start up and connect

[0575] 1.1. The system starts up when a user wears a display device (e.g., AR glasses) and turns on a mobile device (e.g., smartphone). The inputs are the power status of the AR glasses and the smartphone. The output is the system startup.

[0576] 1.2. The terminal automatically pairs with the display device via Bluetooth and checks the connection status. The input is the Bluetooth pairing request from the display device and the mobile terminal, and the output is a connection establishment notification.

[0577] 1.3. The server connects to the network via the mobile device and begins accessing the generative AI platform. The input is the mobile device's network connection request, and the output is the establishment of a connection to the generative AI platform. Specifically, the server accesses the generative AI platform on the cloud via the network and begins monitoring the status of the entire system.

[0578] Step 2: Get user information

[0579] 2.1. The terminal obtains the user's schedule information from the scheduler application. The input is a database query of the scheduler application, and the output is the user's schedule information.

[0580] The camera of the display device captures the visual information of the user's surroundings. The input is the real-time image acquisition request of the camera, and the output is the visual information data.

[0581] 2.3. The terminal transmits the acquired visual information data to the server. The input is visual information data, and the output is data transmission to the server.

[0582] 2.4. The server sends this schedule information and visual information to the generative AI platform and begins analysis. The input is the schedule information and visual information, and the output is the analysis results by the generative AI. Specifically, the server integrates the user's schedule information with the visual information from the camera and sends it to the generative AI.

[0583] Step 3: Data synthesis and analysis

[0584] 3.1. The server integrates schedule information and visual information to grasp the user's current situation. The input is schedule information and visual information, and the output is the integrated user situation data.

[0585] 3.2. The server issues an instruction to the generative AI model to start analysis. The input is the integrated user situation data, and the output is an instruction to start analysis.

[0586] 3.3. The Generative AI receives information that the user has a meeting at 9:00 AM and is currently still in bed. Based on this information, it predicts that the user needs to get up and start getting ready. The input is the integrated situation data, and the output is the behavior prediction result. Specifically, the Generative AI analyzes the schedule and visual information to predict the user's next action.

[0587] Step 4: Generate and distribute proposals

[0588] 4.1. Generative AI generates proposals based on behavioral prediction. The input is the behavioral prediction result, and the output is the proposal data.

[0589] 4.2. The server sends the generated proposal to the display device via the mobile device. The input is the proposal data, and the output is the proposal notification data.

[0590] 4.3. The device converts the proposal into voice and notifies the user through the AR glasses' speaker. The input is the proposal notification data, and the output is a voice notification. Specifically, the device uses voice conversion software to convert the proposal into voice and notify the user, "The meeting will start in 10 minutes. Please start getting ready."

[0591] Step 5: Process the user's response

[0592] 5.1. The user responds by saying "OK" or by nodding their head. The input is the user's response to the proposal, and the output is the response data.

[0593] 5.2. The device captures the response and sends it to the server. The input is the user response data, and the output is sending the response data to the server. Specifically, the device uses a microphone and camera to capture the user's voice and actions.

[0594] 5.3. The server analyzes the user's response data and requests the generation AI to take the next action. The input is the user's response data, and the output is a request to generate the next action.

[0595] Step 6: Feedback Loop

[0596] 6.1. The generation AI generates the next action proposal based on the user's response. The input is a request to generate the next action, and the output is the next proposal data.

[0597] 6.2. The server sends this new proposal to the mobile device. The input is the next proposal data, and the output is the proposal notification data.

[0598] 6.3. The device converts the suggestion into voice and communicates it to the user through the AR glasses. The input is the suggestion notification data, and the output is a voice notification. Specifically, the device again uses the voice conversion software to convert the suggestion into voice and notify the user, "Get up immediately and go to the bathroom to wash your face."

[0599] (Application example 1)

[0600] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0601] There is a growing need for systems that improve the efficiency of users' daily lives and work and provide appropriate support tailored to their individual needs. However, conventional systems have limited ability to predict user behavior and make personalized suggestions in real time. Furthermore, it has been difficult to communicate with users interactively through natural interfaces such as voice and gaze. In particular, there are very few systems that can make suggestions that appropriately reflect a user's preferences and purchasing history when it comes to product recommendations and purchasing procedures on online shopping sites.

[0602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0603] In this invention, the server includes a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from a camera of the display device, means for generating appropriate suggestions for the user based on the prediction, means for providing the suggestions to the user through the display device, feedback means for receiving a user's response and again using the generative artificial intelligence platform to generate a next suggestion, means for recommending products based on the user's purchase history and visual information, means for displaying the recommended product information on the display device, and means for receiving a user's voice or gaze response and generating a next action. This makes it possible to accurately predict user behavior and provide personalized product recommendations and suggestions in real time.

[0604] "User" refers to an individual or corporation that uses the system.

[0605] A "display device" is a device worn by a user that can provide information visually or audibly, and includes smart glasses and head-mounted displays.

[0606] "Mobile terminal" refers to a mobile device such as a smartphone or tablet that has a network connection function and can communicate with a display device.

[0607] A "generative artificial intelligence platform" refers to an AI system that resides in the cloud and is connected via a network to analyze and make predictions on user data.

[0608] "Schedule information" refers to data related to a user's plans and tasks, and is obtained from a mobile device.

[0609] "Visual information" refers to image data about the user's surroundings and objects captured by the display device's camera.

[0610] The term "behavior prediction means" refers to a method and device for predicting a user's next behavior based on schedule information and visual information.

[0611] "Proposal generator" refers to a method and apparatus for generating appropriate suggestions for a user based on behavioral predictions.

[0612] "Feedback means" refers to methods and devices for receiving user responses and generating suggestions again.

[0613] "Purchase history" refers to data on products purchased by a user in the past.

[0614] "Visual information" refers to image data about the objects and environment the user is currently viewing.

[0615] "Product recommendation means" refers to a method and device for recommending suitable products to a user based on purchase history and visual information.

[0616] "Voice response means" refers to a method and apparatus that receives and acts upon a user's voice input.

[0617] "Gaze responsive means" refers to methods and devices that detect and use a user's gaze as a response.

[0618] "Action generation means" refers to a method and apparatus for directing the next action based on the user's response.

[0619] This invention is a system that recommends appropriate products in real time based on a user's purchasing history and visual information, using a display device worn by the user, a mobile terminal capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile terminal.

[0620] System Configuration

[0621] 1. Display device: A device such as smart glasses or a head-mounted display worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0622] 2. Mobile device: A mobile device such as a smartphone or tablet with a shopping application installed.

[0623] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and recommends products based on users' purchasing history and visual information.

[0624] Program processing explanation

[0625] In the present invention, the server, terminal, and user operate the system using the following hardware and software.

[0626] 1. The display device (smart glasses) is equipped with a camera, microphone, speaker, and Bluetooth communication function. The camera is used to capture the user's visual information, the microphone is used to receive audio input, and the speaker is used to provide audio feedback.

[0627] 2. A shopping app with network connectivity is installed on the mobile device (smartphone), and the user's purchase history and schedule information can be obtained through this app. The mobile device communicates with the display device via Bluetooth, collecting the necessary data and sending it to the server.

[0628] 3. The generative AI platform is operated using a cloud-based AI system (generative AI model). This platform analyzes purchase history and visual information sent from mobile devices, predicts user behavior, and recommends appropriate products. The recommendation results are sent to the display device via the mobile device.

[0629] The server uses a generative AI model to predict user behavior and recommend appropriate products to the user based on purchase history and visual information. For example, when a user wears smart glasses and looks at a store shelf, the smart glasses display shows, "We recommend a new smartphone." In this case, an example of a prompt to input into the generative AI model is, "Please generate the next product to recommend based on the product the user is viewing and their past purchase history."

[0630] This allows the server to provide personalized product recommendations and suggestions in real time, allowing users to efficiently find the products they want and complete the purchase process, making online shopping more convenient and providing a satisfying experience for users.

[0631] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0632] Step 1:

[0633] The user puts on the smart glasses and launches a shopping app on their smartphone. The smartphone automatically connects to the smart glasses via Bluetooth and begins accessing the generative artificial intelligence platform via the network. The server monitors the overall system status via the smartphone and begins acquiring the necessary data.

[0634] Input: Wearing smart glasses, turning on smartphone

[0635] Output: Establishing a Bluetooth connection between smart glasses and a smartphone

[0636] Step 2:

[0637] The server retrieves the user's purchase history and current schedule information from the smartphone's shopping app. The camera in the smart glasses captures the visual information the user is looking at and sends the data in real time to the server. The server then sends this data to the artificial intelligence platform for analysis.

[0638] Input: Purchase history, schedule information, visual information

[0639] Output: Dataset for analysis

[0640] Step 3:

[0641] The server sends a prompt to the generative AI model based on the acquired purchase history and visual information to predict the user's behavior. An example of this prompt is, "Based on the products the user is viewing and their past purchase history, please generate the next product recommendation." The generative AI model analyzes the input data and predicts the user's behavior.

[0642] Input: purchase history, visual information, prompt text

[0643] Output: Behavior prediction results

[0644] Step 4:

[0645] The generative AI model recommends appropriate products based on behavioral predictions. The server receives the recommendation results and sends them to the smart glasses, which then display the message "We recommend a new smartphone."

[0646] Input: Behavior prediction result

[0647] Output: Recommended product information

[0648] Step 5:

[0649] The user responds with a voice command such as "Learn more" or "Buy" or by using their gaze. The smart glasses' microphone and eye tracking capture this response and send it to the server, which analyzes it and asks the generative AI model to generate the next action.

[0650] Input: Voice command or gaze information

[0651] Output: Next action instructions

[0652] Step 6:

[0653] The generative AI model generates the next action (e.g., start a checkout or recommend other products) based on the user's response. The server sends this information to the smart glasses, which then initiates the next step.

[0654] Input: Next action instructions

[0655] Output: Result of the next action

[0656] This process allows users to receive personalized product recommendations in real time and easily complete the purchase process, evolving online shopping into a more convenient and engaging experience.

[0657] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0658] The present invention is a system that predicts a user's behavior and provides appropriate suggestions according to their emotional state using a display device worn by the user, a mobile device capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile device, and an emotion engine that recognizes the user's emotions. Below, we will show the specific program processing of this system and explain it in natural language. Specific examples will also be provided.

[0659] System Configuration

[0660] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0661] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0662] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[0663] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[0664] Program processing explanation

[0665] Step 1: Start up and connect

[0666] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0667] Step 2: Get user information

[0668] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[0669] Step 3: Visual Acquisition

[0670] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[0671] Step 4: Obtaining emotional information

[0672] Using the device's microphone and camera, the emotion engine extracts emotional information from the user's voice and facial expressions. The emotion engine then quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends this information to the server.

[0673] Step 5: Data synthesis and analysis

[0674] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[0675] Step 6: Generate and deliver proposals

[0676] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[0677] Step 7: Process the user's response

[0678] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[0679] Step 8: Feedback Loop

[0680] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[0681] Specific examples

[0682] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify the user, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation and emotions in real time.

[0683] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for streamlining the user's daily life and meeting individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping the user live a more comfortable life.

[0684] The processing flow will be explained below.

[0685] Step 1:

[0686] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[0687] Step 2:

[0688] The device retrieves the user's schedule information from the mobile device's scheduler application, for example, information about an important meeting scheduled for 9:00 AM.

[0689] Step 3:

[0690] The terminal activates the camera of the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[0691] Step 4:

[0692] The device uses an emotion engine to analyze the user's facial expressions and voice via the display device's camera and microphone to recognize their emotional state, for example, determining whether they are feeling stressed.

[0693] Step 5:

[0694] The server sends the schedule information, visual information, and emotional information sent from the device to the AI ​​platform to predict the user's behavior. For example, if the user is feeling stressed, it will determine that they need time to relax.

[0695] Step 6:

[0696] Based on the prediction results, the generative AI generates optimal suggestions for the user, such as "There are still 30 minutes until the meeting, so please take a break."

[0697] Step 7:

[0698] The server sends the generated suggestion to the display device via the mobile device, which then announces through the display device's speaker, "You have 30 minutes until the meeting. Please take a short break and refresh yourself."

[0699] Step 8:

[0700] The user responds by saying "OK" or nodding their head, and the device's microphone or camera captures this response and sends the information to the server.

[0701] Step 9:

[0702] The server analyzes the user's response information and further requests the generation artificial intelligence platform to take the next action, for example, determining that appropriate music should be provided while the user is refreshing.

[0703] Step 10:

[0704] The generation AI generates a suggestion for the next action. For example, it may generate a suggestion such as "Play music for relaxation." The server then sends this to the display device via the mobile device. The device then conveys the suggestion to the user via voice.

[0705] This completes the process of providing personalized suggestions in real time based on the user's situation and emotional state, allowing the cycle to be repeated to help users live their daily lives efficiently and comfortably.

[0706] Example 2

[0707] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0708] In recent years, there has been a demand for systems that can predict user behavior and provide appropriate suggestions. However, few existing systems take the user's emotional state into account, making it difficult to provide more highly personalized suggestions. Furthermore, they lack sufficient feedback functionality to analyze user responses in real time and reflect them in future suggestions. There is a need to solve these problems and provide systems that are more beneficial and efficient for users.

[0709] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0710] In this invention, the server includes an emotion engine that recognizes the user's emotions, means for predicting the user's behavior based on the user's schedule information acquired from the mobile terminal, visual information acquired from the camera of the display device, and emotion information acquired from the emotion engine, and means for generating appropriate suggestions for the user based on the prediction, thereby making it possible to predict the user's behavior and generate suggestions that take the user's emotional state into consideration.

[0711] A "display device" is a device worn by the user that is equipped with a camera, microphone, speaker, and Bluetooth communication capabilities.

[0712] A "mobile terminal" is a mobile device with network connectivity and an application that manages the user's schedule information.

[0713] The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[0714] The "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[0715] The "means for predicting user behavior" is a means having a function for predicting the user's next behavior or state based on the user's schedule information, visual information, and emotional information.

[0716] The "means for generating appropriate suggestions for the user" refers to a means having a function for automatically generating optimal suggestions for the user based on the predicted behavior and state of the user.

[0717] The "means for providing the user with the proposal" refers to a means having a function of notifying the user of the generated proposal and presenting its contents.

[0718] A "feedback means" is a means that has the function of receiving a user's response and generating further suggestions based on that information.

[0719] The present invention is a system that predicts a user's behavior and provides appropriate suggestions based on their emotional state using a display device worn by the user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[0720] System Configuration

[0721] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0722] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0723] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[0724] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[0725] Specific configuration and operation of the present invention

[0726] When the user wears the display device and turns on the mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0727] The device retrieves the user's schedule information from the mobile device's scheduler application, including the day's events and tasks. For example, it retrieves specific information such as "I have one meeting scheduled for 9 a.m. today and one presentation scheduled for 3 p.m."

[0728] Next, the device activates the display device's camera and captures visual information around the user in real time. The captured video information is sent to the server. Furthermore, the emotion engine extracts emotional information from the user's voice and facial expressions using the device's microphone and camera. The emotion engine quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends that information to the server.

[0729] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[0730] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[0731] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[0732] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[0733] Specific examples

[0734] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify them, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the system can provide personalized and appropriate suggestions in real time that are tailored to the user's situation and emotions.

[0735] The above is a specific embodiment of the present invention. This invention functions as a powerful tool for streamlining users' daily lives and meeting their individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping users live more comfortably.

[0736] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0737] Step 1: Start up and connect

[0738] Terminal behavior:

[0739] The user wears the display device and turns on the power of the mobile terminal.

[0740] Input: User-initiated activation of display devices and mobile devices

[0741] Data processing: The terminal connects to the display device via Bluetooth and checks various sensor information (camera, microphone, etc.).

[0742] Output: Confirm the connection between the display device and the mobile device and check that it works properly.

[0743] Specific operation: The mobile device automatically connects to the display device via Bluetooth, and then checks the operation of the display device's camera, microphone, and speaker.

[0744] Server behavior:

[0745] Input: Device is connected to the network

[0746] Data processing: The server initiates access to the generative AI platform and monitors the overall system status.

[0747] Output: System status information and start of data acquisition

[0748] Specific operation: The server monitors the status of data sent from the display device and emotion engine via the mobile device.

[0749] Step 2: Get user information

[0750] Terminal behavior:

[0751] Input: Scheduler application on mobile device

[0752] Data processing: The device obtains the user's schedule information and temporarily stores it.

[0753] Output: User's schedule information

[0754] Specific operation: The device retrieves information from the scheduler application, such as "You have one meeting scheduled for 9:00 AM today and one presentation scheduled for 3:00 PM today."

[0755] Server behavior:

[0756] Input: User schedule information sent from the device

[0757] Data processing: The server receives the schedule information and stores it for the next processing step.

[0758] Output: Schedule information storage

[0759] What happens: The server saves the schedule information to a database.

[0760] Step 3: Visual acquisition

[0761] Terminal behavior:

[0762] Input: Display camera

[0763] Data processing: The device activates the camera to capture visual information around the user in real time and temporarily store it.

[0764] Output: Visual information

[0765] Specific operation: The display device's camera captures objects and scenery within the user's field of view and sends the video information to the device.

[0766] Server behavior:

[0767] Input: Visual information sent from the device

[0768] Data processing: The server receives the visual information and stores it for the next processing step.

[0769] Output: Visual information storage

[0770] What happens: The server stores the visual information in a database.

[0771] Step 4: Acquiring emotional information

[0772] Terminal behavior:

[0773] Input: Display device microphone and camera

[0774] Data processing: The device records the user's voice and facial expressions in real time and temporarily stores them.

[0775] Output: Audio and facial expression data

[0776] Specific operation: The display device's microphone captures the user's voice and the camera captures the user's facial expressions.

[0777] Emotion Engine in action:

[0778] Input: Voice and facial expression data sent from the device

[0779] Data processing: The emotion engine quantifies the user's stress level and emotional state and generates information about them.

[0780] Output: Emotional information

[0781] How it works: The emotion engine analyzes the user's real-time emotional state and quantifies stress levels, joy, anger, sadness, and happiness.

[0782] Server behavior:

[0783] Input: Emotion information sent from the emotion engine

[0784] Data processing: The server receives the emotion information and stores it for the next processing step.

[0785] Output: Emotional information storage

[0786] Specific operation: The server stores the emotion information in a database.

[0787] Step 5: Data synthesis and analysis

[0788] Server behavior:

[0789] Input: Schedule information, visual information, emotional information

[0790] Data processing: The server integrates this information and sends it to the Generative AI Platform, which then uses it to predict user behavior.

[0791] Output: Behavior prediction results

[0792] Specific operation: The server sends schedule information, visual information, and emotional information to the generative artificial intelligence platform, and the AI ​​predicts the user's next action.

[0793] Step 6: Generate and deliver proposals

[0794] Generative AI behavior:

[0795] Input: Behavior prediction results and emotion information

[0796] Data processing: The generative artificial intelligence platform generates optimal suggestions for users based on behavioral predictions and emotional information.

[0797] Output: Proposal

[0798] What it does: The AI ​​generates suggestions like, "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[0799] Server behavior:

[0800] Input: Suggestions from the generative AI

[0801] Data processing: The server sends the proposal to the mobile device.

[0802] Output: Send proposal

[0803] Specific operation: The server sends the proposal content to the terminal, and the terminal transfers it to the display device.

[0804] Terminal behavior:

[0805] Input: Proposal sent from the server

[0806] Data processing: The device begins generating the suggestions as voice output.

[0807] Output: Audio notification

[0808] What it does: The device speaker will announce to the user, "You have 30 minutes until your meeting. Please take a short break and refresh yourself."

[0809] Step 7: Process the user's response

[0810] User Action:

[0811] Input: Proposal notification from device

[0812] Data processing: The user responds by saying "Got it" or nodding their head.

[0813] Output: User's voice or gesture response

[0814] Specific Action: The user responds verbally with "Got it" or nods their head in response.

[0815] Terminal behavior:

[0816] Input: The user's voice or gesture response

[0817] Data processing: The device's microphone and camera capture the user's responses and send them to the server.

[0818] Output: Sending user response

[0819] What it does: The device captures the user's response (voice or gesture) and sends it to the server.

[0820] Server behavior:

[0821] Input: User Response

[0822] Data processing: The server analyzes the user's response and requests the generative AI platform to take the next action.

[0823] Output: Next action request

[0824] Specific operation: The server sends the user's response data to the generation artificial intelligence platform and requests a suggestion for the next action.

[0825] Step 8: Feedback Loop

[0826] Generative AI behavior:

[0827] Input: Analysis results based on user responses

[0828] Data processing: A generative AI platform generates next action suggestions based on user responses.

[0829] Output: Suggested next action

[0830] Specific behavior: The AI ​​generates the next suggestion, such as "Wash your face and start getting ready."

[0831] Server behavior:

[0832] Input: Next suggestion from the generative AI

[0833] Data processing: The server sends the next proposal to the mobile device.

[0834] Output: Send next proposal

[0835] Specific operation: The server sends the next proposal to the terminal, which then transfers it to the display device.

[0836] Terminal behavior:

[0837] Input: Next suggestion sent by the server

[0838] Data processing: The device begins generating the next suggestion as a voice output.

[0839] Output: Audio notification

[0840] What it does: The device speaker will announce, "Wash your face and get ready."

[0841] The above is the specific flow of the program processing of this system. This system is a highly functional tool that takes into account the user's situation and emotions and provides optimal suggestions.

[0842] (Application example 2)

[0843] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0844] Conventional security systems have difficulty predicting users' behavior and providing safety suggestions based on their emotional state, which has limited their effectiveness, especially in the areas of stress management and personal security. Furthermore, they are unable to properly reflect the user's real-time emotional state, resulting in suboptimal security suggestions.

[0845] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a mobile terminal capable of communicating with the display device worn by the user, a generative artificial intelligence platform connected to the network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from the display device's camera, means for generating appropriate suggestions for the user based on the prediction, means for providing suggestions to the user through the display device, an emotion engine for recognizing the user's emotional state from voice and facial expressions, means for generating security-related suggestions and action instructions for the user based on the emotion information acquired from the emotion engine, and feedback means for receiving the user's response and again using the generative artificial intelligence platform to generate the next suggestion. This makes it possible to make security suggestions based on the user's real-time emotional state and behavioral predictions.

[0846] 1. A "display device" is a device that can be worn by a user and has a camera, speaker, microphone, and Bluetooth communication capabilities.

[0847] 2. "Mobile terminal" means a mobile device, such as a smartphone or tablet, that can communicate with a display device and has network connectivity.

[0848] 3. The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[0849] 4. "Schedule information" refers to information about the user's daily schedule, tasks, and other time allocation.

[0850] 5. "Visual information" refers to images and image data of the user's surroundings acquired through the camera of the display device.

[0851] 6. "Means for predicting behavior" is a system for predicting a user's next behavior based on schedule information and visual information.

[0852] 7. "Means for generating suggestions" refers to a system that creates optimal action suggestions for users based on behavioral prediction and emotional information.

[0853] 8. An "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates that information.

[0854] 9. A "feedback mechanism" is a system that receives user responses and regenerates subsequent suggestions based on them.

[0855] System Configuration

[0856] This invention is a system that uses a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[0857] Hardware and software used

[0858] 1. Display devices: Smart glasses (e.g., Google Glass) and head-mounted displays (e.g., Oculus Quest), which are equipped with cameras, microphones, speakers, and Bluetooth communication capabilities.

[0859] 2. Mobile device: A smartphone (e.g., iPhone, Android device) with network connectivity.

[0860] 3. Generative AI platform: An artificial intelligence system on the cloud (e.g., Google Cloud AI Platform, AWS SageMaker).

[0861] 4. Emotion engines: facial expression recognition and speech analysis systems (e.g., Amazon Rekognition, IBM Watson).

[0862] Program processing flow

[0863] 1. Startup and connection: When a user puts on smart glasses or a head-mounted display and launches the smartphone app, the mobile device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. It monitors the overall system status and obtains the necessary data.

[0864] 2. Acquisition of visual and emotional information:

[0865] The camera on the display device captures visual information in real time and transmits it to a server via the mobile device.

[0866] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, and also transmits this to the server.

[0867] 3. Data synthesis and analysis:

[0868] The server sends schedule information, visual information, and emotional information to the artificial intelligence platform to predict user behavior.

[0869] For example, if a user has a meeting at 9 a.m. but is currently feeling very tired, the AI ​​will predict that the user needs to relax.

[0870] 4. Proposal generation and delivery:

[0871] Based on behavioral predictions and emotional information, generative AI generates optimal suggestions for users, such as "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[0872] The suggestions are transmitted to the display device via the mobile device and announced to the user via an audio speaker.

[0873] 5. User response processing and feedback loop:

[0874] The user's voice and gesture responses are captured by the emotion engine and sent to the server.

[0875] The server analyzes the user's response and again uses the generative artificial intelligence platform to generate the next suggestion.

[0876] If the user is in the midst of a refreshing time, the system will generate and notify the next suggestion, such as "Wash your face and start getting ready."

[0877] Specific examples

[0878] For example, if the emotion engine detects that a user working in an office is feeling stressed, the application will notify them, "Your stress level is high. Take a break and play some music to help you relax," and then play some music.

[0879] Prompt Sentence Examples

[0880] Analyze voice and facial expression data to detect if the user has an important presentation coming up and is experiencing high stress levels, then play relaxing music as an appropriate action suggestion.

[0881] Audio data: "I'm worried about whether my presentation will go well."

[0882] Facial expression data: Images containing facial tension, lip biting, etc.

[0883] This allows the system to provide appropriate security suggestions and personal support based on the user's real-time emotional state and behavioral predictions.

[0884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0885] Step 1:

[0886] The user puts on the display device and launches the application on the mobile device. The mobile device connects to the display device via Bluetooth. The mobile device then connects to the server via the network and begins accessing the generative artificial intelligence platform. At this point, the server begins monitoring the overall system status and acquiring data.

[0887] Input: Attaching a display device, launching an app on a mobile device

[0888] Output: Device connection status, system readiness status

[0889] Specific operation: The mobile terminal establishes a Bluetooth connection with the display device and starts communication with the server over the network.

[0890] Step 2:

[0891] The device retrieves the user's daily schedule information from the mobile device's schedule app, including the events and tasks for that day.

[0892] Input: Schedule data on your mobile device

[0893] Output: Retrieved schedule information

[0894] Specific operation: Extract time and task information from the mobile device's schedule app and send it to the server.

[0895] Step 3:

[0896] The camera on the display device is activated to capture visual information around the user in real time, and the captured video information is sent to a server via the mobile device.

[0897] Input: Visual data from a camera

[0898] Output: Visual information sent to the server

[0899] Specific operation: The camera on the display device captures surrounding images and transmits them to the server in real time.

[0900] Step 4:

[0901] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, which is also sent to the server.

[0902] Input: Voice data, facial expression data

[0903] Output: Emotion information extracted from the emotion engine

[0904] Specific operation: The emotion engine analyzes voice and facial expressions, quantifies stress levels and emotional states, and sends the results to the server.

[0905] Step 5:

[0906] The server sends schedule information, visual information, and emotional information to a generative artificial intelligence platform to predict user behavior.

[0907] Input: Schedule information, visual information, emotional information

[0908] Output: Behavioral prediction data generated by a generative AI platform

[0909] Specific operation: The generative artificial intelligence platform predicts user behavior based on the data received.

[0910] Step 6:

[0911] The generative AI generates optimal suggestions for users based on behavioral predictions and emotional information. For example, it might generate a suggestion such as, "You have 30 minutes until your meeting. Take a short break and refresh yourself." The suggestion is sent to the display device via the mobile device and notified to the user via the audio speaker.

[0912] Input: Behavioral prediction data, emotional information

[0913] Output: Generated action suggestions

[0914] What happens: The generated suggestions are announced to the user via audio and visual notification.

[0915] Step 7:

[0916] The user's response (voice or gestures) is captured by the display device's microphone or camera and sent to the server.

[0917] Input: Voice response, gesture response

[0918] Output: Response data sent to the server

[0919] Specific operation: The user's voice and gestures are detected and sent to the server.

[0920] Step 8:

[0921] The server analyzes the user's response and uses the AI ​​platform to generate the next suggestion and send it to the display device. For example, if the user is in the middle of a refreshing time, the server will notify the user to "wash their face and get ready."

[0922] Input: User response data

[0923] Output: Suggested next action

[0924] Specific operation: The generative artificial intelligence platform analyzes the user's response data, generates next action suggestions, and notifies them via voice.

[0925] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0926] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0927] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0928] [Third embodiment]

[0929] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0930] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0931] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0932] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0933] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0934] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0935] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0936] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0937] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0938] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0939] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0940] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0941] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by the user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Below, we will explain the specific program processing of this system and its explanation in natural language. Specific examples will also be provided.

[0942] System Configuration

[0943] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[0944] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[0945] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule and visual information.

[0946] Program processing explanation

[0947] Step 1: Start up and connect

[0948] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[0949] Step 2: Get user information

[0950] The device retrieves the user's schedule information from the mobile device's scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[0951] Step 3: Data synthesis and analysis

[0952] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[0953] Step 4: Generate and deliver proposals

[0954] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[0955] Step 5: Process the user's response

[0956] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[0957] Step 6: Feedback Loop

[0958] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[0959] Specific examples

[0960] For example, if a user has an important presentation coming up, the system will determine that they need to start preparing based on their schedule and current status. The system will then provide a voice notification on the display device saying, "30 minutes until the presentation. Please make a final check of your materials," efficiently supporting the user's busy daily life. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation in real time.

[0961] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for making users' daily lives more efficient and meeting their individual needs.

[0962] The processing flow will be explained below.

[0963] Step 1:

[0964] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[0965] Step 2:

[0966] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[0967] Step 3:

[0968] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[0969] Step 4:

[0970] The server receives schedule information and visual information sent from the device and sends it to the generative AI platform, which analyzes this data and predicts user behavior.

[0971] Step 5:

[0972] Based on the analysis results, the generative AI generates optimal suggestions for the user. For example, if it determines that the user has a meeting at 9:00 a.m., it will generate a suggestion such as, "The meeting starts in 10 minutes. Please start getting ready."

[0973] Step 6:

[0974] The server transmits the generated proposal to the display device via the mobile terminal, and the terminal notifies the user of the proposal by voice through the speaker of the display device.

[0975] Step 7:

[0976] The user responds to the suggestions with voice or gesture, for example by saying "I get it" or nodding their head to indicate agreement.

[0977] Step 8:

[0978] The device captures the user's response with a microphone and camera and sends the data to the server, which analyzes the user's response and then requests the generative AI platform to take the next action.

[0979] Step 9:

[0980] The generative AI generates next action suggestions based on the user's response. For example, if it determines that the user is about to start preparing for a meeting, it will generate the next suggestion, such as "Get up immediately and wash your face."

[0981] Step 10:

[0982] The server then transmits the generated next proposal to the display device via the mobile terminal, which then announces the content of the proposal to the user by voice.

[0983] This completes the process of providing personalized, relevant suggestions in real time, tailored to the user's situation. By repeating this cycle, a system that efficiently supports the user's daily life is realized.

[0984] Example 1

[0985] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0986] Conventional AI-based proposal systems have had difficulty efficiently integrating and analyzing users' schedule information and real-time visual information to provide prompt and appropriate proposals. Furthermore, they lacked a feedback function that analyzes users' real-time responses and reflects them in the next proposal. This resulted in issues such as inaccurate prediction of user behavior and inadequate timing of proposals.

[0987] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0988] In this invention, the server includes a means for acquiring and analyzing the user's schedule information and visual information, a means for providing the generated proposals, a means for receiving and analyzing the user's responses, and a feedback means for generating the next proposal based on the analysis results, thereby enabling the server to accurately predict the user's behavior and continue to provide appropriate proposals in real time.

[0989] A "display device" is a device worn by a user to visually display information. Examples include head-mounted displays and smart glasses.

[0990] A "mobile terminal" is a mobile communication device that can be carried by a user, such as a smartphone or tablet.

[0991] The "generative artificial intelligence platform" is a cloud-based artificial intelligence system that generates behavioral predictions and suggestions based on a user's schedule information and visual information.

[0992] "Schedule information" refers to information about a user's schedule and tasks, including, for example, events and reminders registered in a calendar application.

[0993] "Visual information" refers to video data of the user's surroundings captured by the camera of the display device.

[0994] "Behavior prediction" is the process of predicting future behavior based on a user's current situation and schedule information.

[0995] "Suggestions" refer to instructions or advice provided to users by the generating AI.

[0996] "Feedback" is a series of processes that analyzes user responses and generates new suggestions.

[0997] A "response" is a user's reaction or reply to a suggestion, such as a voice command or gesture.

[0998] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by a user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Specific embodiments of this system are described below.

[0999] System Configuration

[1000] 1. Display device

[1001] It is a device such as a head-mounted display or smart glasses worn by the user, and is equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1002] 2. Mobile devices

[1003] It is a mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[1004] 3. Generative AI Platform

[1005] It is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information and visual information.

[1006] System Operation

[1007] Startup and Connection

[1008] When a user puts on a display device (e.g., AR glasses) and turns on a mobile device (e.g., a smartphone), the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[1009] Retrieving User Information

[1010] The device retrieves the user's schedule information from the scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[1011] Data integration and analysis

[1012] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[1013] Proposal generation and distribution

[1014] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[1015] User response processing

[1016] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[1017] Feedback Loop

[1018] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[1019] Specific examples

[1020] For example, if a user has an important presentation coming up at 2 p.m., the system will provide voice notifications in the morning with important information, such as "Please make a final check of the presentation materials." Furthermore, if the camera detects that the user has not checked the materials, it will send an alert saying, "You don't have time to check the materials. Please start checking them quickly," providing thorough support for the user's actions.

[1021] Prompt Sentence Examples

[1022] "It's currently 8:30 AM and a user is in bed with an important meeting at 9 AM. Let's use this information to have a generative AI model suggest an action."

[1023] The above is an embodiment of the present invention. This system can make users' daily lives more efficient and provide appropriate suggestions in real time that meet individual needs.

[1024] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1025] Step 1: Start up and connect

[1026] 1.1. The system starts up when a user wears a display device (e.g., AR glasses) and turns on a mobile device (e.g., smartphone). The inputs are the power status of the AR glasses and the smartphone. The output is the system startup.

[1027] 1.2. The terminal automatically pairs with the display device via Bluetooth and checks the connection status. The input is the Bluetooth pairing request from the display device and the mobile terminal, and the output is a connection establishment notification.

[1028] 1.3. The server connects to the network via the mobile device and begins accessing the generative AI platform. The input is the mobile device's network connection request, and the output is the establishment of a connection to the generative AI platform. Specifically, the server accesses the generative AI platform on the cloud via the network and begins monitoring the status of the entire system.

[1029] Step 2: Get user information

[1030] 2.1. The terminal obtains the user's schedule information from the scheduler application. The input is a database query of the scheduler application, and the output is the user's schedule information.

[1031] The camera of the display device captures the visual information of the user's surroundings. The input is the real-time image acquisition request of the camera, and the output is the visual information data.

[1032] 2.3. The terminal transmits the acquired visual information data to the server. The input is visual information data, and the output is data transmission to the server.

[1033] 2.4. The server sends this schedule information and visual information to the generative AI platform and begins analysis. The input is the schedule information and visual information, and the output is the analysis results by the generative AI. Specifically, the server integrates the user's schedule information with the visual information from the camera and sends it to the generative AI.

[1034] Step 3: Data synthesis and analysis

[1035] 3.1. The server integrates schedule information and visual information to grasp the user's current situation. The input is schedule information and visual information, and the output is the integrated user situation data.

[1036] 3.2. The server issues an instruction to the generative AI model to start analysis. The input is the integrated user situation data, and the output is an instruction to start analysis.

[1037] 3.3. The Generative AI receives information that the user has a meeting at 9:00 AM and is currently still in bed. Based on this information, it predicts that the user needs to get up and start getting ready. The input is the integrated situation data, and the output is the behavior prediction result. Specifically, the Generative AI analyzes the schedule and visual information to predict the user's next action.

[1038] Step 4: Generate and distribute proposals

[1039] 4.1. Generative AI generates proposals based on behavioral prediction. The input is the behavioral prediction result, and the output is the proposal data.

[1040] 4.2. The server sends the generated proposal to the display device via the mobile device. The input is the proposal data, and the output is the proposal notification data.

[1041] 4.3. The device converts the proposal into voice and notifies the user through the AR glasses' speaker. The input is the proposal notification data, and the output is a voice notification. Specifically, the device uses voice conversion software to convert the proposal into voice and notify the user, "The meeting will start in 10 minutes. Please start getting ready."

[1042] Step 5: Process the user's response

[1043] 5.1. The user responds by saying "OK" or by nodding their head. The input is the user's response to the proposal, and the output is the response data.

[1044] 5.2. The device captures the response and sends it to the server. The input is the user response data, and the output is sending the response data to the server. Specifically, the device uses a microphone and camera to capture the user's voice and actions.

[1045] 5.3. The server analyzes the user's response data and requests the generation AI to take the next action. The input is the user's response data, and the output is a request to generate the next action.

[1046] Step 6: Feedback Loop

[1047] 6.1. The generation AI generates the next action proposal based on the user's response. The input is a request to generate the next action, and the output is the next proposal data.

[1048] 6.2. The server sends this new proposal to the mobile device. The input is the next proposal data, and the output is the proposal notification data.

[1049] 6.3. The device converts the suggestion into voice and communicates it to the user through the AR glasses. The input is the suggestion notification data, and the output is a voice notification. Specifically, the device again uses the voice conversion software to convert the suggestion into voice and notify the user, "Get up immediately and go to the bathroom to wash your face."

[1050] (Application example 1)

[1051] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1052] There is a growing need for systems that improve the efficiency of users' daily lives and work and provide appropriate support tailored to their individual needs. However, conventional systems have limited ability to predict user behavior and make personalized suggestions in real time. Furthermore, it has been difficult to communicate with users interactively through natural interfaces such as voice and gaze. In particular, there are very few systems that can make suggestions that appropriately reflect a user's preferences and purchasing history when it comes to product recommendations and purchasing procedures on online shopping sites.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1054] In this invention, the server includes a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from a camera of the display device, means for generating appropriate suggestions for the user based on the prediction, means for providing the suggestions to the user through the display device, feedback means for receiving a user's response and again using the generative artificial intelligence platform to generate a next suggestion, means for recommending products based on the user's purchase history and visual information, means for displaying the recommended product information on the display device, and means for receiving a user's voice or gaze response and generating a next action. This makes it possible to accurately predict user behavior and provide personalized product recommendations and suggestions in real time.

[1055] "User" refers to an individual or corporation that uses the system.

[1056] A "display device" is a device worn by a user that can provide information visually or audibly, and includes smart glasses and head-mounted displays.

[1057] "Mobile terminal" refers to a mobile device such as a smartphone or tablet that has a network connection function and can communicate with a display device.

[1058] A "generative artificial intelligence platform" refers to an AI system that resides in the cloud and is connected via a network to analyze and make predictions on user data.

[1059] "Schedule information" refers to data related to a user's plans and tasks, and is obtained from a mobile device.

[1060] "Visual information" refers to image data about the user's surroundings and objects captured by the display device's camera.

[1061] The term "behavior prediction means" refers to a method and device for predicting a user's next behavior based on schedule information and visual information.

[1062] "Proposal generator" refers to a method and apparatus for generating appropriate suggestions for a user based on behavioral predictions.

[1063] "Feedback means" refers to methods and devices for receiving user responses and generating suggestions again.

[1064] "Purchase history" refers to data on products purchased by a user in the past.

[1065] "Visual information" refers to image data about the objects and environment the user is currently viewing.

[1066] "Product recommendation means" refers to a method and device for recommending suitable products to a user based on purchase history and visual information.

[1067] "Voice response means" refers to a method and apparatus that receives and acts upon a user's voice input.

[1068] "Gaze responsive means" refers to methods and devices that detect and use a user's gaze as a response.

[1069] "Action generation means" refers to a method and apparatus for directing the next action based on the user's response.

[1070] This invention is a system that recommends appropriate products in real time based on a user's purchasing history and visual information, using a display device worn by the user, a mobile terminal capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile terminal.

[1071] System Configuration

[1072] 1. Display device: A device such as smart glasses or a head-mounted display worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1073] 2. Mobile device: A mobile device such as a smartphone or tablet with a shopping application installed.

[1074] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and recommends products based on users' purchasing history and visual information.

[1075] Program processing explanation

[1076] In the present invention, the server, terminal, and user operate the system using the following hardware and software.

[1077] 1. The display device (smart glasses) is equipped with a camera, microphone, speaker, and Bluetooth communication function. The camera is used to capture the user's visual information, the microphone is used to receive audio input, and the speaker is used to provide audio feedback.

[1078] 2. A shopping app with network connectivity is installed on the mobile device (smartphone), and the user's purchase history and schedule information can be obtained through this app. The mobile device communicates with the display device via Bluetooth, collecting the necessary data and sending it to the server.

[1079] 3. The generative AI platform is operated using a cloud-based AI system (generative AI model). This platform analyzes purchase history and visual information sent from mobile devices, predicts user behavior, and recommends appropriate products. The recommendation results are sent to the display device via the mobile device.

[1080] The server uses a generative AI model to predict user behavior and recommend appropriate products to the user based on purchase history and visual information. For example, when a user wears smart glasses and looks at a store shelf, the smart glasses display shows, "We recommend a new smartphone." In this case, an example of a prompt to input into the generative AI model is, "Please generate the next product to recommend based on the product the user is viewing and their past purchase history."

[1081] This allows the server to provide personalized product recommendations and suggestions in real time, allowing users to efficiently find the products they want and complete the purchase process, making online shopping more convenient and providing a satisfying experience for users.

[1082] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1083] Step 1:

[1084] The user puts on the smart glasses and launches a shopping app on their smartphone. The smartphone automatically connects to the smart glasses via Bluetooth and begins accessing the generative artificial intelligence platform via the network. The server monitors the overall system status via the smartphone and begins acquiring the necessary data.

[1085] Input: Wearing smart glasses, turning on smartphone

[1086] Output: Establishing a Bluetooth connection between smart glasses and a smartphone

[1087] Step 2:

[1088] The server retrieves the user's purchase history and current schedule information from the smartphone's shopping app. The camera in the smart glasses captures the visual information the user is looking at and sends the data in real time to the server. The server then sends this data to the artificial intelligence platform for analysis.

[1089] Input: Purchase history, schedule information, visual information

[1090] Output: Dataset for analysis

[1091] Step 3:

[1092] The server sends a prompt to the generative AI model based on the acquired purchase history and visual information to predict the user's behavior. An example of this prompt is, "Based on the products the user is viewing and their past purchase history, please generate the next product recommendation." The generative AI model analyzes the input data and predicts the user's behavior.

[1093] Input: purchase history, visual information, prompt text

[1094] Output: Behavior prediction results

[1095] Step 4:

[1096] The generative AI model recommends appropriate products based on behavioral predictions. The server receives the recommendation results and sends them to the smart glasses, which then display the message "We recommend a new smartphone."

[1097] Input: Behavior prediction result

[1098] Output: Recommended product information

[1099] Step 5:

[1100] The user responds with a voice command such as "Learn more" or "Buy" or by using their gaze. The smart glasses' microphone and eye tracking capture this response and send it to the server, which analyzes it and asks the generative AI model to generate the next action.

[1101] Input: Voice command or gaze information

[1102] Output: Next action instructions

[1103] Step 6:

[1104] The generative AI model generates the next action (e.g., start a checkout or recommend other products) based on the user's response. The server sends this information to the smart glasses, which then initiates the next step.

[1105] Input: Next action instructions

[1106] Output: Result of the next action

[1107] This process allows users to receive personalized product recommendations in real time and easily complete the purchase process, evolving online shopping into a more convenient and engaging experience.

[1108] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1109] The present invention is a system that predicts a user's behavior and provides appropriate suggestions according to their emotional state using a display device worn by the user, a mobile device capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile device, and an emotion engine that recognizes the user's emotions. Below, we will show the specific program processing of this system and explain it in natural language. Specific examples will also be provided.

[1110] System Configuration

[1111] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1112] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[1113] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[1114] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[1115] Program processing explanation

[1116] Step 1: Start up and connect

[1117] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[1118] Step 2: Get user information

[1119] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[1120] Step 3: Visual Acquisition

[1121] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[1122] Step 4: Obtaining emotional information

[1123] Using the device's microphone and camera, the emotion engine extracts emotional information from the user's voice and facial expressions. The emotion engine then quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends this information to the server.

[1124] Step 5: Data synthesis and analysis

[1125] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[1126] Step 6: Generate and deliver proposals

[1127] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[1128] Step 7: Process the user's response

[1129] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[1130] Step 8: Feedback Loop

[1131] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[1132] Specific examples

[1133] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify the user, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation and emotions in real time.

[1134] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for streamlining the user's daily life and meeting individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping the user live a more comfortable life.

[1135] The processing flow will be explained below.

[1136] Step 1:

[1137] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[1138] Step 2:

[1139] The device retrieves the user's schedule information from the mobile device's scheduler application, for example, information about an important meeting scheduled for 9:00 AM.

[1140] Step 3:

[1141] The terminal activates the camera of the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[1142] Step 4:

[1143] The device uses an emotion engine to analyze the user's facial expressions and voice via the display device's camera and microphone to recognize their emotional state, for example, determining whether they are feeling stressed.

[1144] Step 5:

[1145] The server sends the schedule information, visual information, and emotional information sent from the device to the AI ​​platform to predict the user's behavior. For example, if the user is feeling stressed, it will determine that they need time to relax.

[1146] Step 6:

[1147] Based on the prediction results, the generative AI generates optimal suggestions for the user, such as "There are still 30 minutes until the meeting, so please take a break."

[1148] Step 7:

[1149] The server sends the generated suggestion to the display device via the mobile device, which then announces through the display device's speaker, "You have 30 minutes until the meeting. Please take a short break and refresh yourself."

[1150] Step 8:

[1151] The user responds by saying "OK" or nodding their head, and the device's microphone or camera captures this response and sends the information to the server.

[1152] Step 9:

[1153] The server analyzes the user's response information and further requests the generation artificial intelligence platform to take the next action, for example, determining that appropriate music should be provided while the user is refreshing.

[1154] Step 10:

[1155] The generation AI generates a suggestion for the next action. For example, it may generate a suggestion such as "Play music for relaxation." The server then sends this to the display device via the mobile device. The device then conveys the suggestion to the user via voice.

[1156] This completes the process of providing personalized suggestions in real time based on the user's situation and emotional state, allowing the cycle to be repeated to help users live their daily lives efficiently and comfortably.

[1157] Example 2

[1158] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1159] In recent years, there has been a demand for systems that can predict user behavior and provide appropriate suggestions. However, few existing systems take the user's emotional state into account, making it difficult to provide more highly personalized suggestions. Furthermore, they lack sufficient feedback functionality to analyze user responses in real time and reflect them in future suggestions. There is a need to solve these problems and provide systems that are more beneficial and efficient for users.

[1160] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1161] In this invention, the server includes an emotion engine that recognizes the user's emotions, means for predicting the user's behavior based on the user's schedule information acquired from the mobile terminal, visual information acquired from the camera of the display device, and emotion information acquired from the emotion engine, and means for generating appropriate suggestions for the user based on the prediction, thereby making it possible to predict the user's behavior and generate suggestions that take the user's emotional state into consideration.

[1162] A "display device" is a device worn by the user that is equipped with a camera, microphone, speaker, and Bluetooth communication capabilities.

[1163] A "mobile terminal" is a mobile device with network connectivity and an application that manages the user's schedule information.

[1164] The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[1165] The "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[1166] The "means for predicting user behavior" is a means having a function for predicting the user's next behavior or state based on the user's schedule information, visual information, and emotional information.

[1167] The "means for generating appropriate suggestions for the user" refers to a means having a function for automatically generating optimal suggestions for the user based on the predicted behavior and state of the user.

[1168] The "means for providing the user with the proposal" refers to a means having a function of notifying the user of the generated proposal and presenting its contents.

[1169] A "feedback means" is a means that has the function of receiving a user's response and generating further suggestions based on that information.

[1170] The present invention is a system that predicts a user's behavior and provides appropriate suggestions based on their emotional state using a display device worn by the user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[1171] System Configuration

[1172] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1173] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[1174] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[1175] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[1176] Specific configuration and operation of the present invention

[1177] When the user wears the display device and turns on the mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[1178] The device retrieves the user's schedule information from the mobile device's scheduler application, including the day's events and tasks. For example, it retrieves specific information such as "I have one meeting scheduled for 9 a.m. today and one presentation scheduled for 3 p.m."

[1179] Next, the device activates the display device's camera and captures visual information around the user in real time. The captured video information is sent to the server. Furthermore, the emotion engine extracts emotional information from the user's voice and facial expressions using the device's microphone and camera. The emotion engine quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends that information to the server.

[1180] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[1181] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[1182] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[1183] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[1184] Specific examples

[1185] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify them, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the system can provide personalized and appropriate suggestions in real time that are tailored to the user's situation and emotions.

[1186] The above is a specific embodiment of the present invention. This invention functions as a powerful tool for streamlining users' daily lives and meeting their individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping users live more comfortably.

[1187] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1188] Step 1: Start up and connect

[1189] Terminal behavior:

[1190] The user wears the display device and turns on the power of the mobile terminal.

[1191] Input: User-initiated activation of display devices and mobile devices

[1192] Data processing: The terminal connects to the display device via Bluetooth and checks various sensor information (camera, microphone, etc.).

[1193] Output: Confirm the connection between the display device and the mobile device and check that it works properly.

[1194] Specific operation: The mobile device automatically connects to the display device via Bluetooth, and then checks the operation of the display device's camera, microphone, and speaker.

[1195] Server behavior:

[1196] Input: Device is connected to the network

[1197] Data processing: The server initiates access to the generative AI platform and monitors the overall system status.

[1198] Output: System status information and start of data acquisition

[1199] Specific operation: The server monitors the status of data sent from the display device and emotion engine via the mobile device.

[1200] Step 2: Get user information

[1201] Terminal behavior:

[1202] Input: Scheduler application on mobile device

[1203] Data processing: The device obtains the user's schedule information and temporarily stores it.

[1204] Output: User's schedule information

[1205] Specific operation: The device retrieves information from the scheduler application, such as "You have one meeting scheduled for 9:00 AM today and one presentation scheduled for 3:00 PM today."

[1206] Server behavior:

[1207] Input: User schedule information sent from the device

[1208] Data processing: The server receives the schedule information and stores it for the next processing step.

[1209] Output: Schedule information storage

[1210] What happens: The server saves the schedule information to a database.

[1211] Step 3: Visual acquisition

[1212] Terminal behavior:

[1213] Input: Display camera

[1214] Data processing: The device activates the camera to capture visual information around the user in real time and temporarily store it.

[1215] Output: Visual information

[1216] Specific operation: The display device's camera captures objects and scenery within the user's field of view and sends the video information to the device.

[1217] Server behavior:

[1218] Input: Visual information sent from the device

[1219] Data processing: The server receives the visual information and stores it for the next processing step.

[1220] Output: Visual information storage

[1221] What happens: The server stores the visual information in a database.

[1222] Step 4: Acquiring emotional information

[1223] Terminal behavior:

[1224] Input: Display device microphone and camera

[1225] Data processing: The device records the user's voice and facial expressions in real time and temporarily stores them.

[1226] Output: Audio and facial expression data

[1227] Specific operation: The display device's microphone captures the user's voice and the camera captures the user's facial expressions.

[1228] Emotion Engine in action:

[1229] Input: Voice and facial expression data sent from the device

[1230] Data processing: The emotion engine quantifies the user's stress level and emotional state and generates information about them.

[1231] Output: Emotional information

[1232] How it works: The emotion engine analyzes the user's real-time emotional state and quantifies stress levels, joy, anger, sadness, and happiness.

[1233] Server behavior:

[1234] Input: Emotion information sent from the emotion engine

[1235] Data processing: The server receives the emotion information and stores it for the next processing step.

[1236] Output: Emotional information storage

[1237] Specific operation: The server stores the emotion information in a database.

[1238] Step 5: Data synthesis and analysis

[1239] Server behavior:

[1240] Input: Schedule information, visual information, emotional information

[1241] Data processing: The server integrates this information and sends it to the Generative AI Platform, which then uses it to predict user behavior.

[1242] Output: Behavior prediction results

[1243] Specific operation: The server sends schedule information, visual information, and emotional information to the generative artificial intelligence platform, and the AI ​​predicts the user's next action.

[1244] Step 6: Generate and deliver proposals

[1245] Generative AI behavior:

[1246] Input: Behavior prediction results and emotion information

[1247] Data processing: The generative artificial intelligence platform generates optimal suggestions for users based on behavioral predictions and emotional information.

[1248] Output: Proposal

[1249] What it does: The AI ​​generates suggestions like, "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[1250] Server behavior:

[1251] Input: Suggestions from the generative AI

[1252] Data processing: The server sends the proposal to the mobile device.

[1253] Output: Send proposal

[1254] Specific operation: The server sends the proposal content to the terminal, and the terminal transfers it to the display device.

[1255] Terminal behavior:

[1256] Input: Proposal sent from the server

[1257] Data processing: The device begins generating the suggestions as voice output.

[1258] Output: Audio notification

[1259] What it does: The device speaker will announce to the user, "You have 30 minutes until your meeting. Please take a short break and refresh yourself."

[1260] Step 7: Process the user's response

[1261] User Action:

[1262] Input: Proposal notification from device

[1263] Data processing: The user responds by saying "Got it" or nodding their head.

[1264] Output: User's voice or gesture response

[1265] Specific Action: The user responds verbally with "Got it" or nods their head in response.

[1266] Terminal behavior:

[1267] Input: The user's voice or gesture response

[1268] Data processing: The device's microphone and camera capture the user's responses and send them to the server.

[1269] Output: Sending user response

[1270] What it does: The device captures the user's response (voice or gesture) and sends it to the server.

[1271] Server behavior:

[1272] Input: User Response

[1273] Data processing: The server analyzes the user's response and requests the generative AI platform to take the next action.

[1274] Output: Next action request

[1275] Specific operation: The server sends the user's response data to the generation artificial intelligence platform and requests a suggestion for the next action.

[1276] Step 8: Feedback Loop

[1277] Generative AI behavior:

[1278] Input: Analysis results based on user responses

[1279] Data processing: A generative AI platform generates next action suggestions based on user responses.

[1280] Output: Suggested next action

[1281] Specific behavior: The AI ​​generates the next suggestion, such as "Wash your face and start getting ready."

[1282] Server behavior:

[1283] Input: Next suggestion from the generative AI

[1284] Data processing: The server sends the next proposal to the mobile device.

[1285] Output: Send next proposal

[1286] Specific operation: The server sends the next proposal to the terminal, which then transfers it to the display device.

[1287] Terminal behavior:

[1288] Input: Next suggestion sent by the server

[1289] Data processing: The device begins generating the next suggestion as a voice output.

[1290] Output: Audio notification

[1291] What it does: The device speaker will announce, "Wash your face and get ready."

[1292] The above is the specific flow of the program processing of this system. This system is a highly functional tool that takes into account the user's situation and emotions and provides optimal suggestions.

[1293] (Application example 2)

[1294] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1295] Conventional security systems have difficulty predicting users' behavior and providing safety suggestions based on their emotional state, which has limited their effectiveness, especially in the areas of stress management and personal security. Furthermore, they are unable to properly reflect the user's real-time emotional state, resulting in suboptimal security suggestions.

[1296] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a mobile terminal capable of communicating with the display device worn by the user, a generative artificial intelligence platform connected to the network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from the display device's camera, means for generating appropriate suggestions for the user based on the prediction, means for providing suggestions to the user through the display device, an emotion engine for recognizing the user's emotional state from voice and facial expressions, means for generating security-related suggestions and action instructions for the user based on the emotion information acquired from the emotion engine, and feedback means for receiving the user's response and again using the generative artificial intelligence platform to generate the next suggestion. This makes it possible to make security suggestions based on the user's real-time emotional state and behavioral predictions.

[1297] 1. A "display device" is a device that can be worn by a user and has a camera, speaker, microphone, and Bluetooth communication capabilities.

[1298] 2. "Mobile terminal" means a mobile device, such as a smartphone or tablet, that can communicate with a display device and has network connectivity.

[1299] 3. The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[1300] 4. "Schedule information" refers to information about the user's daily schedule, tasks, and other time allocation.

[1301] 5. "Visual information" refers to images and image data of the user's surroundings acquired through the camera of the display device.

[1302] 6. "Means for predicting behavior" is a system for predicting a user's next behavior based on schedule information and visual information.

[1303] 7. "Means for generating suggestions" refers to a system that creates optimal action suggestions for users based on behavioral prediction and emotional information.

[1304] 8. An "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates that information.

[1305] 9. A "feedback mechanism" is a system that receives user responses and regenerates subsequent suggestions based on them.

[1306] System Configuration

[1307] This invention is a system that uses a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[1308] Hardware and software used

[1309] 1. Display devices: Smart glasses (e.g., Google Glass) and head-mounted displays (e.g., Oculus Quest), which are equipped with cameras, microphones, speakers, and Bluetooth communication capabilities.

[1310] 2. Mobile device: A smartphone (e.g., iPhone, Android device) with network connectivity.

[1311] 3. Generative AI platform: An artificial intelligence system on the cloud (e.g., Google Cloud AI Platform, AWS SageMaker).

[1312] 4. Emotion engines: facial expression recognition and speech analysis systems (e.g., Amazon Rekognition, IBM Watson).

[1313] Program processing flow

[1314] 1. Startup and connection: When a user puts on smart glasses or a head-mounted display and launches the smartphone app, the mobile device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. It monitors the overall system status and obtains the necessary data.

[1315] 2. Acquisition of visual and emotional information:

[1316] The camera on the display device captures visual information in real time and transmits it to a server via the mobile device.

[1317] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, and also transmits this to the server.

[1318] 3. Data synthesis and analysis:

[1319] The server sends schedule information, visual information, and emotional information to the artificial intelligence platform to predict user behavior.

[1320] For example, if a user has a meeting at 9 a.m. but is currently feeling very tired, the AI ​​will predict that the user needs to relax.

[1321] 4. Proposal generation and delivery:

[1322] Based on behavioral predictions and emotional information, generative AI generates optimal suggestions for users, such as "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[1323] The suggestions are transmitted to the display device via the mobile device and announced to the user via an audio speaker.

[1324] 5. User response processing and feedback loop:

[1325] The user's voice and gesture responses are captured by the emotion engine and sent to the server.

[1326] The server analyzes the user's response and again uses the generative artificial intelligence platform to generate the next suggestion.

[1327] If the user is in the midst of a refreshing time, the system will generate and notify the next suggestion, such as "Wash your face and start getting ready."

[1328] Specific examples

[1329] For example, if the emotion engine detects that a user working in an office is feeling stressed, the application will notify them, "Your stress level is high. Take a break and play some music to help you relax," and then play some music.

[1330] Prompt Sentence Examples

[1331] Analyze voice and facial expression data to detect if the user has an important presentation coming up and is experiencing high stress levels, then play relaxing music as an appropriate action suggestion.

[1332] Audio data: "I'm worried about whether my presentation will go well."

[1333] Facial expression data: Images containing facial tension, lip biting, etc.

[1334] This allows the system to provide appropriate security suggestions and personal support based on the user's real-time emotional state and behavioral predictions.

[1335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1336] Step 1:

[1337] The user puts on the display device and launches the application on the mobile device. The mobile device connects to the display device via Bluetooth. The mobile device then connects to the server via the network and begins accessing the generative artificial intelligence platform. At this point, the server begins monitoring the overall system status and acquiring data.

[1338] Input: Attaching a display device, launching an app on a mobile device

[1339] Output: Device connection status, system readiness status

[1340] Specific operation: The mobile terminal establishes a Bluetooth connection with the display device and starts communication with the server over the network.

[1341] Step 2:

[1342] The device retrieves the user's daily schedule information from the mobile device's schedule app, including the events and tasks for that day.

[1343] Input: Schedule data on your mobile device

[1344] Output: Retrieved schedule information

[1345] Specific operation: Extract time and task information from the mobile device's schedule app and send it to the server.

[1346] Step 3:

[1347] The camera on the display device is activated to capture visual information around the user in real time, and the captured video information is sent to a server via the mobile device.

[1348] Input: Visual data from a camera

[1349] Output: Visual information sent to the server

[1350] Specific operation: The camera on the display device captures surrounding images and transmits them to the server in real time.

[1351] Step 4:

[1352] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, which is also sent to the server.

[1353] Input: Voice data, facial expression data

[1354] Output: Emotion information extracted from the emotion engine

[1355] Specific operation: The emotion engine analyzes voice and facial expressions, quantifies stress levels and emotional states, and sends the results to the server.

[1356] Step 5:

[1357] The server sends schedule information, visual information, and emotional information to a generative artificial intelligence platform to predict user behavior.

[1358] Input: Schedule information, visual information, emotional information

[1359] Output: Behavioral prediction data generated by a generative AI platform

[1360] Specific operation: The generative artificial intelligence platform predicts user behavior based on the data received.

[1361] Step 6:

[1362] The generative AI generates optimal suggestions for users based on behavioral predictions and emotional information. For example, it might generate a suggestion such as, "You have 30 minutes until your meeting. Take a short break and refresh yourself." The suggestion is sent to the display device via the mobile device and notified to the user via the audio speaker.

[1363] Input: Behavioral prediction data, emotional information

[1364] Output: Generated action suggestions

[1365] What happens: The generated suggestions are announced to the user via audio and visual notification.

[1366] Step 7:

[1367] The user's response (voice or gestures) is captured by the display device's microphone or camera and sent to the server.

[1368] Input: Voice response, gesture response

[1369] Output: Response data sent to the server

[1370] Specific operation: The user's voice and gestures are detected and sent to the server.

[1371] Step 8:

[1372] The server analyzes the user's response and uses the AI ​​platform to generate the next suggestion and send it to the display device. For example, if the user is in the middle of a refreshing time, the server will notify the user to "wash their face and get ready."

[1373] Input: User response data

[1374] Output: Suggested next action

[1375] Specific operation: The generative artificial intelligence platform analyzes the user's response data, generates next action suggestions, and notifies them via voice.

[1376] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1377] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1378] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1379] [Fourth embodiment]

[1380] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1381] 7, a 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.

[1382] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1383] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1384] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1385] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1386] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1387] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1388] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1389] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1390] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1391] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1392] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1393] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by the user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Below, we will explain the specific program processing of this system and its explanation in natural language. Specific examples will also be provided.

[1394] System Configuration

[1395] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1396] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[1397] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule and visual information.

[1398] Program processing explanation

[1399] Step 1: Start up and connect

[1400] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[1401] Step 2: Get user information

[1402] The device retrieves the user's schedule information from the mobile device's scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[1403] Step 3: Data synthesis and analysis

[1404] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[1405] Step 4: Generate and deliver proposals

[1406] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[1407] Step 5: Process the user's response

[1408] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[1409] Step 6: Feedback Loop

[1410] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[1411] Specific examples

[1412] For example, if a user has an important presentation coming up, the system will determine that they need to start preparing based on their schedule and current status. The system will then provide a voice notification on the display device saying, "30 minutes until the presentation. Please make a final check of your materials," efficiently supporting the user's busy daily life. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation in real time.

[1413] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for making users' daily lives more efficient and meeting their individual needs.

[1414] The processing flow will be explained below.

[1415] Step 1:

[1416] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[1417] Step 2:

[1418] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[1419] Step 3:

[1420] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[1421] Step 4:

[1422] The server receives schedule information and visual information sent from the device and sends it to the generative AI platform, which analyzes this data and predicts user behavior.

[1423] Step 5:

[1424] Based on the analysis results, the generative AI generates optimal suggestions for the user. For example, if it determines that the user has a meeting at 9:00 a.m., it will generate a suggestion such as, "The meeting starts in 10 minutes. Please start getting ready."

[1425] Step 6:

[1426] The server transmits the generated proposal to the display device via the mobile terminal, and the terminal notifies the user of the proposal by voice through the speaker of the display device.

[1427] Step 7:

[1428] The user responds to the suggestions with voice or gesture, for example by saying "I get it" or nodding their head to indicate agreement.

[1429] Step 8:

[1430] The device captures the user's response with a microphone and camera and sends the data to the server, which analyzes the user's response and then requests the generative AI platform to take the next action.

[1431] Step 9:

[1432] The generative AI generates next action suggestions based on the user's response. For example, if it determines that the user is about to start preparing for a meeting, it will generate the next suggestion, such as "Get up immediately and wash your face."

[1433] Step 10:

[1434] The server then transmits the generated next proposal to the display device via the mobile terminal, which then announces the content of the proposal to the user by voice.

[1435] This completes the process of providing personalized, relevant suggestions in real time, tailored to the user's situation. By repeating this cycle, a system that efficiently supports the user's daily life is realized.

[1436] Example 1

[1437] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1438] Conventional AI-based proposal systems have had difficulty efficiently integrating and analyzing users' schedule information and real-time visual information to provide prompt and appropriate proposals. Furthermore, they lacked a feedback function that analyzes users' real-time responses and reflects them in the next proposal. This resulted in issues such as inaccurate prediction of user behavior and inadequate timing of proposals.

[1439] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1440] In this invention, the server includes a means for acquiring and analyzing the user's schedule information and visual information, a means for providing the generated proposals, a means for receiving and analyzing the user's responses, and a feedback means for generating the next proposal based on the analysis results, thereby enabling the server to accurately predict the user's behavior and continue to provide appropriate proposals in real time.

[1441] A "display device" is a device worn by a user to visually display information. Examples include head-mounted displays and smart glasses.

[1442] A "mobile terminal" is a mobile communication device that can be carried by a user, such as a smartphone or tablet.

[1443] The "generative artificial intelligence platform" is a cloud-based artificial intelligence system that generates behavioral predictions and suggestions based on a user's schedule information and visual information.

[1444] "Schedule information" refers to information about a user's schedule and tasks, including, for example, events and reminders registered in a calendar application.

[1445] "Visual information" refers to video data of the user's surroundings captured by the camera of the display device.

[1446] "Behavior prediction" is the process of predicting future behavior based on a user's current situation and schedule information.

[1447] "Suggestions" refer to instructions or advice provided to users by the generating AI.

[1448] "Feedback" is a series of processes that analyzes user responses and generates new suggestions.

[1449] A "response" is a user's reaction or reply to a suggestion, such as a voice command or gesture.

[1450] The present invention is a system that predicts user behavior and provides appropriate suggestions using a display device worn by a user, a mobile device capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile device. Specific embodiments of this system are described below.

[1451] System Configuration

[1452] 1. Display device

[1453] It is a device such as a head-mounted display or smart glasses worn by the user, and is equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1454] 2. Mobile devices

[1455] It is a mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[1456] 3. Generative AI Platform

[1457] It is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information and visual information.

[1458] System Operation

[1459] Startup and Connection

[1460] When a user puts on a display device (e.g., AR glasses) and turns on a mobile device (e.g., a smartphone), the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[1461] Retrieving User Information

[1462] The device retrieves the user's schedule information from the scheduler application. At the same time, the display device's camera captures visual information around the user and sends the data to the server. The server then sends the data to the artificial intelligence platform for analysis.

[1463] Data integration and analysis

[1464] The server then uses the acquired schedule and visual information to have the generative AI predict the user's behavior. For example, if the user has a meeting at 9 a.m. and is currently in bed, the AI ​​will determine that the user needs to get up and start getting ready.

[1465] Proposal generation and distribution

[1466] The generative AI generates appropriate suggestions based on predicted behavior. The server sends these suggestions to the display device via the mobile device. The device then announces through a speaker, "The meeting will start in 10 minutes. Please begin preparations."

[1467] User response processing

[1468] The user responds by saying "Got it" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and requests the generation AI to take the next action.

[1469] Feedback Loop

[1470] Based on the response, the AI ​​generates the next suggested action. For example, it might generate a suggestion such as "Get up immediately and wash your face," and send it back to the display device via the server. The device then communicates the suggestion by voice.

[1471] Specific examples

[1472] For example, if a user has an important presentation coming up at 2 p.m., the system will provide voice notifications in the morning with important information, such as "Please make a final check of the presentation materials." Furthermore, if the camera detects that the user has not checked the materials, it will send an alert saying, "You don't have time to check the materials. Please start checking them quickly," providing thorough support for the user's actions.

[1473] Prompt Sentence Examples

[1474] "It's currently 8:30 AM and a user is in bed with an important meeting at 9 AM. Let's use this information to have a generative AI model suggest an action."

[1475] The above is an embodiment of the present invention. This system can make users' daily lives more efficient and provide appropriate suggestions in real time that meet individual needs.

[1476] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1477] Step 1: Start up and connect

[1478] 1.1. The system starts up when a user wears a display device (e.g., AR glasses) and turns on a mobile device (e.g., smartphone). The inputs are the power status of the AR glasses and the smartphone. The output is the system startup.

[1479] 1.2. The terminal automatically pairs with the display device via Bluetooth and checks the connection status. The input is the Bluetooth pairing request from the display device and the mobile terminal, and the output is a connection establishment notification.

[1480] 1.3. The server connects to the network via the mobile device and begins accessing the generative AI platform. The input is the mobile device's network connection request, and the output is the establishment of a connection to the generative AI platform. Specifically, the server accesses the generative AI platform on the cloud via the network and begins monitoring the status of the entire system.

[1481] Step 2: Get user information

[1482] 2.1. The terminal obtains the user's schedule information from the scheduler application. The input is a database query of the scheduler application, and the output is the user's schedule information.

[1483] The camera of the display device captures the visual information of the user's surroundings. The input is the real-time image acquisition request of the camera, and the output is the visual information data.

[1484] 2.3. The terminal transmits the acquired visual information data to the server. The input is visual information data, and the output is data transmission to the server.

[1485] 2.4. The server sends this schedule information and visual information to the generative AI platform and begins analysis. The input is the schedule information and visual information, and the output is the analysis results by the generative AI. Specifically, the server integrates the user's schedule information with the visual information from the camera and sends it to the generative AI.

[1486] Step 3: Data synthesis and analysis

[1487] 3.1. The server integrates schedule information and visual information to grasp the user's current situation. The input is schedule information and visual information, and the output is the integrated user situation data.

[1488] 3.2. The server issues an instruction to the generative AI model to start analysis. The input is the integrated user situation data, and the output is an instruction to start analysis.

[1489] 3.3. The Generative AI receives information that the user has a meeting at 9:00 AM and is currently still in bed. Based on this information, it predicts that the user needs to get up and start getting ready. The input is the integrated situation data, and the output is the behavior prediction result. Specifically, the Generative AI analyzes the schedule and visual information to predict the user's next action.

[1490] Step 4: Generate and distribute proposals

[1491] 4.1. Generative AI generates proposals based on behavioral prediction. The input is the behavioral prediction result, and the output is the proposal data.

[1492] 4.2. The server sends the generated proposal to the display device via the mobile device. The input is the proposal data, and the output is the proposal notification data.

[1493] 4.3. The device converts the proposal into voice and notifies the user through the AR glasses' speaker. The input is the proposal notification data, and the output is a voice notification. Specifically, the device uses voice conversion software to convert the proposal into voice and notify the user, "The meeting will start in 10 minutes. Please start getting ready."

[1494] Step 5: Process the user's response

[1495] 5.1. The user responds by saying "OK" or by nodding their head. The input is the user's response to the proposal, and the output is the response data.

[1496] 5.2. The device captures the response and sends it to the server. The input is the user response data, and the output is sending the response data to the server. Specifically, the device uses a microphone and camera to capture the user's voice and actions.

[1497] 5.3. The server analyzes the user's response data and requests the generation AI to take the next action. The input is the user's response data, and the output is a request to generate the next action.

[1498] Step 6: Feedback Loop

[1499] 6.1. The generation AI generates the next action proposal based on the user's response. The input is a request to generate the next action, and the output is the next proposal data.

[1500] 6.2. The server sends this new proposal to the mobile device. The input is the next proposal data, and the output is the proposal notification data.

[1501] 6.3. The device converts the suggestion into voice and communicates it to the user through the AR glasses. The input is the suggestion notification data, and the output is a voice notification. Specifically, the device again uses the voice conversion software to convert the suggestion into voice and notify the user, "Get up immediately and go to the bathroom to wash your face."

[1502] (Application example 1)

[1503] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1504] There is a growing need for systems that improve the efficiency of users' daily lives and work and provide appropriate support tailored to their individual needs. However, conventional systems have limited ability to predict user behavior and make personalized suggestions in real time. Furthermore, it has been difficult to communicate with users interactively through natural interfaces such as voice and gaze. In particular, there are very few systems that can make suggestions that appropriately reflect a user's preferences and purchasing history when it comes to product recommendations and purchasing procedures on online shopping sites.

[1505] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1506] In this invention, the server includes a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from a camera of the display device, means for generating appropriate suggestions for the user based on the prediction, means for providing the suggestions to the user through the display device, feedback means for receiving a user's response and again using the generative artificial intelligence platform to generate a next suggestion, means for recommending products based on the user's purchase history and visual information, means for displaying the recommended product information on the display device, and means for receiving a user's voice or gaze response and generating a next action. This makes it possible to accurately predict user behavior and provide personalized product recommendations and suggestions in real time.

[1507] "User" refers to an individual or corporation that uses the system.

[1508] A "display device" is a device worn by a user that can provide information visually or audibly, and includes smart glasses and head-mounted displays.

[1509] "Mobile terminal" refers to a mobile device such as a smartphone or tablet that has a network connection function and can communicate with a display device.

[1510] A "generative artificial intelligence platform" refers to an AI system that resides in the cloud and is connected via a network to analyze and make predictions on user data.

[1511] "Schedule information" refers to data related to a user's plans and tasks, and is obtained from a mobile device.

[1512] "Visual information" refers to image data about the user's surroundings and objects captured by the display device's camera.

[1513] The term "behavior prediction means" refers to a method and device for predicting a user's next behavior based on schedule information and visual information.

[1514] "Proposal generator" refers to a method and apparatus for generating appropriate suggestions for a user based on behavioral predictions.

[1515] "Feedback means" refers to methods and devices for receiving user responses and generating suggestions again.

[1516] "Purchase history" refers to data on products purchased by a user in the past.

[1517] "Visual information" refers to image data about the objects and environment the user is currently viewing.

[1518] "Product recommendation means" refers to a method and device for recommending suitable products to a user based on purchase history and visual information.

[1519] "Voice response means" refers to a method and apparatus that receives and acts upon a user's voice input.

[1520] "Gaze responsive means" refers to methods and devices that detect and use a user's gaze as a response.

[1521] "Action generation means" refers to a method and apparatus for directing the next action based on the user's response.

[1522] This invention is a system that recommends appropriate products in real time based on a user's purchasing history and visual information, using a display device worn by the user, a mobile terminal capable of communicating with the display device, and a generative artificial intelligence platform connected to a network via the mobile terminal.

[1523] System Configuration

[1524] 1. Display device: A device such as smart glasses or a head-mounted display worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1525] 2. Mobile device: A mobile device such as a smartphone or tablet with a shopping application installed.

[1526] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and recommends products based on users' purchasing history and visual information.

[1527] Program processing explanation

[1528] In the present invention, the server, terminal, and user operate the system using the following hardware and software.

[1529] 1. The display device (smart glasses) is equipped with a camera, microphone, speaker, and Bluetooth communication function. The camera is used to capture the user's visual information, the microphone is used to receive audio input, and the speaker is used to provide audio feedback.

[1530] 2. A shopping app with network connectivity is installed on the mobile device (smartphone), and the user's purchase history and schedule information can be obtained through this app. The mobile device communicates with the display device via Bluetooth, collecting the necessary data and sending it to the server.

[1531] 3. The generative AI platform is operated using a cloud-based AI system (generative AI model). This platform analyzes purchase history and visual information sent from mobile devices, predicts user behavior, and recommends appropriate products. The recommendation results are sent to the display device via the mobile device.

[1532] The server uses a generative AI model to predict user behavior and recommend appropriate products to the user based on purchase history and visual information. For example, when a user wears smart glasses and looks at a store shelf, the smart glasses display shows, "We recommend a new smartphone." In this case, an example of a prompt to input into the generative AI model is, "Please generate the next product to recommend based on the product the user is viewing and their past purchase history."

[1533] This allows the server to provide personalized product recommendations and suggestions in real time, allowing users to efficiently find the products they want and complete the purchase process, making online shopping more convenient and providing a satisfying experience for users.

[1534] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1535] Step 1:

[1536] The user puts on the smart glasses and launches a shopping app on their smartphone. The smartphone automatically connects to the smart glasses via Bluetooth and begins accessing the generative artificial intelligence platform via the network. The server monitors the overall system status via the smartphone and begins acquiring the necessary data.

[1537] Input: Wearing smart glasses, turning on smartphone

[1538] Output: Establishing a Bluetooth connection between smart glasses and a smartphone

[1539] Step 2:

[1540] The server retrieves the user's purchase history and current schedule information from the smartphone's shopping app. The camera in the smart glasses captures the visual information the user is looking at and sends the data in real time to the server. The server then sends this data to the artificial intelligence platform for analysis.

[1541] Input: Purchase history, schedule information, visual information

[1542] Output: Dataset for analysis

[1543] Step 3:

[1544] The server sends a prompt to the generative AI model based on the acquired purchase history and visual information to predict the user's behavior. An example of this prompt is, "Based on the products the user is viewing and their past purchase history, please generate the next product recommendation." The generative AI model analyzes the input data and predicts the user's behavior.

[1545] Input: purchase history, visual information, prompt text

[1546] Output: Behavior prediction results

[1547] Step 4:

[1548] The generative AI model recommends appropriate products based on behavioral predictions. The server receives the recommendation results and sends them to the smart glasses, which then display the message "We recommend a new smartphone."

[1549] Input: Behavior prediction result

[1550] Output: Recommended product information

[1551] Step 5:

[1552] The user responds with a voice command such as "Learn more" or "Buy" or by using their gaze. The smart glasses' microphone and eye tracking capture this response and send it to the server, which analyzes it and asks the generative AI model to generate the next action.

[1553] Input: Voice command or gaze information

[1554] Output: Next action instructions

[1555] Step 6:

[1556] The generative AI model generates the next action (e.g., start a checkout or recommend other products) based on the user's response. The server sends this information to the smart glasses, which then initiates the next step.

[1557] Input: Next action instructions

[1558] Output: Result of the next action

[1559] This process allows users to receive personalized product recommendations in real time and easily complete the purchase process, evolving online shopping into a more convenient and engaging experience.

[1560] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1561] The present invention is a system that predicts a user's behavior and provides appropriate suggestions according to their emotional state using a display device worn by the user, a mobile device capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile device, and an emotion engine that recognizes the user's emotions. Below, we will show the specific program processing of this system and explain it in natural language. Specific examples will also be provided.

[1562] System Configuration

[1563] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1564] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[1565] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[1566] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[1567] Program processing explanation

[1568] Step 1: Start up and connect

[1569] When a user puts on a display device such as AR glasses and turns on their mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[1570] Step 2: Get user information

[1571] The device retrieves the user's schedule information from the mobile device's scheduler application, which includes, for example, the day's events and tasks.

[1572] Step 3: Visual Acquisition

[1573] The terminal activates the camera on the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[1574] Step 4: Obtaining emotional information

[1575] Using the device's microphone and camera, the emotion engine extracts emotional information from the user's voice and facial expressions. The emotion engine then quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends this information to the server.

[1576] Step 5: Data synthesis and analysis

[1577] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[1578] Step 6: Generate and deliver proposals

[1579] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[1580] Step 7: Process the user's response

[1581] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[1582] Step 8: Feedback Loop

[1583] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[1584] Specific examples

[1585] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify the user, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the present invention can provide personalized and appropriate suggestions tailored to the user's situation and emotions in real time.

[1586] The above is a specific embodiment for carrying out the present invention. The present invention functions as a powerful tool for streamlining the user's daily life and meeting individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping the user live a more comfortable life.

[1587] The processing flow will be explained below.

[1588] Step 1:

[1589] The user puts on the AR glasses and turns on the mobile device. The device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform.

[1590] Step 2:

[1591] The device retrieves the user's schedule information from the mobile device's scheduler application, for example, information about an important meeting scheduled for 9:00 AM.

[1592] Step 3:

[1593] The terminal activates the camera of the display device to capture visual information around the user in real time, and the captured video information is sent to the server.

[1594] Step 4:

[1595] The device uses an emotion engine to analyze the user's facial expressions and voice via the display device's camera and microphone to recognize their emotional state, for example, determining whether they are feeling stressed.

[1596] Step 5:

[1597] The server sends the schedule information, visual information, and emotional information sent from the device to the AI ​​platform to predict the user's behavior. For example, if the user is feeling stressed, it will determine that they need time to relax.

[1598] Step 6:

[1599] Based on the prediction results, the generative AI generates optimal suggestions for the user, such as "There are still 30 minutes until the meeting, so please take a break."

[1600] Step 7:

[1601] The server sends the generated suggestion to the display device via the mobile device, which then announces through the display device's speaker, "You have 30 minutes until the meeting. Please take a short break and refresh yourself."

[1602] Step 8:

[1603] The user responds by saying "OK" or nodding their head, and the device's microphone or camera captures this response and sends the information to the server.

[1604] Step 9:

[1605] The server analyzes the user's response information and further requests the generation artificial intelligence platform to take the next action, for example, determining that appropriate music should be provided while the user is refreshing.

[1606] Step 10:

[1607] The generation AI generates a suggestion for the next action. For example, it may generate a suggestion such as "Play music for relaxation." The server then sends this to the display device via the mobile device. The device then conveys the suggestion to the user via voice.

[1608] This completes the process of providing personalized suggestions in real time based on the user's situation and emotional state, allowing the cycle to be repeated to help users live their daily lives efficiently and comfortably.

[1609] Example 2

[1610] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1611] In recent years, there has been a demand for systems that can predict user behavior and provide appropriate suggestions. However, few existing systems take the user's emotional state into account, making it difficult to provide more highly personalized suggestions. Furthermore, they lack sufficient feedback functionality to analyze user responses in real time and reflect them in future suggestions. There is a need to solve these problems and provide systems that are more beneficial and efficient for users.

[1612] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1613] In this invention, the server includes an emotion engine that recognizes the user's emotions, means for predicting the user's behavior based on the user's schedule information acquired from the mobile terminal, visual information acquired from the camera of the display device, and emotion information acquired from the emotion engine, and means for generating appropriate suggestions for the user based on the prediction, thereby making it possible to predict the user's behavior and generate suggestions that take the user's emotional state into consideration.

[1614] A "display device" is a device worn by the user that is equipped with a camera, microphone, speaker, and Bluetooth communication capabilities.

[1615] A "mobile terminal" is a mobile device with network connectivity and an application that manages the user's schedule information.

[1616] The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[1617] The "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[1618] The "means for predicting user behavior" is a means having a function for predicting the user's next behavior or state based on the user's schedule information, visual information, and emotional information.

[1619] The "means for generating appropriate suggestions for the user" refers to a means having a function for automatically generating optimal suggestions for the user based on the predicted behavior and state of the user.

[1620] The "means for providing the user with the proposal" refers to a means having a function of notifying the user of the generated proposal and presenting its contents.

[1621] A "feedback means" is a means that has the function of receiving a user's response and generating further suggestions based on that information.

[1622] The present invention is a system that predicts a user's behavior and provides appropriate suggestions based on their emotional state using a display device worn by the user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[1623] System Configuration

[1624] 1. Display device: A device such as a head-mounted display or smart glasses worn by the user, equipped with a camera, speaker, microphone, and Bluetooth communication capabilities.

[1625] 2. Mobile terminal: A mobile device such as a smartphone or tablet that has network connectivity and a scheduler application.

[1626] 3. Generative AI platform: An AI system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule, visual information, and emotional information.

[1627] 4. Emotion engine: A system that recognizes emotions from the user's voice and facial expressions and generates emotional information.

[1628] Specific configuration and operation of the present invention

[1629] When the user wears the display device and turns on the mobile device, the device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. At this point, the server monitors the overall system status and begins acquiring the necessary data.

[1630] The device retrieves the user's schedule information from the mobile device's scheduler application, including the day's events and tasks. For example, it retrieves specific information such as "I have one meeting scheduled for 9 a.m. today and one presentation scheduled for 3 p.m."

[1631] Next, the device activates the display device's camera and captures visual information around the user in real time. The captured video information is sent to the server. Furthermore, the emotion engine extracts emotional information from the user's voice and facial expressions using the device's microphone and camera. The emotion engine quantifies the user's emotional state, such as stress level and joy, anger, sadness, or happiness, and sends that information to the server.

[1632] The server sends schedule information, visual information, and emotional information to the AI ​​platform to predict user behavior. For example, if a user has a meeting at 9 a.m. but is currently very tired, the AI ​​will predict that the user should get up earlier and take time to relax.

[1633] The generative AI generates optimal suggestions for the user based on behavioral predictions and emotional information. The server sends these suggestions to the display device via the mobile device. The device then notifies the user through a speaker, saying, "There are 30 minutes until the meeting. Please take a short break and refresh yourself."

[1634] The user responds by saying "OK" or nodding their head. The device's microphone or camera captures this response and sends it to the server. The server analyzes the user's response and then requests the generative AI platform to take the next action.

[1635] Based on the response, the AI ​​generates a suggestion for the next action. For example, if the user is taking a refreshing break, the AI ​​will generate a suggestion such as "Wash your face and start getting ready." The server then sends this to the display device via the mobile device, which then notifies the user by voice.

[1636] Specific examples

[1637] For example, if a user has an important presentation coming up and the emotion engine detects a high stress level, the system will notify them, "You have one hour until your presentation. I'll play some relaxing music," and play appropriate music based on the user's schedule and emotional state. In this way, the system can provide personalized and appropriate suggestions in real time that are tailored to the user's situation and emotions.

[1638] The above is a specific embodiment of the present invention. This invention functions as a powerful tool for streamlining users' daily lives and meeting their individual needs. By taking the user's emotional state into consideration, more accurate action suggestions can be made, helping users live more comfortably.

[1639] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1640] Step 1: Start up and connect

[1641] Terminal behavior:

[1642] The user wears the display device and turns on the power of the mobile terminal.

[1643] Input: User-initiated activation of display devices and mobile devices

[1644] Data processing: The terminal connects to the display device via Bluetooth and checks various sensor information (camera, microphone, etc.).

[1645] Output: Confirm the connection between the display device and the mobile device and check that it works properly.

[1646] Specific operation: The mobile device automatically connects to the display device via Bluetooth, and then checks the operation of the display device's camera, microphone, and speaker.

[1647] Server behavior:

[1648] Input: Device is connected to the network

[1649] Data processing: The server initiates access to the generative AI platform and monitors the overall system status.

[1650] Output: System status information and start of data acquisition

[1651] Specific operation: The server monitors the status of data sent from the display device and emotion engine via the mobile device.

[1652] Step 2: Get user information

[1653] Terminal behavior:

[1654] Input: Scheduler application on mobile device

[1655] Data processing: The device obtains the user's schedule information and temporarily stores it.

[1656] Output: User's schedule information

[1657] Specific operation: The device retrieves information from the scheduler application, such as "You have one meeting scheduled for 9:00 AM today and one presentation scheduled for 3:00 PM today."

[1658] Server behavior:

[1659] Input: User schedule information sent from the device

[1660] Data processing: The server receives the schedule information and stores it for the next processing step.

[1661] Output: Schedule information storage

[1662] What happens: The server saves the schedule information to a database.

[1663] Step 3: Visual acquisition

[1664] Terminal behavior:

[1665] Input: Display camera

[1666] Data processing: The device activates the camera to capture visual information around the user in real time and temporarily store it.

[1667] Output: Visual information

[1668] Specific operation: The display device's camera captures objects and scenery within the user's field of view and sends the video information to the device.

[1669] Server behavior:

[1670] Input: Visual information sent from the device

[1671] Data processing: The server receives the visual information and stores it for the next processing step.

[1672] Output: Visual information storage

[1673] What happens: The server stores the visual information in a database.

[1674] Step 4: Acquiring emotional information

[1675] Terminal behavior:

[1676] Input: Display device microphone and camera

[1677] Data processing: The device records the user's voice and facial expressions in real time and temporarily stores them.

[1678] Output: Audio and facial expression data

[1679] Specific operation: The display device's microphone captures the user's voice and the camera captures the user's facial expressions.

[1680] Emotion Engine in action:

[1681] Input: Voice and facial expression data sent from the device

[1682] Data processing: The emotion engine quantifies the user's stress level and emotional state and generates information about them.

[1683] Output: Emotional information

[1684] How it works: The emotion engine analyzes the user's real-time emotional state and quantifies stress levels, joy, anger, sadness, and happiness.

[1685] Server behavior:

[1686] Input: Emotion information sent from the emotion engine

[1687] Data processing: The server receives the emotion information and stores it for the next processing step.

[1688] Output: Emotional information storage

[1689] Specific operation: The server stores the emotion information in a database.

[1690] Step 5: Data synthesis and analysis

[1691] Server behavior:

[1692] Input: Schedule information, visual information, emotional information

[1693] Data processing: The server integrates this information and sends it to the Generative AI Platform, which then uses it to predict user behavior.

[1694] Output: Behavior prediction results

[1695] Specific operation: The server sends schedule information, visual information, and emotional information to the generative artificial intelligence platform, and the AI ​​predicts the user's next action.

[1696] Step 6: Generate and deliver proposals

[1697] Generative AI behavior:

[1698] Input: Behavior prediction results and emotion information

[1699] Data processing: The generative artificial intelligence platform generates optimal suggestions for users based on behavioral predictions and emotional information.

[1700] Output: Proposal

[1701] What it does: The AI ​​generates suggestions like, "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[1702] Server behavior:

[1703] Input: Suggestions from the generative AI

[1704] Data processing: The server sends the proposal to the mobile device.

[1705] Output: Send proposal

[1706] Specific operation: The server sends the proposal content to the terminal, and the terminal transfers it to the display device.

[1707] Terminal behavior:

[1708] Input: Proposal sent from the server

[1709] Data processing: The device begins generating the suggestions as voice output.

[1710] Output: Audio notification

[1711] What it does: The device speaker will announce to the user, "You have 30 minutes until your meeting. Please take a short break and refresh yourself."

[1712] Step 7: Process the user's response

[1713] User Action:

[1714] Input: Proposal notification from device

[1715] Data processing: The user responds by saying "Got it" or nodding their head.

[1716] Output: User's voice or gesture response

[1717] Specific Action: The user responds verbally with "Got it" or nods their head in response.

[1718] Terminal behavior:

[1719] Input: The user's voice or gesture response

[1720] Data processing: The device's microphone and camera capture the user's responses and send them to the server.

[1721] Output: Sending user response

[1722] What it does: The device captures the user's response (voice or gesture) and sends it to the server.

[1723] Server behavior:

[1724] Input: User Response

[1725] Data processing: The server analyzes the user's response and requests the generative AI platform to take the next action.

[1726] Output: Next action request

[1727] Specific operation: The server sends the user's response data to the generation artificial intelligence platform and requests a suggestion for the next action.

[1728] Step 8: Feedback Loop

[1729] Generative AI behavior:

[1730] Input: Analysis results based on user responses

[1731] Data processing: A generative AI platform generates next action suggestions based on user responses.

[1732] Output: Suggested next action

[1733] Specific behavior: The AI ​​generates the next suggestion, such as "Wash your face and start getting ready."

[1734] Server behavior:

[1735] Input: Next suggestion from the generative AI

[1736] Data processing: The server sends the next proposal to the mobile device.

[1737] Output: Send next proposal

[1738] Specific operation: The server sends the next proposal to the terminal, which then transfers it to the display device.

[1739] Terminal behavior:

[1740] Input: Next suggestion sent by the server

[1741] Data processing: The device begins generating the next suggestion as a voice output.

[1742] Output: Audio notification

[1743] What it does: The device speaker will announce, "Wash your face and get ready."

[1744] The above is the specific flow of the program processing of this system. This system is a highly functional tool that takes into account the user's situation and emotions and provides optimal suggestions.

[1745] (Application example 2)

[1746] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1747] Conventional security systems have difficulty predicting users' behavior and providing safety suggestions based on their emotional state, which has limited their effectiveness, especially in the areas of stress management and personal security. Furthermore, they are unable to properly reflect the user's real-time emotional state, resulting in suboptimal security suggestions.

[1748] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a mobile terminal capable of communicating with the display device worn by the user, a generative artificial intelligence platform connected to the network via the mobile terminal, means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from the display device's camera, means for generating appropriate suggestions for the user based on the prediction, means for providing suggestions to the user through the display device, an emotion engine for recognizing the user's emotional state from voice and facial expressions, means for generating security-related suggestions and action instructions for the user based on the emotion information acquired from the emotion engine, and feedback means for receiving the user's response and again using the generative artificial intelligence platform to generate the next suggestion. This makes it possible to make security suggestions based on the user's real-time emotional state and behavioral predictions.

[1749] 1. A "display device" is a device that can be worn by a user and has a camera, speaker, microphone, and Bluetooth communication capabilities.

[1750] 2. "Mobile terminal" means a mobile device, such as a smartphone or tablet, that can communicate with a display device and has network connectivity.

[1751] 3. The "generative artificial intelligence platform" is an artificial intelligence system that exists on the cloud and predicts behavior and generates suggestions based on the user's schedule information, visual information, and emotional information.

[1752] 4. "Schedule information" refers to information about the user's daily schedule, tasks, and other time allocation.

[1753] 5. "Visual information" refers to images and image data of the user's surroundings acquired through the camera of the display device.

[1754] 6. "Means for predicting behavior" is a system for predicting a user's next behavior based on schedule information and visual information.

[1755] 7. "Means for generating suggestions" refers to a system that creates optimal action suggestions for users based on behavioral prediction and emotional information.

[1756] 8. An "emotion engine" is a system that recognizes emotions from the user's voice and facial expressions and generates that information.

[1757] 9. A "feedback mechanism" is a system that receives user responses and regenerates subsequent suggestions based on them.

[1758] System Configuration

[1759] This invention is a system that uses a display device worn by a user, a mobile terminal capable of communicating with the display device, a generative artificial intelligence platform connected to a network via the mobile terminal, and an emotion engine that recognizes the user's emotions.

[1760] Hardware and software used

[1761] 1. Display devices: Smart glasses (e.g., Google Glass) and head-mounted displays (e.g., Oculus Quest), which are equipped with cameras, microphones, speakers, and Bluetooth communication capabilities.

[1762] 2. Mobile device: A smartphone (e.g., iPhone, Android device) with network connectivity.

[1763] 3. Generative AI platform: An artificial intelligence system on the cloud (e.g., Google Cloud AI Platform, AWS SageMaker).

[1764] 4. Emotion engines: facial expression recognition and speech analysis systems (e.g., Amazon Rekognition, IBM Watson).

[1765] Program processing flow

[1766] 1. Startup and connection: When a user puts on smart glasses or a head-mounted display and launches the smartphone app, the mobile device automatically connects to the display device via Bluetooth. The server connects to the network via the mobile device and begins accessing the generative AI platform. It monitors the overall system status and obtains the necessary data.

[1767] 2. Acquisition of visual and emotional information:

[1768] The camera on the display device captures visual information in real time and transmits it to a server via the mobile device.

[1769] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, and also transmits this to the server.

[1770] 3. Data synthesis and analysis:

[1771] The server sends schedule information, visual information, and emotional information to the artificial intelligence platform to predict user behavior.

[1772] For example, if a user has a meeting at 9 a.m. but is currently feeling very tired, the AI ​​will predict that the user needs to relax.

[1773] 4. Proposal generation and delivery:

[1774] Based on behavioral predictions and emotional information, generative AI generates optimal suggestions for users, such as "You have 30 minutes until your meeting. Take a short break and refresh yourself."

[1775] The suggestions are transmitted to the display device via the mobile device and announced to the user via an audio speaker.

[1776] 5. User response processing and feedback loop:

[1777] The user's voice and gesture responses are captured by the emotion engine and sent to the server.

[1778] The server analyzes the user's response and again uses the generative artificial intelligence platform to generate the next suggestion.

[1779] If the user is in the midst of a refreshing time, the system will generate and notify the next suggestion, such as "Wash your face and start getting ready."

[1780] Specific examples

[1781] For example, if the emotion engine detects that a user working in an office is feeling stressed, the application will notify them, "Your stress level is high. Take a break and play some music to help you relax," and then play some music.

[1782] Prompt Sentence Examples

[1783] Analyze voice and facial expression data to detect if the user has an important presentation coming up and is experiencing high stress levels, then play relaxing music as an appropriate action suggestion.

[1784] Audio data: "I'm worried about whether my presentation will go well."

[1785] Facial expression data: Images containing facial tension, lip biting, etc.

[1786] This allows the system to provide appropriate security suggestions and personal support based on the user's real-time emotional state and behavioral predictions.

[1787] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1788] Step 1:

[1789] The user puts on the display device and launches the application on the mobile device. The mobile device connects to the display device via Bluetooth. The mobile device then connects to the server via the network and begins accessing the generative artificial intelligence platform. At this point, the server begins monitoring the overall system status and acquiring data.

[1790] Input: Attaching a display device, launching an app on a mobile device

[1791] Output: Device connection status, system readiness status

[1792] Specific operation: The mobile terminal establishes a Bluetooth connection with the display device and starts communication with the server over the network.

[1793] Step 2:

[1794] The device retrieves the user's daily schedule information from the mobile device's schedule app, including the events and tasks for that day.

[1795] Input: Schedule data on your mobile device

[1796] Output: Retrieved schedule information

[1797] Specific operation: Extract time and task information from the mobile device's schedule app and send it to the server.

[1798] Step 3:

[1799] The camera on the display device is activated to capture visual information around the user in real time, and the captured video information is sent to a server via the mobile device.

[1800] Input: Visual data from a camera

[1801] Output: Visual information sent to the server

[1802] Specific operation: The camera on the display device captures surrounding images and transmits them to the server in real time.

[1803] Step 4:

[1804] The emotion engine uses the display device's microphone and camera to extract emotion information from the user's voice and facial expressions, which is also sent to the server.

[1805] Input: Voice data, facial expression data

[1806] Output: Emotion information extracted from the emotion engine

[1807] Specific operation: The emotion engine analyzes voice and facial expressions, quantifies stress levels and emotional states, and sends the results to the server.

[1808] Step 5:

[1809] The server sends schedule information, visual information, and emotional information to a generative artificial intelligence platform to predict user behavior.

[1810] Input: Schedule information, visual information, emotional information

[1811] Output: Behavioral prediction data generated by a generative AI platform

[1812] Specific operation: The generative artificial intelligence platform predicts user behavior based on the data received.

[1813] Step 6:

[1814] The generative AI generates optimal suggestions for users based on behavioral predictions and emotional information. For example, it might generate a suggestion such as, "You have 30 minutes until your meeting. Take a short break and refresh yourself." The suggestion is sent to the display device via the mobile device and notified to the user via the audio speaker.

[1815] Input: Behavioral prediction data, emotional information

[1816] Output: Generated action suggestions

[1817] What happens: The generated suggestions are announced to the user via audio and visual notification.

[1818] Step 7:

[1819] The user's response (voice or gestures) is captured by the display device's microphone or camera and sent to the server.

[1820] Input: Voice response, gesture response

[1821] Output: Response data sent to the server

[1822] Specific operation: The user's voice and gestures are detected and sent to the server.

[1823] Step 8:

[1824] The server analyzes the user's response and uses the AI ​​platform to generate the next suggestion and send it to the display device. For example, if the user is in the middle of a refreshing time, the server will notify the user to "wash their face and get ready."

[1825] Input: User response data

[1826] Output: Suggested next action

[1827] Specific operation: The generative artificial intelligence platform analyzes the user's response data, generates next action suggestions, and notifies them via voice.

[1828] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1829] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1830] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1831] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1832] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1833] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1834] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1835] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1836] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1837] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1838] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1839] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1840] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1841] 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.

[1842] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1843] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1844] The hardware resource that execute...

Claims

1. a display device worn by a user; a mobile terminal capable of communicating with the display device; A generative artificial intelligence platform connected to a network via the mobile terminal; a means for predicting user behavior based on the user's schedule information acquired from the mobile terminal and visual information acquired from the camera of the display device; means for generating appropriate suggestions for a user based on said prediction; means for providing said suggestions to a user through said display device; A feedback means receives a user's response and uses the artificial intelligence platform again to generate a next suggestion. Including system.

2. 2. The system of claim 1, wherein said display device includes an audio speaker and means for providing said suggestions audibly.

3. The system of claim 1 further comprising means for receiving the user's response by voice or gesture.

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