system

Abstract

Provide a system. The device includes a device means for transmitting a specific ID carried by a user; A means for an edge computer to receive an ID of the device means; A means for the edge computer to transmit the ID to a server and obtain information related to the user and a generating AI model; A means for providing a user with personalized services using the information acquired by the edge computer and the generated AI model; A system including:

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 recent years, with the increasing use of personalized data analysis and generative AI models, there is a demand for providing individually optimized services while protecting user privacy. However, previous technologies have limited the means for users to carry their own data and AI models and use them in real time when needed. In addition, many devices and systems require individual settings and data loading, which is cumbersome for users. A new system is needed to solve these problems and enable users to receive personalized services easily and safely. [Means for solving the problem]

[0005] This invention proposes a system that includes a device means for transmitting a specific ID carried by a user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to a server and acquire information and a generative AI model related to the user, and a means for providing the user with personalized services using the information and generative AI model acquired by the edge computer. This system enables users to carry their own data and generative AI models and receive optimized services in real time when needed. In particular, through specific application examples such as adjusting driving patterns and in-car environments in autonomous vehicles and operating devices in smart homes, user convenience and safety can be improved.

[0006] "User" means an individual or corporation that uses the system.

[0007] "ID" is a code or number that uniquely identifies a particular device or user.

[0008] "Device means" refers to a device or apparatus that a user can carry and that can transmit a specific ID.

[0009] An "edge computer" is a computer that distributes data processing and processes information in real time near the user.

[0010] A "server" is a central management system that stores, manages, and provides data on a network.

[0011] "Information" includes data about users, generative AI models, history, and other related data.

[0012] A "generative AI model" is an artificial intelligence model that is learned and generated based on user data and can perform specific tasks or services.

[0013] "Personalized Services" are services or features tailored to a user's specific needs and preferences.

[0014] An "autonomous vehicle" is a vehicle that is driven autonomously by a computer system.

[0015] A "smart home device" is a device or equipment used to control and manage electrical appliances and facilities in the home via a network. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[0038] This system allows users to carry a device (hereinafter referred to as "device") that transmits a specific ID, and by having this device communicate with a server via an edge computer, the system provides users with personalized services that are optimized for them. The basic components of this system include the device, the edge computer, and the server.

[0039] Component Description

[0040] 1. Devices carried by users

[0041] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0042] 2. Edge Computers

[0043] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[0044] 3. Server

[0045] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0046] Program processing explanation

[0047] User Actions

[0048] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, such as when getting into a self-driving car or standing near a smart home device.

[0049] Operation of the terminal (edge ​​computer)

[0050] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server for the user's data and the generated AI model. The edge computer uses a high-speed communication protocol to minimize waiting time during this request.

[0051] Server Operation

[0052] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[0053] Specific examples

[0054] For self-driving cars

[0055] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[0056] For smart homes

[0057] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[0058] This allows users to receive services based on their preferences without having to make complicated settings.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user puts on the device.

[0062] Specific behavior:

[0063] The user powers on the device and it becomes active.

[0064] The device begins broadcasting its unique ID.

[0065] Step 2:

[0066] The terminal (edge ​​computer) receives the device ID.

[0067] Specific behavior:

[0068] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[0069] The terminal detects the device's unique ID and stores the ID in its internal memory.

[0070] Step 3:

[0071] The device sends the ID to the server.

[0072] Specific behavior:

[0073] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[0074] The device sends the request using a secure communication protocol (e.g., HTTPS).

[0075] Step 4:

[0076] The server receives the request and retrieves the data.

[0077] Specific behavior:

[0078] The server receives the request from the terminal and performs authentication and authorization.

[0079] The server searches the database for user information and generated AI models related to the corresponding device ID.

[0080] Step 5:

[0081] The server sends the data back to the device.

[0082] Specific behavior:

[0083] The server organizes the search results and generates response data to be sent back to the terminal.

[0084] The server sends the response data to the terminal using a secure communication protocol.

[0085] Step 6:

[0086] The device analyzes the received data and runs the AI ​​model.

[0087] Specific behavior:

[0088] The device analyzes the data received from the server and extracts the necessary information and AI models.

[0089] The device loads the AI ​​model and sets it up in the execution environment.

[0090] Step 7:

[0091] The terminal provides the service to the user.

[0092] Specific behavior:

[0093] The device uses the acquired AI model to generate services for users in real time.

[0094] For example, in the case of a self-driving car, the device will calculate the optimal driving route and adjust the in-car environment.

[0095] In the case of a smart home, the device adjusts the settings of lighting and entertainment devices.

[0096] This allows users to seamlessly enjoy personalized services.

[0097] Example 1

[0098] 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."

[0099] With conventional technology, users had to manually configure many settings to receive individually optimized services. Furthermore, users had to redo the settings every time they moved or entered a new environment, which was inconvenient. Furthermore, data acquisition and processing took time, making it difficult to provide real-time services.

[0100] 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.

[0101] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, and a means for performing real-time processing using the acquired information and generative AI model by the edge computer to provide the user with personalized services. This eliminates the need for the user to manually configure the settings, allowing the user to always automatically receive the optimal service.

[0102] "User" refers to an individual who carries a device that transmits a specific ID and uses this system.

[0103] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID via wireless communication.

[0104] An "edge computer" refers to an intermediate device that receives an ID transmitted from a device means and communicates with a server.

[0105] "Server" refers to a computer system that manages and provides user-related information and generated AI models based on IDs sent from edge computers.

[0106] "Generative AI model" refers to a data model that uses artificial intelligence technology to provide users with personalized services that are optimized for them.

[0107] "Personalized services" refer to dedicated services provided according to a user's preferences and behavioral history based on the user's specific ID.

[0108] "Real-time processing" refers to processing in which data acquisition and service provision are carried out without delay after the ID is transmitted from the device means.

[0109] "Driving route optimization" refers to the function of calculating and providing the optimal driving route based on the user's driving history and current traffic information.

[0110] "Automatic adjustment of the in-car environment" refers to a function that automatically adjusts the in-car temperature, seat position, audio settings, etc. based on the user's preferences.

[0111] "Smart home devices" refers to various electronic devices used to improve user comfort in a living environment, such as lighting, air conditioning, and entertainment equipment.

[0112] "Secure communication protocol" refers to a communication method for securely exchanging data between device means, edge computers, and servers.

[0113] Detailed description of embodiments of the present invention will be given below. This system provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Basic components of this system include a device, an edge computer, and a server.

[0114] Component Description

[0115] Devices carried by users

[0116] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0117] Edge Computer

[0118] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[0119] server

[0120] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0121] Program processing explanation

[0122] The program's processing is explained in detail below. The hardware used includes Raspberry Pi and Intel NUC, the software includes Azure (registered trademark) ML and AWS (registered trademark) SageMaker, and the database includes MySQL (registered trademark) and MongoDB.

[0123] User Actions

[0124] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, for example, when getting into a self-driving car or performing a specific action in a smart home.

[0125] Operation of the terminal (edge ​​computer)

[0126] The terminal first receives the ID transmitted from the device and then sends a data request to the server based on this ID. Specifically, it sends a secure request using the HTTP protocol, requesting a generative AI model based on the user's driving history data, favorite TV shows, etc.

[0127] Server Operation

[0128] When the server receives the request, it searches the database for the relevant user information and generated AI model, which are then encrypted and sent back to the edge computer using a secure communication protocol (e.g., HTTPS).

[0129] The device receives, decompresses, analyzes, and processes the data in real time to provide users with personalized services, such as optimizing driving routes and automatically adjusting the in-car environment in a self-driving car, or adjusting lighting and entertainment settings in a smart home.

[0130] Specific examples

[0131] For self-driving cars

[0132] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[0133] Example prompt:

[0134] "Create a generative AI model that suggests the optimal driving route based on the user's driving history data and return it to the user with ID 'USER1234'"

[0135] For smart homes

[0136] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[0137] Example prompt:

[0138] "Generative AI models are created to optimize lighting and entertainment settings based on a user's past behavioral history."

[0139] This allows users to receive services based on their preferences without having to make complicated settings.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] The user puts on the device.

[0143] Specific behavior:

[0144] The user wears a device with an RFID tag or NFC chip built in. This device constantly transmits a unique ID for the user. An LED on the device lights up to confirm that the device is being worn.

[0145] Input: When the user wears the device, it starts transmitting its ID.

[0146] Output: The ID to be sent (e.g. USER1234).

[0147] Step 2:

[0148] The terminal (edge ​​computer) receives the ID from the device.

[0149] Specific behavior:

[0150] The edge computer uses an NFC reader or Bluetooth receiver to receive the ID transmitted by the user's device.

[0151] Input: The ID originating from the device.

[0152] Output: The received ID (e.g. USER1234).

[0153] Step 3:

[0154] The device sends a request to the server.

[0155] Specific behavior:

[0156] The terminal generates an HTTP request based on the received ID and sends it to the server using a secure communication protocol (HTTPS).

[0157] Input: The received ID (e.g. USER1234).

[0158] Output: The data request sent to the server.

[0159] Step 4:

[0160] The server retrieves the user's data and sends back a response.

[0161] Specific behavior:

[0162] The server analyzes the received request, searches a database (e.g., MySQL, MongoDB) for the relevant user information and generated AI model, encrypts the search results, and sends them back to the edge computer.

[0163] Input: The data request sent to the server.

[0164] Output: Encrypted user information and generated AI model.

[0165] Step 5:

[0166] The terminal receives the data and processes it.

[0167] Specific behavior:

[0168] The device receives the data returned from the server, decompresses and analyzes it, and then performs real-time processing based on the information obtained, such as optimizing driving routes or automatically adjusting the in-car environment.

[0169] Input: Encrypted user information and generated AI model.

[0170] Output: Parsed data and optimized configuration information.

[0171] Step 6:

[0172] To provide users with the best personalized service.

[0173] Specific behavior:

[0174] The device will then use the analyzed data to provide personalized services, such as adjusting the air conditioning, adjusting the seat position, and playing audio in a self-driving car, as well as adjusting lighting and entertainment devices in a smart home.

[0175] Input: Parsed data and optimized configuration information.

[0176] Output: The personalized service provided to the user (e.g., tailored driving directions or preferences).

[0177] (Application example 1)

[0178] 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."

[0179] Traditional brick-and-mortar stores faced the challenge of being unable to efficiently provide services to individual users. Users were unable to quickly obtain product information based on their preferences and past purchase history, and stores lacked the means to efficiently make personalized suggestions. This resulted in a decline in users' purchasing motivation and had a negative impact on store sales. There were also technical challenges in providing personalized services to a large number of users at once in real time.

[0180] 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.

[0181] In this invention, the server includes a device means for transmitting a specific ID carried by the user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing personalized services to the user using the information and generative AI model acquired by the edge computer, and a means for communicating with the server via the edge computer when the user enters a store and providing product suggestions and special offer information based on the user's past purchase history and preferences in real time. This allows users to receive product suggestions and special discount information based on their preferences and past purchase history in real time simply by entering the store using their specific ID. Stores can also efficiently make personalized suggestions, improving the user experience and increasing sales.

[0182] "Device means" refers to a device carried by a user that has the function of transmitting a specific ID.

[0183] An "edge computer" refers to a proximity-based computing device that receives the ID of a device means and communicates with a server to obtain the necessary information and generative AI models.

[0184] "Server" refers to a computer system that manages information related to users and generative AI models and provides necessary data based on the ID sent from the edge computer.

[0185] A "generative AI model" is a model generated using artificial intelligence to provide personalized services based on a user's past behavior and preferences.

[0186] "Individualized services" refer to the provision of services and product suggestions that take into account the user's preferences and past behavioral history based on the user's specific ID.

[0187] "Means of providing in real time" refers to a method in which an edge computer instantly provides personalized information and services to users based on data obtained from a server.

[0188] "Communication protocol" refers to the rules and regulations used to communicate data between edge computers and servers.

[0189] "Past purchase history" refers to data that records information about products and services that a user has previously purchased.

[0190] "Product Suggestion" refers to providing a list of recommended products and services based on a user's characteristics.

[0191] "Special offer information" refers to special discounts, coupons, and other preferential treatment information provided to users.

[0192] The present invention is a system in which a user carries a device that transmits a specific ID, communicates with a server via an edge computer, and provides personalized services optimized for the user. Specific components for realizing this system include a device means, an edge computer, and a server.

[0193] Device Means

[0194] The device means is a device that has the function of transmitting a user's unique ID using an RFID tag or NFC chip. The device operates on low power and is attached to the user's clothing or accessories. This allows a specific ID to be constantly transmitted through the device carried by the user.

[0195] Edge Computer

[0196] The edge computer has the ability to receive the device ID in real time. The edge computer sends the received ID to the server to obtain information related to the user and the generated AI model. The edge computer uses a high-speed communication protocol (e.g., MQTT) to minimize the latency between receiving and sending data.

[0197] server

[0198] The server manages the information and generated AI model corresponding to the ID sent by the user. When the server receives a request from the edge computer, it searches the database for the corresponding user information and generated AI model and returns them to the edge computer. To ensure security, the server performs authentication and encrypted communication.

[0199] Providing personalized services

[0200] The edge computer uses the information acquired and the generated AI model to provide personalized services to users. When a user enters a physical store, the edge computer communicates with the server, allowing for real-time product suggestions and special offer information based on the user's past purchase history and preferences. Specific examples of services include the following:

[0201] Display of recommended products: When a user enters a store, the edge computer receives the device ID and retrieves a list of recommended products from the server. This allows the recommended products to be displayed on in-store displays and on the user's smartphone.

[0202] Offering special discount coupons: Based on the user's ID, a personalized special discount coupon will be offered. Coupon details will be sent to the user via a smartphone app.

[0203] Product search function: The smartphone app allows customers to check the location of products in the store in real time, and provides guidance on the shortest route when searching for a specific product.

[0204] Hardware and software used

[0205] Hardware: RFID tags, NFC chips, edge computers, servers, smartphones

[0206] Software: Python, Django (server side), MQTT (communication protocol), React Native (smartphone app)

[0207] Prompt Sentence Examples

[0208] Get User Data Prompt: "Return JSON data that captures a user's purchasing history and preferences based on their user ID."

[0209] Product recommendation prompt: "Generate a list of recommended products based on this user data."

[0210] This allows users to receive services based on their preferences, and stores to efficiently make personalized offers.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] The user puts on the device means and enters the store.

[0214] What it does: A user enters a physical store wearing a device, such as an RFID tag or NFC chip, that constantly broadcasts a specific ID.

[0215] Step 2:

[0216] The edge computer receives the ID of the device means.

[0217] Specific operation: An edge computer is installed in the store and receives IDs transmitted from devices in real time. The input is the user's device ID, and the output is the received ID data.

[0218] Step 3:

[0219] The edge computer sends the received ID to the server.

[0220] Specific operation: The edge computer receives the user ID and sends it to the server using a communication protocol such as MQTT. The input is the received ID data, and the output is a request to the server.

[0221] Step 4:

[0222] The server searches for user information and generated AI models corresponding to the ID.

[0223] Specific operation: The server searches the database and obtains the user information (purchase history, preferences, etc.) and generative AI model corresponding to the ID. The input is a request to the server, and the output is the search result of the user information and the generative AI model.

[0224] Step 5:

[0225] The server returns the retrieved data to the edge computer.

[0226] Specific operation: The server returns user information and the generated AI model to the edge computer. The input is the user information and generated AI model from the search results, and the output is the response to the edge computer.

[0227] Step 6:

[0228] The edge computer processes data based on the information acquired and the generated AI model.

[0229] Specific operation: The edge computer analyzes the data received from the server and uses a generative AI model to generate optimal product suggestions and bonus information for the user. The input is the response data from the server, and the output is customized data based on the analysis results.

[0230] Step 7:

[0231] The edge computer provides the generated customization data to the user.

[0232] Specific operation: The edge computer displays the analysis results (recommended product lists and special offer information) on in-store displays or on the user's smartphone. The input is customized data of the analysis results, and the output is displayed on the user interface.

[0233] Step 8:

[0234] Users select and purchase products based on the information provided.

[0235] Specific operation: The user selects a product based on the information displayed on their smartphone or display, and proceeds with the purchase if necessary. The input is information from the user interface, and the output is the user's selection and purchase data.

[0236] This series of processes allows users to receive product suggestions and special offer information based on their preferences in real time, and enables stores to efficiently make personalized suggestions.

[0237] 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.

[0238] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[0239] The system of the present invention provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Furthermore, by combining an emotion engine, the system has the ability to dynamically adjust service content based on the user's emotional state. The basic components of this system include a device, an edge computer, a server, and an emotion engine.

[0240] Component Description

[0241] 1. Devices carried by users

[0242] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0243] 2. Edge Computers

[0244] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. In addition, the edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, thereby recognizing the user's emotional state.

[0245] 3. Server

[0246] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0247] 4. Emotion Engine

[0248] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, allowing the services provided to be dynamically adjusted according to the user's current emotional state.

[0249] Program processing explanation

[0250] User Actions

[0251] The user first puts on their device and enables it to broadcast its ID. Then, as the user moves closer to the edge computer (for example, getting into a self-driving car or standing near a smart home device), the emotion engine recognizes the user's emotions in real time.

[0252] Operation of the terminal (edge ​​computer)

[0253] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server requesting user information and a generative AI model. At the same time, the emotion engine analyzes the user's emotional state, and this data is also processed within the edge computer.

[0254] Server Operation

[0255] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[0256] Specific examples

[0257] For self-driving cars

[0258] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The emotion engine recognizes the user's emotional state; for example, if the user is nervous, the edge computer adjusts the driving style to be safer. The in-car environment is also optimized according to the user's emotional state. For example, it may play relaxing music or adjust the air conditioning temperature.

[0259] For smart homes

[0260] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state and, for example, if the user is tired, it will automatically lower the brightness of the lights or play relaxing music.

[0261] This allows users to seamlessly receive services based on their preferences and emotional state without having to go through complicated settings.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] The user puts on the device.

[0265] Specific behavior:

[0266] The user powers on the device and it becomes active.

[0267] The device begins broadcasting its unique ID.

[0268] Step 2:

[0269] The terminal (edge ​​computer) receives the device ID.

[0270] Specific behavior:

[0271] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[0272] The terminal detects the device's unique ID and stores the ID in its internal memory.

[0273] Step 3:

[0274] The device sends the ID to the server.

[0275] Specific behavior:

[0276] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[0277] The device sends the request using a secure communication protocol (e.g., HTTPS).

[0278] Step 4:

[0279] The server receives the request and retrieves the data.

[0280] Specific behavior:

[0281] The server receives the request from the terminal and performs authentication and authorization.

[0282] The server searches the database for user information and generated AI models related to the corresponding device ID.

[0283] Step 5:

[0284] The server sends the data back to the device.

[0285] Specific behavior:

[0286] The server organizes the search results and generates response data to be sent back to the terminal.

[0287] The server sends the response data to the terminal using a secure communication protocol.

[0288] Step 6:

[0289] The device analyzes the received data and runs the AI ​​model.

[0290] Specific behavior:

[0291] The device analyzes the data received from the server and extracts the necessary information and generative AI model.

[0292] The device loads the AI ​​model and sets it up in the execution environment.

[0293] Step 7:

[0294] The device (emotion engine) recognizes the user's emotions.

[0295] Specific behavior:

[0296] The device's built-in emotion engine collects data using the user's facial expression recognition camera, voice analysis microphone, and biometric sensors.

[0297] The emotion engine analyzes the collected data and recognizes the user's real-time emotional state.

[0298] Step 8:

[0299] The terminal provides the service to the user.

[0300] Specific behavior:

[0301] The device uses the analysis results of the AI ​​model and emotion engine acquired to generate individual services for users in real time.

[0302] For example, in the case of a self-driving car, the device calculates the optimal driving route and adjusts the in-car environment (music selection, air conditioning settings, etc.).

[0303] In the case of a smart home, the device will automatically adjust the brightness of the lights, play relaxing music, and perform other tasks.

[0304] This allows users to seamlessly enjoy personalized services, particularly those tailored to their emotional state.

[0305] Example 2

[0306] 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."

[0307] Conventional personalized service provision systems provide uniform services without considering the user's emotional state, making it difficult to increase true user satisfaction. It is also difficult to adjust services in real time according to the user's behavior and environment. Therefore, a system that can dynamically adjust services based on the user's emotional state is needed.

[0308] 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.

[0309] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with a personalized service using the information and generative AI model acquired by the edge computer, a means for the edge computer to incorporate an emotion engine and to analyze the user's facial expressions, voice, and biometric information to recognize the user's emotional state, and a means for dynamically adjusting the content of the service according to the emotional state, thereby enabling the provision of detailed services based on the user's emotional state.

[0310] "Device means for transmitting a specific ID carried by a user" refers to a device that can be carried by a user and is used to transmit unique identification information, and specific examples include RFID tags and NFC chips.

[0311] An "edge computer" is an advanced processing device that receives IDs and analyzes user information, and is responsible for relaying and analyzing data between the server and user devices.

[0312] "Server" refers to a central processing unit that retrieves user information and generative AI models from a database based on requests received from the edge computer and sends that information to the edge computer.

[0313] "Generative AI models" refer to algorithms or programs that are generated based on a user's past data and prompts and are used to provide personalized services.

[0314] "Emotion engine" refers to a software or hardware device that analyzes a user's facial expressions, voice, and biometric information to recognize their emotional state in real time.

[0315] "Means for analyzing a user's facial expressions, voice, and biometric information" refers to technology that uses cameras, microphones, and biometric sensors to determine a user's emotional state and utilizes that information to provide services.

[0316] "Means for dynamically adjusting service content" refers to a function that changes the content of the service provided in real time according to the user's emotional state, providing the user with the optimal experience.

[0317] The present invention is a system that provides personalized services to users by using a device that transmits a specific ID carried by the user, an edge computer, a server, and an emotion engine. The following describes in detail an embodiment of the present invention.

[0318] First, a user needs a device to carry with them. This device can be an RFID tag or an NFC chip, and transmits a unique ID. The user wears this device on their arm or in their pocket, and the device is constantly transmitting its ID.

[0319] Next is the edge computer. The edge computer has the ability to receive IDs sent from devices and a network interface for communicating with the server. This edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, recognizes the user's emotional state in real time, and provides optimized services to the user based on the generative AI model obtained from the server.

[0320] The server is a central processing unit that manages the generated AI model and related data for each user. It searches for user information corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security, enabling the management of user information and the provision of appropriate services.

[0321] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, and dynamically adjusts the service content based on the user's emotional state.

[0322] Take the case of a self-driving car as a specific example. When a user wears their device and gets into the self-driving car, the edge computer receives the device's ID and sends a request to the server. The server then sends back to the edge computer a generative AI model based on the user's driving history and preferences. The emotion engine analyzes the user's emotional state in real time; for example, if the user is nervous, the edge computer will adjust the driving style to be safer. The in-car environment is also optimized according to the user's emotional state, playing relaxing music or adjusting the air conditioning temperature.

[0323] In the case of a smart home, when a user wears a device and stands near the smart home device, the edge computer receives the device's ID and sends a request to the server. The server then sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state, and if the user is tired, for example, it will automatically lower the brightness of the lights or play relaxing music.

[0324] An example of a prompt sentence might be:

[0325] Example prompt to adjust driving style based on the user's emotional state when getting into a self-driving car:

[0326] "If the user is nervous, we want to adjust their driving style to be safer and play relaxing music."

[0327] Example prompt to adjust the environment based on the user's emotional state when near a smart home device:

[0328] "If the user is tired, we want to lower the lighting brightness and play relaxing music."

[0329] This allows users to seamlessly receive optimal services based on their preferences and emotional state without any special operations.

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1: User puts on the device

[0332] A user wears their device (e.g., an RFID tag or NFC chip) and configures it to transmit a specific ID.

[0333] Specific actions

[0334] A user wears an RFID tag as a watch and turns on the device, or carries an NFC chip.

[0335] input

[0336] A user-specific ID set on the device

[0337] output

[0338] The ID will be in a calling state.

[0339] Step 2: The user moves closer to the edge computer

[0340] The user moves within the communication range of the edge computer.

[0341] Specific actions

[0342] A user opens the door of a self-driving car and gets in, or enters the living room of a smart home.

[0343] input

[0344] User location information

[0345] output

[0346] The user's device ID comes within the communication range of the edge computer.

[0347] Step 3: Edge computer receives ID

[0348] The terminal's edge computer receives the ID sent from the device.

[0349] Specific actions

[0350] The ID receiving module in the edge computer captures the signal and decodes the ID.

[0351] input

[0352] Unique ID of the user originating from the device

[0353] output

[0354] The received user's ID data.

[0355] Step 4: The edge computer sends a request to the server

[0356] The device's edge computer sends a request to the server based on the received ID, requesting user information and a generated AI model.

[0357] Specific actions

[0358] Create request data including user ID and send it to the server along with authentication information using HTTPS protocol.

[0359] input

[0360] Received user ID data

[0361] output

[0362] The request data for the server.

[0363] Step 5: The server retrieves and sends the user information

[0364] The server analyzes the received request, searches the database for the relevant user information and generated AI model, and sends it back to the edge computer.

[0365] Specific actions

[0366] A query using the user ID as a key is executed against the database, and the obtained user information and generated AI model are packaged in a secure format and sent to the edge computer.

[0367] input

[0368] Request data for the server (including user ID)

[0369] output

[0370] Applicable user information and generated AI models.

[0371] Step 6: The edge computer analyzes the received data

[0372] The edge computer on the terminal receives the response from the server, extracts the necessary data, and analyzes it.

[0373] Specific actions

[0374] Analyzes the received data, loads the user profile and generated AI model into memory, and initializes necessary settings and parameters.

[0375] input

[0376] User information and generated AI model sent from the server

[0377] output

[0378] User profile and AI model loaded into memory.

[0379] Step 7: The edge computer uses the emotion engine to analyze the user's emotional state.

[0380] The emotion engine installed in the device's edge computer analyzes the user's facial expressions, voice, and biometric information to determine their emotional state in real time.

[0381] Specific actions

[0382] A facial expression analysis camera captures the user's facial expressions and uses image processing to recognize emotions, a voice analysis microphone analyzes the tone of the user's voice, and processes data from biometric sensors (e.g., heart rate monitors).

[0383] input

[0384] User's facial expressions, voice, and biometric information

[0385] output

[0386] User emotional state data.

[0387] Step 8: Provide optimized services to users

[0388] The device's edge computer provides personalized services based on the user's emotional state and profile information.

[0389] Specific actions

[0390] In a self-driving car, it adjusts the driving mode to help the user relax, and in a smart home, it changes the lighting and music settings.

[0391] input

[0392] User emotional state data, profile information, generative AI model

[0393] output

[0394] Implementing personalized services (e.g. adjusting driving modes, changing lighting settings).

[0395] (Application example 2)

[0396] 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."

[0397] In today's world, while services that utilize personal information and emotional data are on the rise, there is also a rapid demand for security systems to ensure user safety. However, current security systems rarely dynamically adjust based on the user's real-time emotional state, making it difficult to immediately sense a user's emotions and stress and take appropriate action. Furthermore, security systems often require users to wear specialized devices to perform emotion analysis tailored to each user's individual situation, which can be cumbersome or require additional specialized equipment. Therefore, there is a need for a system that can perform emotion analysis using a commonly carried device and provide optimal security services in real time.

[0398] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0399] In this invention, the server includes a device means for transmitting a specific ID carried by a user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with personalized services using the information and generative AI model acquired by the edge computer, a means for the edge computer to be installed on a smartphone and for an emotion engine to analyze the user's emotional state in real time using the smartphone's camera and microphone, and a means for dynamically adjusting the security level based on the user's emotional state and issuing an alert when necessary, thereby making it possible to provide optimal security services based on the user's real-time emotional state.

[0400] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID.

[0401] An "edge computer" is a computer that receives the ID of a device means, communicates with a server to obtain information and generative AI models, and performs processing to provide personalized services to users.

[0402] The "server" is a system that manages user-related data and generated AI models based on the ID sent from the edge computer, and returns the necessary information while ensuring security.

[0403] A "generative AI model" is an artificial intelligence model that is generated based on user-specific data and is used to predict and respond to user behavior and preferences.

[0404] The "emotion engine" is a system that uses cameras, microphones, biometric sensors, etc. to analyze the user's facial expressions, voice, and biometric information, and recognizes the user's emotional state in real time.

[0405] A "smartphone" is a portable information device that is equipped with a camera, microphone, and various sensors and can have an emotion engine installed.

[0406] "Real-time analytics" is the process of instantly processing collected data to instantly determine a user's emotional or other state.

[0407] "Security level" indicates the degree of various responses and actions that the system provides to protect the safety of users.

[0408] "Alert sending" is a function that immediately sends notifications and warnings when it is determined that a user is in danger.

[0409] Detailed description of embodiments of the present invention will be given below. The overall configuration of the system includes a device carried by a user that has the function of transmitting a specific ID, an edge computer, a server, and an emotion engine. As a specific example, a real-time security system using a smartphone will be described.

[0410] System configuration

[0411] 1. Device Means

[0412] A user carries a device that transmits a specific ID, such as a smartwatch or smart card, that transmits the user's unique ID.

[0413] 2. Edge Computers

[0414] The edge computer is installed on a smartphone. The smartphone is equipped with a camera, microphone, and biometric sensors, and an emotion engine is installed. This engine is used to analyze the user's emotional state in real time. The analyzed emotion data is processed by the edge computer, and communication with the server is performed as necessary.

[0415] 3. Server

[0416] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server is built using cloud services such as Google (registered trademark) Cloud Platform (GCP) and Amazon Web Services (AWS). Communications are encrypted using secure protocols such as TLS.

[0417] 4. Emotion Engine

[0418] The emotion engine analyzes the user's facial expressions, voice, and biometric information. For example, it can use libraries such as Microsoft's Emotion API. This allows the engine to recognize the user's emotional state in real time and dynamically adjust the services provided according to the user's current emotional state.

[0419] Specific example of system operation

[0420] For real-time security monitoring systems

[0421] User Actions

[0422] First, the user wears a smartwatch or similar device and transmits an ID, which is received by a smartphone.

[0423] Edge computer operation

[0424] When the smartphone receives a specific ID, it sends a request to the server based on that ID to retrieve a generative AI model related to the user, while the emotion engine analyzes the user's emotional state using the smartphone's camera, microphone, and biometric sensors.

[0425] Server Operation

[0426] When the server receives a request from the edge computer, it searches the database for relevant user information and the generated AI model and sends it back to the edge computer. The server ensures security by using encrypted communications.

[0427] Actions based on the user's emotional state

[0428] If the emotion engine detects a user's emotional state (e.g., stress or fear), the edge computer automatically adjusts the security level and issues alerts as needed, which are then sent to emergency contacts using APIs such as Twilio.

[0429] Prompt Sentence Examples

[0430] "A system that analyzes a user's emotional state in real time and issues an alert if a specific emotion is detected."

[0431] This system allows users to enjoy real-time security services based on their emotional state, enabling them to take appropriate action even in sudden stress or fear situations.

[0432] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0433] Step 1:

[0434] A user wears a device such as a smart watch or smart card that transmits a specific ID. The ID transmitted from this device is verified to be unique to the user.

[0435] Input: A device with a user-specific ID

[0436] Output: A specific ID is sent to your smartphone.

[0437] Specific operation: The user wears a smartwatch, which transmits its ID to those around it.

[0438] Step 2:

[0439] The smartphone (edge ​​computer) receives the ID sent from the device. After receiving the ID, it sends a request for a generative AI model to the server based on that ID.

[0440] Input: Device ID

[0441] Output: Generated AI model request to the server

[0442] Specific operation: The smartphone receives the ID via Bluetooth or NFC and sends a request including that ID to the server.

[0443] Step 3:

[0444] The server receives a request from the edge computer, searches for the user data and generated AI model corresponding to the ID, and once the search is complete, it returns the results to the edge computer.

[0445] Input: A request containing an ID

[0446] Output: User data and generative AI model

[0447] How it works: The server searches the database for relevant information and sends the results, including the generative AI model, back to the edge computer.

[0448] Step 4:

[0449] The edge computer prepares to provide personalized services to users based on the generated AI model and user information received from the server, while the smartphone's emotion engine analyzes the user's facial expressions, voice, and biometric information in real time.

[0450] Input: Generative AI model and user information

[0451] Output: Preparation for providing individual services

[0452] How it works: Your smartphone collects data using the camera, microphone, and biometric sensors, which the emotion engine analyzes.

[0453] Step 5:

[0454] The emotion engine analyzes the user's emotional state in real time and dynamically adjusts the security level based on the results. If the user is in a state of stress or fear, the edge computer will send an alert.

[0455] Input: Sentiment analysis data

[0456] Output: Dynamically adjusted security levels and alerts

[0457] Specific operation: The emotion engine performs facial expression and voice analysis, and if an abnormality is detected, an alert is sent to an emergency contact using the Twilio API, etc.

[0458] In this way, a system can be realized that monitors the emotional state of a user in real time and dynamically provides necessary security measures.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] [Second embodiment]

[0463] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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).

[0469] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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."

[0475] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[0476] This system allows users to carry a device (hereinafter referred to as "device") that transmits a specific ID, and by having this device communicate with a server via an edge computer, the system provides users with personalized services that are optimized for them. The basic components of this system include the device, the edge computer, and the server.

[0477] Component Description

[0478] 1. Devices carried by users

[0479] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0480] 2. Edge Computers

[0481] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[0482] 3. Server

[0483] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0484] Program processing explanation

[0485] User Actions

[0486] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, such as when getting into a self-driving car or standing near a smart home device.

[0487] Operation of the terminal (edge ​​computer)

[0488] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server for the user's data and the generated AI model. The edge computer uses a high-speed communication protocol to minimize waiting time during this request.

[0489] Server Operation

[0490] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[0491] Specific examples

[0492] For self-driving cars

[0493] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[0494] For smart homes

[0495] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[0496] This allows users to receive services based on their preferences without having to make complicated settings.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] The user puts on the device.

[0500] Specific behavior:

[0501] The user powers on the device and it becomes active.

[0502] The device begins broadcasting its unique ID.

[0503] Step 2:

[0504] The terminal (edge ​​computer) receives the device ID.

[0505] Specific behavior:

[0506] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[0507] The terminal detects the device's unique ID and stores the ID in its internal memory.

[0508] Step 3:

[0509] The device sends the ID to the server.

[0510] Specific behavior:

[0511] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[0512] The device sends the request using a secure communication protocol (e.g., HTTPS).

[0513] Step 4:

[0514] The server receives the request and retrieves the data.

[0515] Specific behavior:

[0516] The server receives the request from the terminal and performs authentication and authorization.

[0517] The server searches the database for user information and generated AI models related to the corresponding device ID.

[0518] Step 5:

[0519] The server sends the data back to the device.

[0520] Specific behavior:

[0521] The server organizes the search results and generates response data to be sent back to the terminal.

[0522] The server sends the response data to the terminal using a secure communication protocol.

[0523] Step 6:

[0524] The device analyzes the received data and runs the AI ​​model.

[0525] Specific behavior:

[0526] The device analyzes the data received from the server and extracts the necessary information and AI models.

[0527] The device loads the AI ​​model and sets it up in the execution environment.

[0528] Step 7:

[0529] The terminal provides the service to the user.

[0530] Specific behavior:

[0531] The device uses the acquired AI model to generate services for users in real time.

[0532] For example, in the case of a self-driving car, the device will calculate the optimal driving route and adjust the in-car environment.

[0533] In the case of a smart home, the device adjusts the settings of lighting and entertainment devices.

[0534] This allows users to seamlessly enjoy personalized services.

[0535] Example 1

[0536] 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."

[0537] With conventional technology, users had to manually configure many settings to receive individually optimized services. Furthermore, users had to redo the settings every time they moved or entered a new environment, which was inconvenient. Furthermore, data acquisition and processing took time, making it difficult to provide real-time services.

[0538] 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.

[0539] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, and a means for performing real-time processing using the acquired information and generative AI model by the edge computer to provide the user with personalized services. This eliminates the need for the user to manually configure the settings, allowing the user to always automatically receive the optimal service.

[0540] "User" refers to an individual who carries a device that transmits a specific ID and uses this system.

[0541] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID via wireless communication.

[0542] An "edge computer" refers to an intermediate device that receives an ID transmitted from a device means and communicates with a server.

[0543] "Server" refers to a computer system that manages and provides user-related information and generated AI models based on IDs sent from edge computers.

[0544] "Generative AI model" refers to a data model that uses artificial intelligence technology to provide users with personalized services that are optimized for them.

[0545] "Personalized services" refer to dedicated services provided according to a user's preferences and behavioral history based on the user's specific ID.

[0546] "Real-time processing" refers to processing in which data acquisition and service provision are carried out without delay after the ID is transmitted from the device means.

[0547] "Driving route optimization" refers to the function of calculating and providing the optimal driving route based on the user's driving history and current traffic information.

[0548] "Automatic adjustment of the in-car environment" refers to a function that automatically adjusts the in-car temperature, seat position, audio settings, etc. based on the user's preferences.

[0549] "Smart home devices" refers to various electronic devices used to improve user comfort in a living environment, such as lighting, air conditioning, and entertainment equipment.

[0550] "Secure communication protocol" refers to a communication method for securely exchanging data between device means, edge computers, and servers.

[0551] Detailed description of embodiments of the present invention will be given below. This system provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Basic components of this system include a device, an edge computer, and a server.

[0552] Component Description

[0553] Devices carried by users

[0554] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0555] Edge Computer

[0556] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[0557] server

[0558] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0559] Program processing explanation

[0560] The program's processing is explained in detail below. The hardware used is Raspberry Pi and Intel NUC, the software is Azure ML and AWS SageMaker, and the database is MySQL and MongoDB.

[0561] User Actions

[0562] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, for example, when getting into a self-driving car or performing a specific action in a smart home.

[0563] Operation of the terminal (edge ​​computer)

[0564] The terminal first receives the ID transmitted from the device and then sends a data request to the server based on this ID. Specifically, it sends a secure request using the HTTP protocol, requesting a generative AI model based on the user's driving history data, favorite TV shows, etc.

[0565] Server Operation

[0566] When the server receives the request, it searches the database for the relevant user information and generated AI model, which are then encrypted and sent back to the edge computer using a secure communication protocol (e.g., HTTPS).

[0567] The device receives, decompresses, analyzes, and processes the data in real time to provide users with personalized services, such as optimizing driving routes and automatically adjusting the in-car environment in a self-driving car, or adjusting lighting and entertainment settings in a smart home.

[0568] Specific examples

[0569] For self-driving cars

[0570] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[0571] Example prompt:

[0572] "Create a generative AI model that suggests the optimal driving route based on the user's driving history data and return it to the user with ID 'USER1234'"

[0573] For smart homes

[0574] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[0575] Example prompt:

[0576] "Generative AI models are created to optimize lighting and entertainment settings based on a user's past behavioral history."

[0577] This allows users to receive services based on their preferences without having to make complicated settings.

[0578] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0579] Step 1:

[0580] The user puts on the device.

[0581] Specific behavior:

[0582] The user wears a device with an RFID tag or NFC chip built in. This device constantly transmits a unique ID for the user. An LED on the device lights up to confirm that the device is being worn.

[0583] Input: When the user wears the device, it starts transmitting its ID.

[0584] Output: The ID to be sent (e.g. USER1234).

[0585] Step 2:

[0586] The terminal (edge ​​computer) receives the ID from the device.

[0587] Specific behavior:

[0588] The edge computer uses an NFC reader or Bluetooth receiver to receive the ID transmitted by the user's device.

[0589] Input: The ID originating from the device.

[0590] Output: The received ID (e.g. USER1234).

[0591] Step 3:

[0592] The device sends a request to the server.

[0593] Specific behavior:

[0594] The terminal generates an HTTP request based on the received ID and sends it to the server using a secure communication protocol (HTTPS).

[0595] Input: The received ID (e.g. USER1234).

[0596] Output: The data request sent to the server.

[0597] Step 4:

[0598] The server retrieves the user's data and sends back a response.

[0599] Specific behavior:

[0600] The server analyzes the received request, searches a database (e.g., MySQL, MongoDB) for the relevant user information and generated AI model, encrypts the search results, and sends them back to the edge computer.

[0601] Input: The data request sent to the server.

[0602] Output: Encrypted user information and generated AI model.

[0603] Step 5:

[0604] The terminal receives the data and processes it.

[0605] Specific behavior:

[0606] The device receives the data returned from the server, decompresses and analyzes it, and then performs real-time processing based on the information obtained, such as optimizing driving routes or automatically adjusting the in-car environment.

[0607] Input: Encrypted user information and generated AI model.

[0608] Output: Parsed data and optimized configuration information.

[0609] Step 6:

[0610] To provide users with the best personalized service.

[0611] Specific behavior:

[0612] The device will then use the analyzed data to provide personalized services, such as adjusting the air conditioning, adjusting the seat position, and playing audio in a self-driving car, as well as adjusting lighting and entertainment devices in a smart home.

[0613] Input: Parsed data and optimized configuration information.

[0614] Output: The personalized service provided to the user (e.g., tailored driving directions or preferences).

[0615] (Application example 1)

[0616] 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."

[0617] Traditional brick-and-mortar stores faced the challenge of being unable to efficiently provide services to individual users. Users were unable to quickly obtain product information based on their preferences and past purchase history, and stores lacked the means to efficiently make personalized suggestions. This resulted in a decline in users' purchasing motivation and had a negative impact on store sales. There were also technical challenges in providing personalized services to a large number of users at once in real time.

[0618] 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.

[0619] In this invention, the server includes a device means for transmitting a specific ID carried by the user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing personalized services to the user using the information and generative AI model acquired by the edge computer, and a means for communicating with the server via the edge computer when the user enters a store and providing product suggestions and special offer information based on the user's past purchase history and preferences in real time. This allows users to receive product suggestions and special discount information based on their preferences and past purchase history in real time simply by entering the store using their specific ID. Stores can also efficiently make personalized suggestions, improving the user experience and increasing sales.

[0620] "Device means" refers to a device carried by a user that has the function of transmitting a specific ID.

[0621] An "edge computer" refers to a proximity-based computing device that receives the ID of a device means and communicates with a server to obtain the necessary information and generative AI models.

[0622] "Server" refers to a computer system that manages information related to users and generative AI models and provides necessary data based on the ID sent from the edge computer.

[0623] A "generative AI model" is a model generated using artificial intelligence to provide personalized services based on a user's past behavior and preferences.

[0624] "Individualized services" refer to the provision of services and product suggestions that take into account the user's preferences and past behavioral history based on the user's specific ID.

[0625] "Means of providing in real time" refers to a method in which an edge computer instantly provides personalized information and services to users based on data obtained from a server.

[0626] "Communication protocol" refers to the rules and regulations used to communicate data between edge computers and servers.

[0627] "Past purchase history" refers to data that records information about products and services that a user has previously purchased.

[0628] "Product Suggestion" refers to providing a list of recommended products and services based on a user's characteristics.

[0629] "Special offer information" refers to special discounts, coupons, and other preferential treatment information provided to users.

[0630] The present invention is a system in which a user carries a device that transmits a specific ID, communicates with a server via an edge computer, and provides personalized services optimized for the user. Specific components for realizing this system include a device means, an edge computer, and a server.

[0631] Device Means

[0632] The device means is a device that has the function of transmitting a user's unique ID using an RFID tag or NFC chip. The device operates on low power and is attached to the user's clothing or accessories. This allows a specific ID to be constantly transmitted through the device carried by the user.

[0633] Edge Computer

[0634] The edge computer has the ability to receive the device ID in real time. The edge computer sends the received ID to the server to obtain information related to the user and the generated AI model. The edge computer uses a high-speed communication protocol (e.g., MQTT) to minimize the latency between receiving and sending data.

[0635] server

[0636] The server manages the information and generated AI model corresponding to the ID sent by the user. When the server receives a request from the edge computer, it searches the database for the corresponding user information and generated AI model and returns them to the edge computer. To ensure security, the server performs authentication and encrypted communication.

[0637] Providing personalized services

[0638] The edge computer uses the information acquired and the generated AI model to provide personalized services to users. When a user enters a physical store, the edge computer communicates with the server, allowing for real-time product suggestions and special offer information based on the user's past purchase history and preferences. Specific examples of services include the following:

[0639] Display of recommended products: When a user enters a store, the edge computer receives the device ID and retrieves a list of recommended products from the server. This allows the recommended products to be displayed on in-store displays and on the user's smartphone.

[0640] Offering special discount coupons: Based on the user's ID, a personalized special discount coupon will be offered. Coupon details will be sent to the user via a smartphone app.

[0641] Product search function: The smartphone app allows customers to check the location of products in the store in real time, and provides guidance on the shortest route when searching for a specific product.

[0642] Hardware and software used

[0643] Hardware: RFID tags, NFC chips, edge computers, servers, smartphones

[0644] Software: Python, Django (server side), MQTT (communication protocol), React Native (smartphone app)

[0645] Prompt Sentence Examples

[0646] Get User Data Prompt: "Return JSON data that captures a user's purchasing history and preferences based on their user ID."

[0647] Product recommendation prompt: "Generate a list of recommended products based on this user data."

[0648] This allows users to receive services based on their preferences, and stores to efficiently make personalized offers.

[0649] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0650] Step 1:

[0651] The user puts on the device means and enters the store.

[0652] What it does: A user enters a physical store wearing a device, such as an RFID tag or NFC chip, that constantly broadcasts a specific ID.

[0653] Step 2:

[0654] The edge computer receives the ID of the device means.

[0655] Specific operation: An edge computer is installed in the store and receives IDs transmitted from devices in real time. The input is the user's device ID, and the output is the received ID data.

[0656] Step 3:

[0657] The edge computer sends the received ID to the server.

[0658] Specific operation: The edge computer receives the user ID and sends it to the server using a communication protocol such as MQTT. The input is the received ID data, and the output is a request to the server.

[0659] Step 4:

[0660] The server searches for user information and generated AI models corresponding to the ID.

[0661] Specific operation: The server searches the database and obtains the user information (purchase history, preferences, etc.) and generative AI model corresponding to the ID. The input is a request to the server, and the output is the search result of the user information and the generative AI model.

[0662] Step 5:

[0663] The server returns the retrieved data to the edge computer.

[0664] Specific operation: The server returns user information and the generated AI model to the edge computer. The input is the user information and generated AI model from the search results, and the output is the response to the edge computer.

[0665] Step 6:

[0666] The edge computer processes data based on the information acquired and the generated AI model.

[0667] Specific operation: The edge computer analyzes the data received from the server and uses a generative AI model to generate optimal product suggestions and bonus information for the user. The input is the response data from the server, and the output is customized data based on the analysis results.

[0668] Step 7:

[0669] The edge computer provides the generated customization data to the user.

[0670] Specific operation: The edge computer displays the analysis results (recommended product lists and special offer information) on in-store displays or on the user's smartphone. The input is customized data of the analysis results, and the output is displayed on the user interface.

[0671] Step 8:

[0672] Users select and purchase products based on the information provided.

[0673] Specific operation: The user selects a product based on the information displayed on their smartphone or display, and proceeds with the purchase if necessary. The input is information from the user interface, and the output is the user's selection and purchase data.

[0674] This series of processes allows users to receive product suggestions and special offer information based on their preferences in real time, and enables stores to efficiently make personalized suggestions.

[0675] 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.

[0676] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[0677] The system of the present invention provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Furthermore, by combining an emotion engine, the system has the ability to dynamically adjust service content based on the user's emotional state. The basic components of this system include a device, an edge computer, a server, and an emotion engine.

[0678] Component Description

[0679] 1. Devices carried by users

[0680] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0681] 2. Edge Computers

[0682] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. In addition, the edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, thereby recognizing the user's emotional state.

[0683] 3. Server

[0684] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0685] 4. Emotion Engine

[0686] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, allowing the services provided to be dynamically adjusted according to the user's current emotional state.

[0687] Program processing explanation

[0688] User Actions

[0689] The user first puts on their device and enables it to broadcast its ID. Then, as the user moves closer to the edge computer (for example, getting into a self-driving car or standing near a smart home device), the emotion engine recognizes the user's emotions in real time.

[0690] Operation of the terminal (edge ​​computer)

[0691] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server requesting user information and a generative AI model. At the same time, the emotion engine analyzes the user's emotional state, and this data is also processed within the edge computer.

[0692] Server Operation

[0693] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[0694] Specific examples

[0695] For self-driving cars

[0696] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The emotion engine recognizes the user's emotional state; for example, if the user is nervous, the edge computer adjusts the driving style to be safer. The in-car environment is also optimized according to the user's emotional state. For example, it may play relaxing music or adjust the air conditioning temperature.

[0697] For smart homes

[0698] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state and, for example, if the user is tired, it will automatically lower the brightness of the lights or play relaxing music.

[0699] This allows users to seamlessly receive services based on their preferences and emotional state without having to go through complicated settings.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The user puts on the device.

[0703] Specific behavior:

[0704] The user powers on the device and it becomes active.

[0705] The device begins broadcasting its unique ID.

[0706] Step 2:

[0707] The terminal (edge ​​computer) receives the device ID.

[0708] Specific behavior:

[0709] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[0710] The terminal detects the device's unique ID and stores the ID in its internal memory.

[0711] Step 3:

[0712] The device sends the ID to the server.

[0713] Specific behavior:

[0714] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[0715] The device sends the request using a secure communication protocol (e.g., HTTPS).

[0716] Step 4:

[0717] The server receives the request and retrieves the data.

[0718] Specific behavior:

[0719] The server receives the request from the terminal and performs authentication and authorization.

[0720] The server searches the database for user information and generated AI models related to the corresponding device ID.

[0721] Step 5:

[0722] The server sends the data back to the device.

[0723] Specific behavior:

[0724] The server organizes the search results and generates response data to be sent back to the terminal.

[0725] The server sends the response data to the terminal using a secure communication protocol.

[0726] Step 6:

[0727] The device analyzes the received data and runs the AI ​​model.

[0728] Specific behavior:

[0729] The device analyzes the data received from the server and extracts the necessary information and generative AI model.

[0730] The device loads the AI ​​model and sets it up in the execution environment.

[0731] Step 7:

[0732] The device (emotion engine) recognizes the user's emotions.

[0733] Specific behavior:

[0734] The device's built-in emotion engine collects data using the user's facial expression recognition camera, voice analysis microphone, and biometric sensors.

[0735] The emotion engine analyzes the collected data and recognizes the user's real-time emotional state.

[0736] Step 8:

[0737] The terminal provides the service to the user.

[0738] Specific behavior:

[0739] The device uses the analysis results of the AI ​​model and emotion engine acquired to generate individual services for users in real time.

[0740] For example, in the case of a self-driving car, the device calculates the optimal driving route and adjusts the in-car environment (music selection, air conditioning settings, etc.).

[0741] In the case of a smart home, the device will automatically adjust the brightness of the lights, play relaxing music, and perform other tasks.

[0742] This allows users to seamlessly enjoy personalized services, particularly those tailored to their emotional state.

[0743] Example 2

[0744] 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."

[0745] Conventional personalized service provision systems provide uniform services without considering the user's emotional state, making it difficult to increase true user satisfaction. It is also difficult to adjust services in real time according to the user's behavior and environment. Therefore, a system that can dynamically adjust services based on the user's emotional state is needed.

[0746] 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.

[0747] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with a personalized service using the information and generative AI model acquired by the edge computer, a means for the edge computer to incorporate an emotion engine and to analyze the user's facial expressions, voice, and biometric information to recognize the user's emotional state, and a means for dynamically adjusting the content of the service according to the emotional state, thereby enabling the provision of detailed services based on the user's emotional state.

[0748] "Device means for transmitting a specific ID carried by a user" refers to a device that can be carried by a user and is used to transmit unique identification information, and specific examples include RFID tags and NFC chips.

[0749] An "edge computer" is an advanced processing device that receives IDs and analyzes user information, and is responsible for relaying and analyzing data between the server and user devices.

[0750] "Server" refers to a central processing unit that retrieves user information and generative AI models from a database based on requests received from the edge computer and sends that information to the edge computer.

[0751] "Generative AI models" refer to algorithms or programs that are generated based on a user's past data and prompts and are used to provide personalized services.

[0752] "Emotion engine" refers to a software or hardware device that analyzes a user's facial expressions, voice, and biometric information to recognize their emotional state in real time.

[0753] "Means for analyzing a user's facial expressions, voice, and biometric information" refers to technology that uses cameras, microphones, and biometric sensors to determine a user's emotional state and utilizes that information to provide services.

[0754] "Means for dynamically adjusting service content" refers to a function that changes the content of the service provided in real time according to the user's emotional state, providing the user with the optimal experience.

[0755] The present invention is a system that provides personalized services to users by using a device that transmits a specific ID carried by the user, an edge computer, a server, and an emotion engine. The following describes in detail an embodiment of the present invention.

[0756] First, a user needs a device to carry with them. This device can be an RFID tag or an NFC chip, and transmits a unique ID. The user wears this device on their arm or in their pocket, and the device is constantly transmitting its ID.

[0757] Next is the edge computer. The edge computer has the ability to receive IDs sent from devices and a network interface for communicating with the server. This edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, recognizes the user's emotional state in real time, and provides optimized services to the user based on the generative AI model obtained from the server.

[0758] The server is a central processing unit that manages the generated AI model and related data for each user. It searches for user information corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security, enabling the management of user information and the provision of appropriate services.

[0759] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, and dynamically adjusts the service content based on the user's emotional state.

[0760] Take the case of a self-driving car as a specific example. When a user wears their device and gets into the self-driving car, the edge computer receives the device's ID and sends a request to the server. The server then sends back to the edge computer a generative AI model based on the user's driving history and preferences. The emotion engine analyzes the user's emotional state in real time; for example, if the user is nervous, the edge computer will adjust the driving style to be safer. The in-car environment is also optimized according to the user's emotional state, playing relaxing music or adjusting the air conditioning temperature.

[0761] In the case of a smart home, when a user wears a device and stands near the smart home device, the edge computer receives the device's ID and sends a request to the server. The server then sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state, and if the user is tired, for example, it will automatically lower the brightness of the lights or play relaxing music.

[0762] An example of a prompt sentence might be:

[0763] Example prompt to adjust driving style based on the user's emotional state when getting into a self-driving car:

[0764] "If the user is nervous, we want to adjust their driving style to be safer and play relaxing music."

[0765] Example prompt to adjust the environment based on the user's emotional state when near a smart home device:

[0766] "If the user is tired, we want to lower the lighting brightness and play relaxing music."

[0767] This allows users to seamlessly receive optimal services based on their preferences and emotional state without any special operations.

[0768] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0769] Step 1: User puts on the device

[0770] A user wears their device (e.g., an RFID tag or NFC chip) and configures it to transmit a specific ID.

[0771] Specific actions

[0772] A user wears an RFID tag as a watch and turns on the device, or carries an NFC chip.

[0773] input

[0774] A user-specific ID set on the device

[0775] output

[0776] The ID will be in a calling state.

[0777] Step 2: The user moves closer to the edge computer

[0778] The user moves within the communication range of the edge computer.

[0779] Specific actions

[0780] A user opens the door of a self-driving car and gets in, or enters the living room of a smart home.

[0781] input

[0782] User location information

[0783] output

[0784] The user's device ID comes within the communication range of the edge computer.

[0785] Step 3: Edge computer receives ID

[0786] The terminal's edge computer receives the ID sent from the device.

[0787] Specific actions

[0788] The ID receiving module in the edge computer captures the signal and decodes the ID.

[0789] input

[0790] Unique ID of the user originating from the device

[0791] output

[0792] The received user's ID data.

[0793] Step 4: The edge computer sends a request to the server

[0794] The device's edge computer sends a request to the server based on the received ID, requesting user information and a generated AI model.

[0795] Specific actions

[0796] Create request data including user ID and send it to the server along with authentication information using HTTPS protocol.

[0797] input

[0798] Received user ID data

[0799] output

[0800] The request data for the server.

[0801] Step 5: The server retrieves and sends the user information

[0802] The server analyzes the received request, searches the database for the relevant user information and generated AI model, and sends it back to the edge computer.

[0803] Specific actions

[0804] A query using the user ID as a key is executed against the database, and the obtained user information and generated AI model are packaged in a secure format and sent to the edge computer.

[0805] input

[0806] Request data for the server (including user ID)

[0807] output

[0808] Applicable user information and generated AI models.

[0809] Step 6: The edge computer analyzes the received data

[0810] The edge computer on the terminal receives the response from the server, extracts the necessary data, and analyzes it.

[0811] Specific actions

[0812] Analyzes the received data, loads the user profile and generated AI model into memory, and initializes necessary settings and parameters.

[0813] input

[0814] User information and generated AI model sent from the server

[0815] output

[0816] User profile and AI model loaded into memory.

[0817] Step 7: The edge computer uses the emotion engine to analyze the user's emotional state.

[0818] The emotion engine installed in the device's edge computer analyzes the user's facial expressions, voice, and biometric information to determine their emotional state in real time.

[0819] Specific actions

[0820] A facial expression analysis camera captures the user's facial expressions and uses image processing to recognize emotions, a voice analysis microphone analyzes the tone of the user's voice, and processes data from biometric sensors (e.g., heart rate monitors).

[0821] input

[0822] User's facial expressions, voice, and biometric information

[0823] output

[0824] User emotional state data.

[0825] Step 8: Provide optimized services to users

[0826] The device's edge computer provides personalized services based on the user's emotional state and profile information.

[0827] Specific actions

[0828] In a self-driving car, it adjusts the driving mode to help the user relax, and in a smart home, it changes the lighting and music settings.

[0829] input

[0830] User emotional state data, profile information, generative AI model

[0831] output

[0832] Implementing personalized services (e.g. adjusting driving modes, changing lighting settings).

[0833] (Application example 2)

[0834] 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."

[0835] In today's world, while services that utilize personal information and emotional data are on the rise, there is also a rapid demand for security systems to ensure user safety. However, current security systems rarely dynamically adjust based on the user's real-time emotional state, making it difficult to immediately sense a user's emotions and stress and take appropriate action. Furthermore, security systems often require users to wear specialized devices to perform emotion analysis tailored to each user's individual situation, which can be cumbersome or require additional specialized equipment. Therefore, there is a need for a system that can perform emotion analysis using a commonly carried device and provide optimal security services in real time.

[0836] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0837] In this invention, the server includes a device means for transmitting a specific ID carried by a user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with personalized services using the information and generative AI model acquired by the edge computer, a means for the edge computer to be installed on a smartphone and for an emotion engine to analyze the user's emotional state in real time using the smartphone's camera and microphone, and a means for dynamically adjusting the security level based on the user's emotional state and issuing an alert when necessary, thereby making it possible to provide optimal security services based on the user's real-time emotional state.

[0838] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID.

[0839] An "edge computer" is a computer that receives the ID of a device means, communicates with a server to obtain information and generative AI models, and performs processing to provide personalized services to users.

[0840] The "server" is a system that manages user-related data and generated AI models based on the ID sent from the edge computer, and returns the necessary information while ensuring security.

[0841] A "generative AI model" is an artificial intelligence model that is generated based on user-specific data and is used to predict and respond to user behavior and preferences.

[0842] The "emotion engine" is a system that uses cameras, microphones, biometric sensors, etc. to analyze the user's facial expressions, voice, and biometric information, and recognizes the user's emotional state in real time.

[0843] A "smartphone" is a portable information device that is equipped with a camera, microphone, and various sensors and can have an emotion engine installed.

[0844] "Real-time analytics" is the process of instantly processing collected data to instantly determine a user's emotional or other state.

[0845] "Security level" indicates the degree of various responses and actions that the system provides to protect the safety of users.

[0846] "Alert sending" is a function that immediately sends notifications and warnings when it is determined that a user is in danger.

[0847] Detailed description of embodiments of the present invention will be given below. The overall configuration of the system includes a device carried by a user that has the function of transmitting a specific ID, an edge computer, a server, and an emotion engine. As a specific example, a real-time security system using a smartphone will be described.

[0848] System configuration

[0849] 1. Device Means

[0850] A user carries a device that transmits a specific ID, such as a smartwatch or smart card, that transmits the user's unique ID.

[0851] 2. Edge Computers

[0852] The edge computer is installed on a smartphone. The smartphone is equipped with a camera, microphone, and biometric sensors, and an emotion engine is installed. This engine is used to analyze the user's emotional state in real time. The analyzed emotion data is processed by the edge computer, and communication with the server is performed as necessary.

[0853] 3. Server

[0854] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server is built using cloud services such as Google Cloud Platform (GCP) and Amazon Web Services (AWS). Communications are encrypted using secure protocols such as TLS.

[0855] 4. Emotion Engine

[0856] The emotion engine analyzes the user's facial expressions, voice, and biometric information. For example, it can use libraries such as Microsoft's Emotion API. This allows it to recognize the user's emotional state in real time and dynamically adjust the services provided according to the user's current emotional state.

[0857] Specific example of system operation

[0858] For real-time security monitoring systems

[0859] User Actions

[0860] First, the user wears a smartwatch or similar device and transmits an ID, which is received by a smartphone.

[0861] Edge computer operation

[0862] When the smartphone receives a specific ID, it sends a request to the server based on that ID to retrieve a generative AI model related to the user, while the emotion engine analyzes the user's emotional state using the smartphone's camera, microphone, and biometric sensors.

[0863] Server Operation

[0864] When the server receives a request from the edge computer, it searches the database for relevant user information and the generated AI model and sends it back to the edge computer. The server ensures security by using encrypted communications.

[0865] Actions based on the user's emotional state

[0866] If the emotion engine detects a user's emotional state (e.g., stress or fear), the edge computer automatically adjusts the security level and issues alerts as needed, which are then sent to emergency contacts using APIs such as Twilio.

[0867] Prompt Sentence Examples

[0868] "A system that analyzes a user's emotional state in real time and issues an alert if a specific emotion is detected."

[0869] This system allows users to enjoy real-time security services based on their emotional state, enabling them to take appropriate action even in sudden stress or fear situations.

[0870] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0871] Step 1:

[0872] A user wears a device such as a smart watch or smart card that transmits a specific ID. The ID transmitted from this device is verified to be unique to the user.

[0873] Input: A device with a user-specific ID

[0874] Output: A specific ID is sent to your smartphone.

[0875] Specific operation: The user wears a smartwatch, which transmits its ID to those around it.

[0876] Step 2:

[0877] The smartphone (edge ​​computer) receives the ID sent from the device. After receiving the ID, it sends a request for a generative AI model to the server based on that ID.

[0878] Input: Device ID

[0879] Output: Generated AI model request to the server

[0880] Specific operation: The smartphone receives the ID via Bluetooth or NFC and sends a request including that ID to the server.

[0881] Step 3:

[0882] The server receives a request from the edge computer, searches for the user data and generated AI model corresponding to the ID, and once the search is complete, it returns the results to the edge computer.

[0883] Input: A request containing an ID

[0884] Output: User data and generative AI model

[0885] How it works: The server searches the database for relevant information and sends the results, including the generative AI model, back to the edge computer.

[0886] Step 4:

[0887] The edge computer prepares to provide personalized services to users based on the generated AI model and user information received from the server, while the smartphone's emotion engine analyzes the user's facial expressions, voice, and biometric information in real time.

[0888] Input: Generative AI model and user information

[0889] Output: Preparation for providing individual services

[0890] How it works: Your smartphone collects data using the camera, microphone, and biometric sensors, which the emotion engine analyzes.

[0891] Step 5:

[0892] The emotion engine analyzes the user's emotional state in real time and dynamically adjusts the security level based on the results. If the user is in a state of stress or fear, the edge computer will send an alert.

[0893] Input: Sentiment analysis data

[0894] Output: Dynamically adjusted security levels and alerts

[0895] Specific operation: The emotion engine performs facial expression and voice analysis, and if an abnormality is detected, an alert is sent to an emergency contact using the Twilio API, etc.

[0896] In this way, a system can be realized that monitors the emotional state of a user in real time and dynamically provides necessary security measures.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] [Third embodiment]

[0901] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0902] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0903] 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).

[0904] 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.

[0905] 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.

[0906] 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).

[0907] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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."

[0913] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[0914] This system allows users to carry a device (hereinafter referred to as "device") that transmits a specific ID, and by having this device communicate with a server via an edge computer, the system provides users with personalized services that are optimized for them. The basic components of this system include the device, the edge computer, and the server.

[0915] Component Description

[0916] 1. Devices carried by users

[0917] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0918] 2. Edge Computers

[0919] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[0920] 3. Server

[0921] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0922] Program processing explanation

[0923] User Actions

[0924] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, such as when getting into a self-driving car or standing near a smart home device.

[0925] Operation of the terminal (edge ​​computer)

[0926] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server for the user's data and the generated AI model. The edge computer uses a high-speed communication protocol to minimize waiting time during this request.

[0927] Server Operation

[0928] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[0929] Specific examples

[0930] For self-driving cars

[0931] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[0932] For smart homes

[0933] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[0934] This allows users to receive services based on their preferences without having to make complicated settings.

[0935] The processing flow will be explained below.

[0936] Step 1:

[0937] The user puts on the device.

[0938] Specific behavior:

[0939] The user powers on the device and it becomes active.

[0940] The device begins broadcasting its unique ID.

[0941] Step 2:

[0942] The terminal (edge ​​computer) receives the device ID.

[0943] Specific behavior:

[0944] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[0945] The terminal detects the device's unique ID and stores the ID in its internal memory.

[0946] Step 3:

[0947] The device sends the ID to the server.

[0948] Specific behavior:

[0949] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[0950] The device sends the request using a secure communication protocol (e.g., HTTPS).

[0951] Step 4:

[0952] The server receives the request and retrieves the data.

[0953] Specific behavior:

[0954] The server receives the request from the terminal and performs authentication and authorization.

[0955] The server searches the database for user information and generated AI models related to the corresponding device ID.

[0956] Step 5:

[0957] The server sends the data back to the device.

[0958] Specific behavior:

[0959] The server organizes the search results and generates response data to be sent back to the terminal.

[0960] The server sends the response data to the terminal using a secure communication protocol.

[0961] Step 6:

[0962] The device analyzes the received data and runs the AI ​​model.

[0963] Specific behavior:

[0964] The device analyzes the data received from the server and extracts the necessary information and AI models.

[0965] The device loads the AI ​​model and sets it up in the execution environment.

[0966] Step 7:

[0967] The terminal provides the service to the user.

[0968] Specific behavior:

[0969] The device uses the acquired AI model to generate services for users in real time.

[0970] For example, in the case of a self-driving car, the device will calculate the optimal driving route and adjust the in-car environment.

[0971] In the case of a smart home, the device adjusts the settings of lighting and entertainment devices.

[0972] This allows users to seamlessly enjoy personalized services.

[0973] Example 1

[0974] 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."

[0975] With conventional technology, users had to manually configure many settings to receive individually optimized services. Furthermore, users had to redo the settings every time they moved or entered a new environment, which was inconvenient. Furthermore, data acquisition and processing took time, making it difficult to provide real-time services.

[0976] 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.

[0977] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, and a means for performing real-time processing using the acquired information and generative AI model by the edge computer to provide the user with personalized services. This eliminates the need for the user to manually configure the settings, allowing the user to always automatically receive the optimal service.

[0978] "User" refers to an individual who carries a device that transmits a specific ID and uses this system.

[0979] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID via wireless communication.

[0980] An "edge computer" refers to an intermediate device that receives an ID transmitted from a device means and communicates with a server.

[0981] "Server" refers to a computer system that manages and provides user-related information and generated AI models based on IDs sent from edge computers.

[0982] "Generative AI model" refers to a data model that uses artificial intelligence technology to provide users with personalized services that are optimized for them.

[0983] "Personalized services" refer to dedicated services provided according to a user's preferences and behavioral history based on the user's specific ID.

[0984] "Real-time processing" refers to processing in which data acquisition and service provision are carried out without delay after the ID is transmitted from the device means.

[0985] "Driving route optimization" refers to the function of calculating and providing the optimal driving route based on the user's driving history and current traffic information.

[0986] "Automatic adjustment of the in-car environment" refers to a function that automatically adjusts the in-car temperature, seat position, audio settings, etc. based on the user's preferences.

[0987] "Smart home devices" refers to various electronic devices used to improve user comfort in a living environment, such as lighting, air conditioning, and entertainment equipment.

[0988] "Secure communication protocol" refers to a communication method for securely exchanging data between device means, edge computers, and servers.

[0989] Detailed description of embodiments of the present invention will be given below. This system provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Basic components of this system include a device, an edge computer, and a server.

[0990] Component Description

[0991] Devices carried by users

[0992] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[0993] Edge Computer

[0994] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[0995] server

[0996] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[0997] Program processing explanation

[0998] The program's processing is explained in detail below. The hardware used is Raspberry Pi and Intel NUC, the software is Azure ML and AWS SageMaker, and the database is MySQL and MongoDB.

[0999] User Actions

[1000] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, for example, when getting into a self-driving car or performing a specific action in a smart home.

[1001] Operation of the terminal (edge ​​computer)

[1002] The terminal first receives the ID transmitted from the device and then sends a data request to the server based on this ID. Specifically, it sends a secure request using the HTTP protocol, requesting a generative AI model based on the user's driving history data, favorite TV shows, etc.

[1003] Server Operation

[1004] When the server receives the request, it searches the database for the relevant user information and generated AI model, which are then encrypted and sent back to the edge computer using a secure communication protocol (e.g., HTTPS).

[1005] The device receives, decompresses, analyzes, and processes the data in real time to provide users with personalized services, such as optimizing driving routes and automatically adjusting the in-car environment in a self-driving car, or adjusting lighting and entertainment settings in a smart home.

[1006] Specific examples

[1007] For self-driving cars

[1008] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[1009] Example prompt:

[1010] "Create a generative AI model that suggests the optimal driving route based on the user's driving history data and return it to the user with ID 'USER1234'"

[1011] For smart homes

[1012] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[1013] Example prompt:

[1014] "Generative AI models are created to optimize lighting and entertainment settings based on a user's past behavioral history."

[1015] This allows users to receive services based on their preferences without having to make complicated settings.

[1016] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1017] Step 1:

[1018] The user puts on the device.

[1019] Specific behavior:

[1020] The user wears a device with an RFID tag or NFC chip built in. This device constantly transmits a unique ID for the user. An LED on the device lights up to confirm that the device is being worn.

[1021] Input: When the user wears the device, it starts transmitting its ID.

[1022] Output: The ID to be sent (e.g. USER1234).

[1023] Step 2:

[1024] The terminal (edge ​​computer) receives the ID from the device.

[1025] Specific behavior:

[1026] The edge computer uses an NFC reader or Bluetooth receiver to receive the ID transmitted by the user's device.

[1027] Input: The ID originating from the device.

[1028] Output: The received ID (e.g. USER1234).

[1029] Step 3:

[1030] The device sends a request to the server.

[1031] Specific behavior:

[1032] The terminal generates an HTTP request based on the received ID and sends it to the server using a secure communication protocol (HTTPS).

[1033] Input: The received ID (e.g. USER1234).

[1034] Output: The data request sent to the server.

[1035] Step 4:

[1036] The server retrieves the user's data and sends back a response.

[1037] Specific behavior:

[1038] The server analyzes the received request, searches a database (e.g., MySQL, MongoDB) for the relevant user information and generated AI model, encrypts the search results, and sends them back to the edge computer.

[1039] Input: The data request sent to the server.

[1040] Output: Encrypted user information and generated AI model.

[1041] Step 5:

[1042] The terminal receives the data and processes it.

[1043] Specific behavior:

[1044] The device receives the data returned from the server, decompresses and analyzes it, and then performs real-time processing based on the information obtained, such as optimizing driving routes or automatically adjusting the in-car environment.

[1045] Input: Encrypted user information and generated AI model.

[1046] Output: Parsed data and optimized configuration information.

[1047] Step 6:

[1048] To provide users with the best personalized service.

[1049] Specific behavior:

[1050] The device will then use the analyzed data to provide personalized services, such as adjusting the air conditioning, adjusting the seat position, and playing audio in a self-driving car, as well as adjusting lighting and entertainment devices in a smart home.

[1051] Input: Parsed data and optimized configuration information.

[1052] Output: The personalized service provided to the user (e.g., tailored driving directions or preferences).

[1053] (Application example 1)

[1054] 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."

[1055] Traditional brick-and-mortar stores faced the challenge of being unable to efficiently provide services to individual users. Users were unable to quickly obtain product information based on their preferences and past purchase history, and stores lacked the means to efficiently make personalized suggestions. This resulted in a decline in users' purchasing motivation and had a negative impact on store sales. There were also technical challenges in providing personalized services to a large number of users at once in real time.

[1056] 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.

[1057] In this invention, the server includes a device means for transmitting a specific ID carried by the user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing personalized services to the user using the information and generative AI model acquired by the edge computer, and a means for communicating with the server via the edge computer when the user enters a store and providing product suggestions and special offer information based on the user's past purchase history and preferences in real time. This allows users to receive product suggestions and special discount information based on their preferences and past purchase history in real time simply by entering the store using their specific ID. Stores can also efficiently make personalized suggestions, improving the user experience and increasing sales.

[1058] "Device means" refers to a device carried by a user that has the function of transmitting a specific ID.

[1059] An "edge computer" refers to a proximity-based computing device that receives the ID of a device means and communicates with a server to obtain the necessary information and generative AI models.

[1060] "Server" refers to a computer system that manages information related to users and generative AI models and provides necessary data based on the ID sent from the edge computer.

[1061] A "generative AI model" is a model generated using artificial intelligence to provide personalized services based on a user's past behavior and preferences.

[1062] "Individualized services" refer to the provision of services and product suggestions that take into account the user's preferences and past behavioral history based on the user's specific ID.

[1063] "Means of providing in real time" refers to a method in which an edge computer instantly provides personalized information and services to users based on data obtained from a server.

[1064] "Communication protocol" refers to the rules and regulations used to communicate data between edge computers and servers.

[1065] "Past purchase history" refers to data that records information about products and services that a user has previously purchased.

[1066] "Product Suggestion" refers to providing a list of recommended products and services based on a user's characteristics.

[1067] "Special offer information" refers to special discounts, coupons, and other preferential treatment information provided to users.

[1068] The present invention is a system in which a user carries a device that transmits a specific ID, communicates with a server via an edge computer, and provides personalized services optimized for the user. Specific components for realizing this system include a device means, an edge computer, and a server.

[1069] Device Means

[1070] The device means is a device that has the function of transmitting a user's unique ID using an RFID tag or NFC chip. The device operates on low power and is attached to the user's clothing or accessories. This allows a specific ID to be constantly transmitted through the device carried by the user.

[1071] Edge Computer

[1072] The edge computer has the ability to receive the device ID in real time. The edge computer sends the received ID to the server to obtain information related to the user and the generated AI model. The edge computer uses a high-speed communication protocol (e.g., MQTT) to minimize the latency between receiving and sending data.

[1073] server

[1074] The server manages the information and generated AI model corresponding to the ID sent by the user. When the server receives a request from the edge computer, it searches the database for the corresponding user information and generated AI model and returns them to the edge computer. To ensure security, the server performs authentication and encrypted communication.

[1075] Providing personalized services

[1076] The edge computer uses the information acquired and the generated AI model to provide personalized services to users. When a user enters a physical store, the edge computer communicates with the server, allowing for real-time product suggestions and special offer information based on the user's past purchase history and preferences. Specific examples of services include the following:

[1077] Display of recommended products: When a user enters a store, the edge computer receives the device ID and retrieves a list of recommended products from the server. This allows the recommended products to be displayed on in-store displays and on the user's smartphone.

[1078] Offering special discount coupons: Based on the user's ID, a personalized special discount coupon will be offered. Coupon details will be sent to the user via a smartphone app.

[1079] Product search function: The smartphone app allows customers to check the location of products in the store in real time, and provides guidance on the shortest route when searching for a specific product.

[1080] Hardware and software used

[1081] Hardware: RFID tags, NFC chips, edge computers, servers, smartphones

[1082] Software: Python, Django (server side), MQTT (communication protocol), React Native (smartphone app)

[1083] Prompt Sentence Examples

[1084] Get User Data Prompt: "Return JSON data that captures a user's purchasing history and preferences based on their user ID."

[1085] Product recommendation prompt: "Generate a list of recommended products based on this user data."

[1086] This allows users to receive services based on their preferences, and stores to efficiently make personalized offers.

[1087] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1088] Step 1:

[1089] The user puts on the device means and enters the store.

[1090] What it does: A user enters a physical store wearing a device, such as an RFID tag or NFC chip, that constantly broadcasts a specific ID.

[1091] Step 2:

[1092] The edge computer receives the ID of the device means.

[1093] Specific operation: An edge computer is installed in the store and receives IDs transmitted from devices in real time. The input is the user's device ID, and the output is the received ID data.

[1094] Step 3:

[1095] The edge computer sends the received ID to the server.

[1096] Specific operation: The edge computer receives the user ID and sends it to the server using a communication protocol such as MQTT. The input is the received ID data, and the output is a request to the server.

[1097] Step 4:

[1098] The server searches for user information and generated AI models corresponding to the ID.

[1099] Specific operation: The server searches the database and obtains the user information (purchase history, preferences, etc.) and generative AI model corresponding to the ID. The input is a request to the server, and the output is the search result of the user information and the generative AI model.

[1100] Step 5:

[1101] The server returns the retrieved data to the edge computer.

[1102] Specific operation: The server returns user information and the generated AI model to the edge computer. The input is the user information and generated AI model from the search results, and the output is the response to the edge computer.

[1103] Step 6:

[1104] The edge computer processes data based on the information acquired and the generated AI model.

[1105] Specific operation: The edge computer analyzes the data received from the server and uses a generative AI model to generate optimal product suggestions and bonus information for the user. The input is the response data from the server, and the output is customized data based on the analysis results.

[1106] Step 7:

[1107] The edge computer provides the generated customization data to the user.

[1108] Specific operation: The edge computer displays the analysis results (recommended product lists and special offer information) on in-store displays or on the user's smartphone. The input is customized data of the analysis results, and the output is displayed on the user interface.

[1109] Step 8:

[1110] Users select and purchase products based on the information provided.

[1111] Specific operation: The user selects a product based on the information displayed on their smartphone or display, and proceeds with the purchase if necessary. The input is information from the user interface, and the output is the user's selection and purchase data.

[1112] This series of processes allows users to receive product suggestions and special offer information based on their preferences in real time, and enables stores to efficiently make personalized suggestions.

[1113] 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.

[1114] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[1115] The system of the present invention provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Furthermore, by combining an emotion engine, the system has the ability to dynamically adjust service content based on the user's emotional state. The basic components of this system include a device, an edge computer, a server, and an emotion engine.

[1116] Component Description

[1117] 1. Devices carried by users

[1118] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[1119] 2. Edge Computers

[1120] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. In addition, the edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, thereby recognizing the user's emotional state.

[1121] 3. Server

[1122] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[1123] 4. Emotion Engine

[1124] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, allowing the services provided to be dynamically adjusted according to the user's current emotional state.

[1125] Program processing explanation

[1126] User Actions

[1127] The user first puts on their device and enables it to broadcast its ID. Then, as the user moves closer to the edge computer (for example, getting into a self-driving car or standing near a smart home device), the emotion engine recognizes the user's emotions in real time.

[1128] Operation of the terminal (edge ​​computer)

[1129] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server requesting user information and a generative AI model. At the same time, the emotion engine analyzes the user's emotional state, and this data is also processed within the edge computer.

[1130] Server Operation

[1131] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[1132] Specific examples

[1133] For self-driving cars

[1134] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The emotion engine recognizes the user's emotional state; for example, if the user is nervous, the edge computer adjusts the driving style to be safer. The in-car environment is also optimized according to the user's emotional state. For example, it may play relaxing music or adjust the air conditioning temperature.

[1135] For smart homes

[1136] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state and, for example, if the user is tired, it will automatically lower the brightness of the lights or play relaxing music.

[1137] This allows users to seamlessly receive services based on their preferences and emotional state without having to go through complicated settings.

[1138] The processing flow will be explained below.

[1139] Step 1:

[1140] The user puts on the device.

[1141] Specific behavior:

[1142] The user powers on the device and it becomes active.

[1143] The device begins broadcasting its unique ID.

[1144] Step 2:

[1145] The terminal (edge ​​computer) receives the device ID.

[1146] Specific behavior:

[1147] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[1148] The terminal detects the device's unique ID and stores the ID in its internal memory.

[1149] Step 3:

[1150] The device sends the ID to the server.

[1151] Specific behavior:

[1152] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[1153] The device sends the request using a secure communication protocol (e.g., HTTPS).

[1154] Step 4:

[1155] The server receives the request and retrieves the data.

[1156] Specific behavior:

[1157] The server receives the request from the terminal and performs authentication and authorization.

[1158] The server searches the database for user information and generated AI models related to the corresponding device ID.

[1159] Step 5:

[1160] The server sends the data back to the device.

[1161] Specific behavior:

[1162] The server organizes the search results and generates response data to be sent back to the terminal.

[1163] The server sends the response data to the terminal using a secure communication protocol.

[1164] Step 6:

[1165] The device analyzes the received data and runs the AI ​​model.

[1166] Specific behavior:

[1167] The device analyzes the data received from the server and extracts the necessary information and generative AI model.

[1168] The device loads the AI ​​model and sets it up in the execution environment.

[1169] Step 7:

[1170] The device (emotion engine) recognizes the user's emotions.

[1171] Specific behavior:

[1172] The device's built-in emotion engine collects data using the user's facial expression recognition camera, voice analysis microphone, and biometric sensors.

[1173] The emotion engine analyzes the collected data and recognizes the user's real-time emotional state.

[1174] Step 8:

[1175] The terminal provides the service to the user.

[1176] Specific behavior:

[1177] The device uses the analysis results of the AI ​​model and emotion engine acquired to generate individual services for users in real time.

[1178] For example, in the case of a self-driving car, the device calculates the optimal driving route and adjusts the in-car environment (music selection, air conditioning settings, etc.).

[1179] In the case of a smart home, the device will automatically adjust the brightness of the lights, play relaxing music, and perform other tasks.

[1180] This allows users to seamlessly enjoy personalized services, particularly those tailored to their emotional state.

[1181] Example 2

[1182] 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."

[1183] Conventional personalized service provision systems provide uniform services without considering the user's emotional state, making it difficult to increase true user satisfaction. It is also difficult to adjust services in real time according to the user's behavior and environment. Therefore, a system that can dynamically adjust services based on the user's emotional state is needed.

[1184] 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.

[1185] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with a personalized service using the information and generative AI model acquired by the edge computer, a means for the edge computer to incorporate an emotion engine and to analyze the user's facial expressions, voice, and biometric information to recognize the user's emotional state, and a means for dynamically adjusting the content of the service according to the emotional state, thereby enabling the provision of detailed services based on the user's emotional state.

[1186] "Device means for transmitting a specific ID carried by a user" refers to a device that can be carried by a user and is used to transmit unique identification information, and specific examples include RFID tags and NFC chips.

[1187] An "edge computer" is an advanced processing device that receives IDs and analyzes user information, and is responsible for relaying and analyzing data between the server and user devices.

[1188] "Server" refers to a central processing unit that retrieves user information and generative AI models from a database based on requests received from the edge computer and sends that information to the edge computer.

[1189] "Generative AI models" refer to algorithms or programs that are generated based on a user's past data and prompts and are used to provide personalized services.

[1190] "Emotion engine" refers to a software or hardware device that analyzes a user's facial expressions, voice, and biometric information to recognize their emotional state in real time.

[1191] "Means for analyzing a user's facial expressions, voice, and biometric information" refers to technology that uses cameras, microphones, and biometric sensors to determine a user's emotional state and utilizes that information to provide services.

[1192] "Means for dynamically adjusting service content" refers to a function that changes the content of the service provided in real time according to the user's emotional state, providing the user with the optimal experience.

[1193] The present invention is a system that provides personalized services to users by using a device that transmits a specific ID carried by the user, an edge computer, a server, and an emotion engine. The following describes in detail an embodiment of the present invention.

[1194] First, a user needs a device to carry with them. This device can be an RFID tag or an NFC chip, and transmits a unique ID. The user wears this device on their arm or in their pocket, and the device is constantly transmitting its ID.

[1195] Next is the edge computer. The edge computer has the ability to receive IDs sent from devices and a network interface for communicating with the server. This edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, recognizes the user's emotional state in real time, and provides optimized services to the user based on the generative AI model obtained from the server.

[1196] The server is a central processing unit that manages the generated AI model and related data for each user. It searches for user information corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security, enabling the management of user information and the provision of appropriate services.

[1197] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, and dynamically adjusts the service content based on the user's emotional state.

[1198] Take the case of a self-driving car as a specific example. When a user wears their device and gets into the self-driving car, the edge computer receives the device's ID and sends a request to the server. The server then sends back to the edge computer a generative AI model based on the user's driving history and preferences. The emotion engine analyzes the user's emotional state in real time; for example, if the user is nervous, the edge computer will adjust the driving style to be safer. The in-car environment is also optimized according to the user's emotional state, playing relaxing music or adjusting the air conditioning temperature.

[1199] In the case of a smart home, when a user wears a device and stands near the smart home device, the edge computer receives the device's ID and sends a request to the server. The server then sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state, and if the user is tired, for example, it will automatically lower the brightness of the lights or play relaxing music.

[1200] An example of a prompt sentence might be:

[1201] Example prompt to adjust driving style based on the user's emotional state when getting into a self-driving car:

[1202] "If the user is nervous, we want to adjust their driving style to be safer and play relaxing music."

[1203] Example prompt to adjust the environment based on the user's emotional state when near a smart home device:

[1204] "If the user is tired, we want to lower the lighting brightness and play relaxing music."

[1205] This allows users to seamlessly receive optimal services based on their preferences and emotional state without any special operations.

[1206] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1207] Step 1: User puts on the device

[1208] A user wears their device (e.g., an RFID tag or NFC chip) and configures it to transmit a specific ID.

[1209] Specific actions

[1210] A user wears an RFID tag as a watch and turns on the device, or carries an NFC chip.

[1211] input

[1212] A user-specific ID set on the device

[1213] output

[1214] The ID will be in a calling state.

[1215] Step 2: The user moves closer to the edge computer

[1216] The user moves within the communication range of the edge computer.

[1217] Specific actions

[1218] A user opens the door of a self-driving car and gets in, or enters the living room of a smart home.

[1219] input

[1220] User location information

[1221] output

[1222] The user's device ID comes within the communication range of the edge computer.

[1223] Step 3: Edge computer receives ID

[1224] The terminal's edge computer receives the ID sent from the device.

[1225] Specific actions

[1226] The ID receiving module in the edge computer captures the signal and decodes the ID.

[1227] input

[1228] Unique ID of the user originating from the device

[1229] output

[1230] The received user's ID data.

[1231] Step 4: The edge computer sends a request to the server

[1232] The device's edge computer sends a request to the server based on the received ID, requesting user information and a generated AI model.

[1233] Specific actions

[1234] Create request data including user ID and send it to the server along with authentication information using HTTPS protocol.

[1235] input

[1236] Received user ID data

[1237] output

[1238] The request data for the server.

[1239] Step 5: The server retrieves and sends the user information

[1240] The server analyzes the received request, searches the database for the relevant user information and generated AI model, and sends it back to the edge computer.

[1241] Specific actions

[1242] A query using the user ID as a key is executed against the database, and the obtained user information and generated AI model are packaged in a secure format and sent to the edge computer.

[1243] input

[1244] Request data for the server (including user ID)

[1245] output

[1246] Applicable user information and generated AI models.

[1247] Step 6: The edge computer analyzes the received data

[1248] The edge computer on the terminal receives the response from the server, extracts the necessary data, and analyzes it.

[1249] Specific actions

[1250] Analyzes the received data, loads the user profile and generated AI model into memory, and initializes necessary settings and parameters.

[1251] input

[1252] User information and generated AI model sent from the server

[1253] output

[1254] User profile and AI model loaded into memory.

[1255] Step 7: The edge computer uses the emotion engine to analyze the user's emotional state.

[1256] The emotion engine installed in the device's edge computer analyzes the user's facial expressions, voice, and biometric information to determine their emotional state in real time.

[1257] Specific actions

[1258] A facial expression analysis camera captures the user's facial expressions and uses image processing to recognize emotions, a voice analysis microphone analyzes the tone of the user's voice, and processes data from biometric sensors (e.g., heart rate monitors).

[1259] input

[1260] User's facial expressions, voice, and biometric information

[1261] output

[1262] User emotional state data.

[1263] Step 8: Provide optimized services to users

[1264] The device's edge computer provides personalized services based on the user's emotional state and profile information.

[1265] Specific actions

[1266] In a self-driving car, it adjusts the driving mode to help the user relax, and in a smart home, it changes the lighting and music settings.

[1267] input

[1268] User emotional state data, profile information, generative AI model

[1269] output

[1270] Implementing personalized services (e.g. adjusting driving modes, changing lighting settings).

[1271] (Application example 2)

[1272] 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."

[1273] In today's world, while services that utilize personal information and emotional data are on the rise, there is also a rapid demand for security systems to ensure user safety. However, current security systems rarely dynamically adjust based on the user's real-time emotional state, making it difficult to immediately sense a user's emotions and stress and take appropriate action. Furthermore, security systems often require users to wear specialized devices to perform emotion analysis tailored to each user's individual situation, which can be cumbersome or require additional specialized equipment. Therefore, there is a need for a system that can perform emotion analysis using a commonly carried device and provide optimal security services in real time.

[1274] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1275] In this invention, the server includes a device means for transmitting a specific ID carried by a user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with personalized services using the information and generative AI model acquired by the edge computer, a means for the edge computer to be installed on a smartphone and for an emotion engine to analyze the user's emotional state in real time using the smartphone's camera and microphone, and a means for dynamically adjusting the security level based on the user's emotional state and issuing an alert when necessary, thereby making it possible to provide optimal security services based on the user's real-time emotional state.

[1276] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID.

[1277] An "edge computer" is a computer that receives the ID of a device means, communicates with a server to obtain information and generative AI models, and performs processing to provide personalized services to users.

[1278] The "server" is a system that manages user-related data and generated AI models based on the ID sent from the edge computer, and returns the necessary information while ensuring security.

[1279] A "generative AI model" is an artificial intelligence model that is generated based on user-specific data and is used to predict and respond to user behavior and preferences.

[1280] The "emotion engine" is a system that uses cameras, microphones, biometric sensors, etc. to analyze the user's facial expressions, voice, and biometric information, and recognizes the user's emotional state in real time.

[1281] A "smartphone" is a portable information device that is equipped with a camera, microphone, and various sensors and can have an emotion engine installed.

[1282] "Real-time analytics" is the process of instantly processing collected data to instantly determine a user's emotional or other state.

[1283] "Security level" indicates the degree of various responses and actions that the system provides to protect the safety of users.

[1284] "Alert sending" is a function that immediately sends notifications and warnings when it is determined that a user is in danger.

[1285] Detailed description of embodiments of the present invention will be given below. The overall configuration of the system includes a device carried by a user that has the function of transmitting a specific ID, an edge computer, a server, and an emotion engine. As a specific example, a real-time security system using a smartphone will be described.

[1286] System configuration

[1287] 1. Device Means

[1288] A user carries a device that transmits a specific ID, such as a smartwatch or smart card, that transmits the user's unique ID.

[1289] 2. Edge Computers

[1290] The edge computer is installed on a smartphone. The smartphone is equipped with a camera, microphone, and biometric sensors, and an emotion engine is installed. This engine is used to analyze the user's emotional state in real time. The analyzed emotion data is processed by the edge computer, and communication with the server is performed as necessary.

[1291] 3. Server

[1292] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server is built using cloud services such as Google Cloud Platform (GCP) and Amazon Web Services (AWS). Communications are encrypted using secure protocols such as TLS.

[1293] 4. Emotion Engine

[1294] The emotion engine analyzes the user's facial expressions, voice, and biometric information. For example, it can use libraries such as Microsoft's Emotion API. This allows it to recognize the user's emotional state in real time and dynamically adjust the services provided according to the user's current emotional state.

[1295] Specific example of system operation

[1296] For real-time security monitoring systems

[1297] User Actions

[1298] First, the user wears a smartwatch or similar device and transmits an ID, which is received by a smartphone.

[1299] Edge computer operation

[1300] When the smartphone receives a specific ID, it sends a request to the server based on that ID to retrieve a generative AI model related to the user, while the emotion engine analyzes the user's emotional state using the smartphone's camera, microphone, and biometric sensors.

[1301] Server Operation

[1302] When the server receives a request from the edge computer, it searches the database for relevant user information and the generated AI model and sends it back to the edge computer. The server ensures security by using encrypted communications.

[1303] Actions based on the user's emotional state

[1304] If the emotion engine detects a user's emotional state (e.g., stress or fear), the edge computer automatically adjusts the security level and issues alerts as needed, which are then sent to emergency contacts using APIs such as Twilio.

[1305] Prompt Sentence Examples

[1306] "A system that analyzes a user's emotional state in real time and issues an alert if a specific emotion is detected."

[1307] This system allows users to enjoy real-time security services based on their emotional state, enabling them to take appropriate action even in sudden stress or fear situations.

[1308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1309] Step 1:

[1310] A user wears a device such as a smart watch or smart card that transmits a specific ID. The ID transmitted from this device is verified to be unique to the user.

[1311] Input: A device with a user-specific ID

[1312] Output: A specific ID is sent to your smartphone.

[1313] Specific operation: The user wears a smartwatch, which transmits its ID to those around it.

[1314] Step 2:

[1315] The smartphone (edge ​​computer) receives the ID sent from the device. After receiving the ID, it sends a request for a generative AI model to the server based on that ID.

[1316] Input: Device ID

[1317] Output: Generated AI model request to the server

[1318] Specific operation: The smartphone receives the ID via Bluetooth or NFC and sends a request including that ID to the server.

[1319] Step 3:

[1320] The server receives a request from the edge computer, searches for the user data and generated AI model corresponding to the ID, and once the search is complete, it returns the results to the edge computer.

[1321] Input: A request containing an ID

[1322] Output: User data and generative AI model

[1323] How it works: The server searches the database for relevant information and sends the results, including the generative AI model, back to the edge computer.

[1324] Step 4:

[1325] The edge computer prepares to provide personalized services to users based on the generated AI model and user information received from the server, while the smartphone's emotion engine analyzes the user's facial expressions, voice, and biometric information in real time.

[1326] Input: Generative AI model and user information

[1327] Output: Preparation for providing individual services

[1328] How it works: Your smartphone collects data using the camera, microphone, and biometric sensors, which the emotion engine analyzes.

[1329] Step 5:

[1330] The emotion engine analyzes the user's emotional state in real time and dynamically adjusts the security level based on the results. If the user is in a state of stress or fear, the edge computer will send an alert.

[1331] Input: Sentiment analysis data

[1332] Output: Dynamically adjusted security levels and alerts

[1333] Specific operation: The emotion engine performs facial expression and voice analysis, and if an abnormality is detected, an alert is sent to an emergency contact using the Twilio API, etc.

[1334] In this way, a system can be realized that monitors the emotional state of a user in real time and dynamically provides necessary security measures.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] [Fourth embodiment]

[1339] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1340] 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.

[1341] 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).

[1342] 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.

[1343] 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.

[1344] 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).

[1345] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] 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.

[1350] 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.

[1351] 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."

[1352] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[1353] This system allows users to carry a device (hereinafter referred to as "device") that transmits a specific ID, and by having this device communicate with a server via an edge computer, the system provides users with personalized services that are optimized for them. The basic components of this system include the device, the edge computer, and the server.

[1354] Component Description

[1355] 1. Devices carried by users

[1356] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[1357] 2. Edge Computers

[1358] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[1359] 3. Server

[1360] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[1361] Program processing explanation

[1362] User Actions

[1363] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, such as when getting into a self-driving car or standing near a smart home device.

[1364] Operation of the terminal (edge ​​computer)

[1365] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server for the user's data and the generated AI model. The edge computer uses a high-speed communication protocol to minimize waiting time during this request.

[1366] Server Operation

[1367] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[1368] Specific examples

[1369] For self-driving cars

[1370] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[1371] For smart homes

[1372] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[1373] This allows users to receive services based on their preferences without having to make complicated settings.

[1374] The processing flow will be explained below.

[1375] Step 1:

[1376] The user puts on the device.

[1377] Specific behavior:

[1378] The user powers on the device and it becomes active.

[1379] The device begins broadcasting its unique ID.

[1380] Step 2:

[1381] The terminal (edge ​​computer) receives the device ID.

[1382] Specific behavior:

[1383] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[1384] The terminal detects the device's unique ID and stores the ID in its internal memory.

[1385] Step 3:

[1386] The device sends the ID to the server.

[1387] Specific behavior:

[1388] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[1389] The device sends the request using a secure communication protocol (e.g., HTTPS).

[1390] Step 4:

[1391] The server receives the request and retrieves the data.

[1392] Specific behavior:

[1393] The server receives the request from the terminal and performs authentication and authorization.

[1394] The server searches the database for user information and generated AI models related to the corresponding device ID.

[1395] Step 5:

[1396] The server sends the data back to the device.

[1397] Specific behavior:

[1398] The server organizes the search results and generates response data to be sent back to the terminal.

[1399] The server sends the response data to the terminal using a secure communication protocol.

[1400] Step 6:

[1401] The device analyzes the received data and runs the AI ​​model.

[1402] Specific behavior:

[1403] The device analyzes the data received from the server and extracts the necessary information and AI models.

[1404] The device loads the AI ​​model and sets it up in the execution environment.

[1405] Step 7:

[1406] The terminal provides the service to the user.

[1407] Specific behavior:

[1408] The device uses the acquired AI model to generate services for users in real time.

[1409] For example, in the case of a self-driving car, the device will calculate the optimal driving route and adjust the in-car environment.

[1410] In the case of a smart home, the device adjusts the settings of lighting and entertainment devices.

[1411] This allows users to seamlessly enjoy personalized services.

[1412] Example 1

[1413] 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."

[1414] With conventional technology, users had to manually configure many settings to receive individually optimized services. Furthermore, users had to redo the settings every time they moved or entered a new environment, which was inconvenient. Furthermore, data acquisition and processing took time, making it difficult to provide real-time services.

[1415] 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.

[1416] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, and a means for performing real-time processing using the acquired information and generative AI model by the edge computer to provide the user with personalized services. This eliminates the need for the user to manually configure the settings, allowing the user to always automatically receive the optimal service.

[1417] "User" refers to an individual who carries a device that transmits a specific ID and uses this system.

[1418] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID via wireless communication.

[1419] An "edge computer" refers to an intermediate device that receives an ID transmitted from a device means and communicates with a server.

[1420] "Server" refers to a computer system that manages and provides user-related information and generated AI models based on IDs sent from edge computers.

[1421] "Generative AI model" refers to a data model that uses artificial intelligence technology to provide users with personalized services that are optimized for them.

[1422] "Personalized services" refer to dedicated services provided according to a user's preferences and behavioral history based on the user's specific ID.

[1423] "Real-time processing" refers to processing in which data acquisition and service provision are carried out without delay after the ID is transmitted from the device means.

[1424] "Driving route optimization" refers to the function of calculating and providing the optimal driving route based on the user's driving history and current traffic information.

[1425] "Automatic adjustment of the in-car environment" refers to a function that automatically adjusts the in-car temperature, seat position, audio settings, etc. based on the user's preferences.

[1426] "Smart home devices" refers to various electronic devices used to improve user comfort in a living environment, such as lighting, air conditioning, and entertainment equipment.

[1427] "Secure communication protocol" refers to a communication method for securely exchanging data between device means, edge computers, and servers.

[1428] Detailed description of embodiments of the present invention will be given below. This system provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Basic components of this system include a device, an edge computer, and a server.

[1429] Component Description

[1430] Devices carried by users

[1431] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[1432] Edge Computer

[1433] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. Because the edge computer performs processing in real time near the user, it can minimize delays.

[1434] server

[1435] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[1436] Program processing explanation

[1437] The program's processing is explained in detail below. The hardware used is Raspberry Pi and Intel NUC, the software is Azure ML and AWS SageMaker, and the database is MySQL and MongoDB.

[1438] User Actions

[1439] A user first puts on their device and enables it to broadcast its identity, then the user moves closer to the edge computer, for example, when getting into a self-driving car or performing a specific action in a smart home.

[1440] Operation of the terminal (edge ​​computer)

[1441] The terminal first receives the ID transmitted from the device and then sends a data request to the server based on this ID. Specifically, it sends a secure request using the HTTP protocol, requesting a generative AI model based on the user's driving history data, favorite TV shows, etc.

[1442] Server Operation

[1443] When the server receives the request, it searches the database for the relevant user information and generated AI model, which are then encrypted and sent back to the edge computer using a secure communication protocol (e.g., HTTPS).

[1444] The device receives, decompresses, analyzes, and processes the data in real time to provide users with personalized services, such as optimizing driving routes and automatically adjusting the in-car environment in a self-driving car, or adjusting lighting and entertainment settings in a smart home.

[1445] Specific examples

[1446] For self-driving cars

[1447] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The edge computer uses this data to optimize the driving route and in-car environment for the user. For example, it automatically adjusts the air conditioning settings, adjusts the seat position, and plays audio.

[1448] Example prompt:

[1449] "Create a generative AI model that suggests the optimal driving route based on the user's driving history data and return it to the user with ID 'USER1234'"

[1450] For smart homes

[1451] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server returns the user's preferences and past behavioral history data to the edge computer. Based on this data, the edge computer can automatically adjust lighting brightness or operate entertainment devices, for example.

[1452] Example prompt:

[1453] "Generative AI models are created to optimize lighting and entertainment settings based on a user's past behavioral history."

[1454] This allows users to receive services based on their preferences without having to make complicated settings.

[1455] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1456] Step 1:

[1457] The user puts on the device.

[1458] Specific behavior:

[1459] The user wears a device with an RFID tag or NFC chip built in. This device constantly transmits a unique ID for the user. An LED on the device lights up to confirm that the device is being worn.

[1460] Input: When the user wears the device, it starts transmitting its ID.

[1461] Output: The ID to be sent (e.g. USER1234).

[1462] Step 2:

[1463] The terminal (edge ​​computer) receives the ID from the device.

[1464] Specific behavior:

[1465] The edge computer uses an NFC reader or Bluetooth receiver to receive the ID transmitted by the user's device.

[1466] Input: The ID originating from the device.

[1467] Output: The received ID (e.g. USER1234).

[1468] Step 3:

[1469] The device sends a request to the server.

[1470] Specific behavior:

[1471] The terminal generates an HTTP request based on the received ID and sends it to the server using a secure communication protocol (HTTPS).

[1472] Input: The received ID (e.g. USER1234).

[1473] Output: The data request sent to the server.

[1474] Step 4:

[1475] The server retrieves the user's data and sends back a response.

[1476] Specific behavior:

[1477] The server analyzes the received request, searches a database (e.g., MySQL, MongoDB) for the relevant user information and generated AI model, encrypts the search results, and sends them back to the edge computer.

[1478] Input: The data request sent to the server.

[1479] Output: Encrypted user information and generated AI model.

[1480] Step 5:

[1481] The terminal receives the data and processes it.

[1482] Specific behavior:

[1483] The device receives the data returned from the server, decompresses and analyzes it, and then performs real-time processing based on the information obtained, such as optimizing driving routes or automatically adjusting the in-car environment.

[1484] Input: Encrypted user information and generated AI model.

[1485] Output: Parsed data and optimized configuration information.

[1486] Step 6:

[1487] To provide users with the best personalized service.

[1488] Specific behavior:

[1489] The device will then use the analyzed data to provide personalized services, such as adjusting the air conditioning, adjusting the seat position, and playing audio in a self-driving car, as well as adjusting lighting and entertainment devices in a smart home.

[1490] Input: Parsed data and optimized configuration information.

[1491] Output: The personalized service provided to the user (e.g., tailored driving directions or preferences).

[1492] (Application example 1)

[1493] 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."

[1494] Traditional brick-and-mortar stores faced the challenge of being unable to efficiently provide services to individual users. Users were unable to quickly obtain product information based on their preferences and past purchase history, and stores lacked the means to efficiently make personalized suggestions. This resulted in a decline in users' purchasing motivation and had a negative impact on store sales. There were also technical challenges in providing personalized services to a large number of users at once in real time.

[1495] 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.

[1496] In this invention, the server includes a device means for transmitting a specific ID carried by the user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing personalized services to the user using the information and generative AI model acquired by the edge computer, and a means for communicating with the server via the edge computer when the user enters a store and providing product suggestions and special offer information based on the user's past purchase history and preferences in real time. This allows users to receive product suggestions and special discount information based on their preferences and past purchase history in real time simply by entering the store using their specific ID. Stores can also efficiently make personalized suggestions, improving the user experience and increasing sales.

[1497] "Device means" refers to a device carried by a user that has the function of transmitting a specific ID.

[1498] An "edge computer" refers to a proximity-based computing device that receives the ID of a device means and communicates with a server to obtain the necessary information and generative AI models.

[1499] "Server" refers to a computer system that manages information related to users and generative AI models and provides necessary data based on the ID sent from the edge computer.

[1500] A "generative AI model" is a model generated using artificial intelligence to provide personalized services based on a user's past behavior and preferences.

[1501] "Individualized services" refer to the provision of services and product suggestions that take into account the user's preferences and past behavioral history based on the user's specific ID.

[1502] "Means of providing in real time" refers to a method in which an edge computer instantly provides personalized information and services to users based on data obtained from a server.

[1503] "Communication protocol" refers to the rules and regulations used to communicate data between edge computers and servers.

[1504] "Past purchase history" refers to data that records information about products and services that a user has previously purchased.

[1505] "Product Suggestion" refers to providing a list of recommended products and services based on a user's characteristics.

[1506] "Special offer information" refers to special discounts, coupons, and other preferential treatment information provided to users.

[1507] The present invention is a system in which a user carries a device that transmits a specific ID, communicates with a server via an edge computer, and provides personalized services optimized for the user. Specific components for realizing this system include a device means, an edge computer, and a server.

[1508] Device Means

[1509] The device means is a device that has the function of transmitting a user's unique ID using an RFID tag or NFC chip. The device operates on low power and is attached to the user's clothing or accessories. This allows a specific ID to be constantly transmitted through the device carried by the user.

[1510] Edge Computer

[1511] The edge computer has the ability to receive the device ID in real time. The edge computer sends the received ID to the server to obtain information related to the user and the generated AI model. The edge computer uses a high-speed communication protocol (e.g., MQTT) to minimize the latency between receiving and sending data.

[1512] server

[1513] The server manages the information and generated AI model corresponding to the ID sent by the user. When the server receives a request from the edge computer, it searches the database for the corresponding user information and generated AI model and returns them to the edge computer. To ensure security, the server performs authentication and encrypted communication.

[1514] Providing personalized services

[1515] The edge computer uses the information acquired and the generated AI model to provide personalized services to users. When a user enters a physical store, the edge computer communicates with the server, allowing for real-time product suggestions and special offer information based on the user's past purchase history and preferences. Specific examples of services include the following:

[1516] Display of recommended products: When a user enters a store, the edge computer receives the device ID and retrieves a list of recommended products from the server. This allows the recommended products to be displayed on in-store displays and on the user's smartphone.

[1517] Offering special discount coupons: Based on the user's ID, a personalized special discount coupon will be offered. Coupon details will be sent to the user via a smartphone app.

[1518] Product search function: The smartphone app allows customers to check the location of products in the store in real time, and provides guidance on the shortest route when searching for a specific product.

[1519] Hardware and software used

[1520] Hardware: RFID tags, NFC chips, edge computers, servers, smartphones

[1521] Software: Python, Django (server side), MQTT (communication protocol), React Native (smartphone app)

[1522] Prompt Sentence Examples

[1523] Get User Data Prompt: "Return JSON data that captures a user's purchasing history and preferences based on their user ID."

[1524] Product recommendation prompt: "Generate a list of recommended products based on this user data."

[1525] This allows users to receive services based on their preferences, and stores to efficiently make personalized offers.

[1526] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1527] Step 1:

[1528] The user puts on the device means and enters the store.

[1529] What it does: A user enters a physical store wearing a device, such as an RFID tag or NFC chip, that constantly broadcasts a specific ID.

[1530] Step 2:

[1531] The edge computer receives the ID of the device means.

[1532] Specific operation: An edge computer is installed in the store and receives IDs transmitted from devices in real time. The input is the user's device ID, and the output is the received ID data.

[1533] Step 3:

[1534] The edge computer sends the received ID to the server.

[1535] Specific operation: The edge computer receives the user ID and sends it to the server using a communication protocol such as MQTT. The input is the received ID data, and the output is a request to the server.

[1536] Step 4:

[1537] The server searches for user information and generated AI models corresponding to the ID.

[1538] Specific operation: The server searches the database and obtains the user information (purchase history, preferences, etc.) and generative AI model corresponding to the ID. The input is a request to the server, and the output is the search result of the user information and the generative AI model.

[1539] Step 5:

[1540] The server returns the retrieved data to the edge computer.

[1541] Specific operation: The server returns user information and the generated AI model to the edge computer. The input is the user information and generated AI model from the search results, and the output is the response to the edge computer.

[1542] Step 6:

[1543] The edge computer processes data based on the information acquired and the generated AI model.

[1544] Specific operation: The edge computer analyzes the data received from the server and uses a generative AI model to generate optimal product suggestions and bonus information for the user. The input is the response data from the server, and the output is customized data based on the analysis results.

[1545] Step 7:

[1546] The edge computer provides the generated customization data to the user.

[1547] Specific operation: The edge computer displays the analysis results (recommended product lists and special offer information) on in-store displays or on the user's smartphone. The input is customized data of the analysis results, and the output is displayed on the user interface.

[1548] Step 8:

[1549] Users select and purchase products based on the information provided.

[1550] Specific operation: The user selects a product based on the information displayed on their smartphone or display, and proceeds with the purchase if necessary. The input is information from the user interface, and the output is the user's selection and purchase data.

[1551] This series of processes allows users to receive product suggestions and special offer information based on their preferences in real time, and enables stores to efficiently make personalized suggestions.

[1552] 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.

[1553] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a detailed description of the embodiments of the present invention.

[1554] The system of the present invention provides users with personalized services optimized for them by having them carry a device that transmits a specific ID and have it communicate with a server via an edge computer. Furthermore, by combining an emotion engine, the system has the ability to dynamically adjust service content based on the user's emotional state. The basic components of this system include a device, an edge computer, a server, and an emotion engine.

[1555] Component Description

[1556] 1. Devices carried by users

[1557] The device, which transmits a unique user ID and is attached to the user's clothing or accessories, typically an RFID tag or NFC chip, operates on low power and can remain active at all times.

[1558] 2. Edge Computers

[1559] The edge computer has the function of receiving the ID sent from the device. Based on the received ID, the edge computer communicates with the server to obtain the necessary data and generative AI models. In addition, the edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, thereby recognizing the user's emotional state.

[1560] 3. Server

[1561] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security.

[1562] 4. Emotion Engine

[1563] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, allowing the services provided to be dynamically adjusted according to the user's current emotional state.

[1564] Program processing explanation

[1565] User Actions

[1566] The user first puts on their device and enables it to broadcast its ID. Then, as the user moves closer to the edge computer (for example, getting into a self-driving car or standing near a smart home device), the emotion engine recognizes the user's emotions in real time.

[1567] Operation of the terminal (edge ​​computer)

[1568] The edge computer first receives the ID sent from the device. Upon receiving this ID, the edge computer sends a request to the server requesting user information and a generative AI model. At the same time, the emotion engine analyzes the user's emotional state, and this data is also processed within the edge computer.

[1569] Server Operation

[1570] The server receives requests from the edge computer, searches the database for relevant user information and generated AI models, and returns the search results to the edge computer, allowing it to process the requests in real time.

[1571] Specific examples

[1572] For self-driving cars

[1573] The user puts on their device and gets into the self-driving car. The edge computer receives the device's ID and sends a request to the server. The server returns a generative AI model based on the user's driving history and preferences to the edge computer. The emotion engine recognizes the user's emotional state; for example, if the user is nervous, the edge computer adjusts the driving style to be safer. The in-car environment is also optimized according to the user's emotional state. For example, it may play relaxing music or adjust the air conditioning temperature.

[1574] For smart homes

[1575] The user wears the device and stands near a smart home device. The edge computer receives the device's ID and sends a request to the server. The server sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state and, for example, if the user is tired, it will automatically lower the brightness of the lights or play relaxing music.

[1576] This allows users to seamlessly receive services based on their preferences and emotional state without having to go through complicated settings.

[1577] The processing flow will be explained below.

[1578] Step 1:

[1579] The user puts on the device.

[1580] Specific behavior:

[1581] The user powers on the device and it becomes active.

[1582] The device begins broadcasting its unique ID.

[1583] Step 2:

[1584] The terminal (edge ​​computer) receives the device ID.

[1585] Specific behavior:

[1586] The device scans for nearby devices using a short-range communication protocol (e.g., Bluetooth, NFC).

[1587] The terminal detects the device's unique ID and stores the ID in its internal memory.

[1588] Step 3:

[1589] The device sends the ID to the server.

[1590] Specific behavior:

[1591] The terminal generates a data acquisition request to the server using the ID acquired from the device.

[1592] The device sends the request using a secure communication protocol (e.g., HTTPS).

[1593] Step 4:

[1594] The server receives the request and retrieves the data.

[1595] Specific behavior:

[1596] The server receives the request from the terminal and performs authentication and authorization.

[1597] The server searches the database for user information and generated AI models related to the corresponding device ID.

[1598] Step 5:

[1599] The server sends the data back to the device.

[1600] Specific behavior:

[1601] The server organizes the search results and generates response data to be sent back to the terminal.

[1602] The server sends the response data to the terminal using a secure communication protocol.

[1603] Step 6:

[1604] The device analyzes the received data and runs the AI ​​model.

[1605] Specific behavior:

[1606] The device analyzes the data received from the server and extracts the necessary information and generative AI model.

[1607] The device loads the AI ​​model and sets it up in the execution environment.

[1608] Step 7:

[1609] The device (emotion engine) recognizes the user's emotions.

[1610] Specific behavior:

[1611] The device's built-in emotion engine collects data using the user's facial expression recognition camera, voice analysis microphone, and biometric sensors.

[1612] The emotion engine analyzes the collected data and recognizes the user's real-time emotional state.

[1613] Step 8:

[1614] The terminal provides the service to the user.

[1615] Specific behavior:

[1616] The device uses the analysis results of the AI ​​model and emotion engine acquired to generate individual services for users in real time.

[1617] For example, in the case of a self-driving car, the device calculates the optimal driving route and adjusts the in-car environment (music selection, air conditioning settings, etc.).

[1618] In the case of a smart home, the device will automatically adjust the brightness of the lights, play relaxing music, and perform other tasks.

[1619] This allows users to seamlessly enjoy personalized services, particularly those tailored to their emotional state.

[1620] Example 2

[1621] 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."

[1622] Conventional personalized service provision systems provide uniform services without considering the user's emotional state, making it difficult to increase true user satisfaction. It is also difficult to adjust services in real time according to the user's behavior and environment. Therefore, a system that can dynamically adjust services based on the user's emotional state is needed.

[1623] 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.

[1624] In this invention, the server includes a device means for transmitting a specific ID carried by the user, a means for the edge computer to receive the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with a personalized service using the information and generative AI model acquired by the edge computer, a means for the edge computer to incorporate an emotion engine and to analyze the user's facial expressions, voice, and biometric information to recognize the user's emotional state, and a means for dynamically adjusting the content of the service according to the emotional state, thereby enabling the provision of detailed services based on the user's emotional state.

[1625] "Device means for transmitting a specific ID carried by a user" refers to a device that can be carried by a user and is used to transmit unique identification information, and specific examples include RFID tags and NFC chips.

[1626] An "edge computer" is an advanced processing device that receives IDs and analyzes user information, and is responsible for relaying and analyzing data between the server and user devices.

[1627] "Server" refers to a central processing unit that retrieves user information and generative AI models from a database based on requests received from the edge computer and sends that information to the edge computer.

[1628] "Generative AI models" refer to algorithms or programs that are generated based on a user's past data and prompts and are used to provide personalized services.

[1629] "Emotion engine" refers to a software or hardware device that analyzes a user's facial expressions, voice, and biometric information to recognize their emotional state in real time.

[1630] "Means for analyzing a user's facial expressions, voice, and biometric information" refers to technology that uses cameras, microphones, and biometric sensors to determine a user's emotional state and utilizes that information to provide services.

[1631] "Means for dynamically adjusting service content" refers to a function that changes the content of the service provided in real time according to the user's emotional state, providing the user with the optimal experience.

[1632] The present invention is a system that provides personalized services to users by using a device that transmits a specific ID carried by the user, an edge computer, a server, and an emotion engine. The following describes in detail an embodiment of the present invention.

[1633] First, a user needs a device to carry with them. This device can be an RFID tag or an NFC chip, and transmits a unique ID. The user wears this device on their arm or in their pocket, and the device is constantly transmitting its ID.

[1634] Next is the edge computer. The edge computer has the ability to receive IDs sent from devices and a network interface for communicating with the server. This edge computer is equipped with an emotion engine that analyzes the user's facial expressions, voice, and biometric information, recognizes the user's emotional state in real time, and provides optimized services to the user based on the generative AI model obtained from the server.

[1635] The server is a central processing unit that manages the generated AI model and related data for each user. It searches for user information corresponding to the ID sent from the edge computer and returns it to the edge computer. The server performs authentication and encrypted communication to ensure security, enabling the management of user information and the provision of appropriate services.

[1636] The emotion engine uses a facial expression analysis camera, a voice analysis microphone, and biometric sensors to recognize the user's emotions in real time, and dynamically adjusts the service content based on the user's emotional state.

[1637] Take the case of a self-driving car as a specific example. When a user wears their device and gets into the self-driving car, the edge computer receives the device's ID and sends a request to the server. The server then sends back to the edge computer a generative AI model based on the user's driving history and preferences. The emotion engine analyzes the user's emotional state in real time; for example, if the user is nervous, the edge computer will adjust the driving style to be safer. The in-car environment is also optimized according to the user's emotional state, playing relaxing music or adjusting the air conditioning temperature.

[1638] In the case of a smart home, when a user wears a device and stands near the smart home device, the edge computer receives the device's ID and sends a request to the server. The server then sends the user's preferences and past behavioral history data back to the edge computer. The emotion engine recognizes the user's emotional state, and if the user is tired, for example, it will automatically lower the brightness of the lights or play relaxing music.

[1639] An example of a prompt sentence might be:

[1640] Example prompt to adjust driving style based on the user's emotional state when getting into a self-driving car:

[1641] "If the user is nervous, we want to adjust their driving style to be safer and play relaxing music."

[1642] Example prompt to adjust the environment based on the user's emotional state when near a smart home device:

[1643] "If the user is tired, we want to lower the lighting brightness and play relaxing music."

[1644] This allows users to seamlessly receive optimal services based on their preferences and emotional state without any special operations.

[1645] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1646] Step 1: User puts on the device

[1647] A user wears their device (e.g., an RFID tag or NFC chip) and configures it to transmit a specific ID.

[1648] Specific actions

[1649] A user wears an RFID tag as a watch and turns on the device, or carries an NFC chip.

[1650] input

[1651] A user-specific ID set on the device

[1652] output

[1653] The ID will be in a calling state.

[1654] Step 2: The user moves closer to the edge computer

[1655] The user moves within the communication range of the edge computer.

[1656] Specific actions

[1657] A user opens the door of a self-driving car and gets in, or enters the living room of a smart home.

[1658] input

[1659] User location information

[1660] output

[1661] The user's device ID comes within the communication range of the edge computer.

[1662] Step 3: Edge computer receives ID

[1663] The terminal's edge computer receives the ID sent from the device.

[1664] Specific actions

[1665] The ID receiving module in the edge computer captures the signal and decodes the ID.

[1666] input

[1667] Unique ID of the user originating from the device

[1668] output

[1669] The received user's ID data.

[1670] Step 4: The edge computer sends a request to the server

[1671] The device's edge computer sends a request to the server based on the received ID, requesting user information and a generated AI model.

[1672] Specific actions

[1673] Create request data including user ID and send it to the server along with authentication information using HTTPS protocol.

[1674] input

[1675] Received user ID data

[1676] output

[1677] The request data for the server.

[1678] Step 5: The server retrieves and sends the user information

[1679] The server analyzes the received request, searches the database for the relevant user information and generated AI model, and sends it back to the edge computer.

[1680] Specific actions

[1681] A query using the user ID as a key is executed against the database, and the obtained user information and generated AI model are packaged in a secure format and sent to the edge computer.

[1682] input

[1683] Request data for the server (including user ID)

[1684] output

[1685] Applicable user information and generated AI models.

[1686] Step 6: The edge computer analyzes the received data

[1687] The edge computer on the terminal receives the response from the server, extracts the necessary data, and analyzes it.

[1688] Specific actions

[1689] Analyzes the received data, loads the user profile and generated AI model into memory, and initializes necessary settings and parameters.

[1690] input

[1691] User information and generated AI model sent from the server

[1692] output

[1693] User profile and AI model loaded into memory.

[1694] Step 7: The edge computer uses the emotion engine to analyze the user's emotional state.

[1695] The emotion engine installed in the device's edge computer analyzes the user's facial expressions, voice, and biometric information to determine their emotional state in real time.

[1696] Specific actions

[1697] A facial expression analysis camera captures the user's facial expressions and uses image processing to recognize emotions, a voice analysis microphone analyzes the tone of the user's voice, and processes data from biometric sensors (e.g., heart rate monitors).

[1698] input

[1699] User's facial expressions, voice, and biometric information

[1700] output

[1701] User emotional state data.

[1702] Step 8: Provide optimized services to users

[1703] The device's edge computer provides personalized services based on the user's emotional state and profile information.

[1704] Specific actions

[1705] In a self-driving car, it adjusts the driving mode to help the user relax, and in a smart home, it changes the lighting and music settings.

[1706] input

[1707] User emotional state data, profile information, generative AI model

[1708] output

[1709] Implementing personalized services (e.g. adjusting driving modes, changing lighting settings).

[1710] (Application example 2)

[1711] 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."

[1712] In today's world, while services that utilize personal information and emotional data are on the rise, there is also a rapid demand for security systems to ensure user safety. However, current security systems rarely dynamically adjust based on the user's real-time emotional state, making it difficult to immediately sense a user's emotions and stress and take appropriate action. Furthermore, security systems often require users to wear specialized devices to perform emotion analysis tailored to each user's individual situation, which can be cumbersome or require additional specialized equipment. Therefore, there is a need for a system that can perform emotion analysis using a commonly carried device and provide optimal security services in real time.

[1713] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1714] In this invention, the server includes a device means for transmitting a specific ID carried by a user, an edge computer means for receiving the ID of the device means, a means for the edge computer to transmit the ID to the server and acquire information related to the user and a generative AI model, a means for providing the user with personalized services using the information and generative AI model acquired by the edge computer, a means for the edge computer to be installed on a smartphone and for an emotion engine to analyze the user's emotional state in real time using the smartphone's camera and microphone, and a means for dynamically adjusting the security level based on the user's emotional state and issuing an alert when necessary, thereby making it possible to provide optimal security services based on the user's real-time emotional state.

[1715] "Device means" refers to a device that a user carries and has the function of transmitting a specific ID.

[1716] An "edge computer" is a computer that receives the ID of a device means, communicates with a server to obtain information and generative AI models, and performs processing to provide personalized services to users.

[1717] The "server" is a system that manages user-related data and generated AI models based on the ID sent from the edge computer, and returns the necessary information while ensuring security.

[1718] A "generative AI model" is an artificial intelligence model that is generated based on user-specific data and is used to predict and respond to user behavior and preferences.

[1719] The "emotion engine" is a system that uses cameras, microphones, biometric sensors, etc. to analyze the user's facial expressions, voice, and biometric information, and recognizes the user's emotional state in real time.

[1720] A "smartphone" is a portable information device that is equipped with a camera, microphone, and various sensors and can have an emotion engine installed.

[1721] "Real-time analytics" is the process of instantly processing collected data to instantly determine a user's emotional or other state.

[1722] "Security level" indicates the degree of various responses and actions that the system provides to protect the safety of users.

[1723] "Alert sending" is a function that immediately sends notifications and warnings when it is determined that a user is in danger.

[1724] Detailed description of embodiments of the present invention will be given below. The overall configuration of the system includes a device carried by a user that has the function of transmitting a specific ID, an edge computer, a server, and an emotion engine. As a specific example, a real-time security system using a smartphone will be described.

[1725] System configuration

[1726] 1. Device Means

[1727] A user carries a device that transmits a specific ID, such as a smartwatch or smart card, that transmits the user's unique ID.

[1728] 2. Edge Computers

[1729] The edge computer is installed on a smartphone. The smartphone is equipped with a camera, microphone, and biometric sensors, and an emotion engine is installed. This engine is used to analyze the user's emotional state in real time. The analyzed emotion data is processed by the edge computer, and communication with the server is performed as necessary.

[1730] 3. Server

[1731] The server manages the generated AI model and related data for each user. It searches for data corresponding to the ID sent from the edge computer and returns it to the edge computer. The server is built using cloud services such as Google Cloud Platform (GCP) and Amazon Web Services (AWS). Communications are encrypted using secure protocols such as TLS.

[1732] 4. Emotion Engine

[1733] The emotion engine analyzes the user's facial expressions, voice, and biometric information. For example, it can use libraries such as Microsoft's Emotion API. This allows it to recognize the user's emotional state in real time and dynamically adjust the services provided according to the user's current emotional state.

[1734] Specific example of system operation

[1735] For real-time security monitoring systems

[1736] User Actions

[1737] First, the user wears a smartwatch or similar device and transmits an ID, which is received by a smartphone.

[1738] Edge computer operation

[1739] When the smartphone receives a specific ID, it sends a request to the server based on that ID to retrieve a generative AI model related to the user, while the emotion engine analyzes the user's emotional state using the smartphone's camera, microphone, and biometric sensors.

[1740] Server Operation

[1741] When the server receives a request from the edge computer, it searches the database for relevant user information and the generated AI model and sends it back to the edge computer. The server ensures security by using encrypted communications.

[1742] Actions based on the user's emotional state

[1743] If the emotion engine detects a user's emotional state (e.g., stress or fear), the edge computer automatically adjusts the security level and issues alerts as needed, which are then sent to emergency contacts using APIs such as Twilio.

[1744] Prompt Sentence Examples

[1745] "A system that analyzes a user's emotional state in real time and issues an alert if a specific emotion is detected."

[1746] This system allows users to enjoy real-time security services based on their emotional state, enabling them to take appropriate action even in sudden stress or fear situations.

[1747] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1748] Step 1:

[1749] A user wears a device such as a smart watch or smart card that transmits a specific ID. The ID transmitted from this device is verified to be unique to the user.

[1750] Input: A device with a user-specific ID

[1751] Output: A specific ID is sent to your smartphone.

[1752] Specific operation: The user wears a smartwatch, which transmits its ID to those around it.

[1753] Step 2:

[1754] The smartphone (edge ​​computer) receives the ID sent from the device. After receiving the ID, it sends a request for a generative AI model to the server based on that ID.

[1755] Input: Device ID

[1756] Output: Generated AI model request to the server

[1757] Specific operation: The smartphone receives the ID via Bluetooth or NFC and sends a request including that ID to the server.

[1758] Step 3:

[1759] The server receives a request from the edge computer, searches for the user data and generated AI model corresponding to the ID, and once the search is complete, it returns the results to the edge computer.

[1760] Input: A request containing an ID

[1761] Output: User data and generative AI model

[1762] How it works: The server searches the database for relevant information and sends the results, including the generative AI model, back to the edge computer.

[1763] Step 4:

[1764] The edge computer prepares to provide personalized services to users based on the generated AI model and user information received from the server, while the smartphone's emotion engine analyzes the user's facial expressions, voice, and biometric information in real time.

[1765] Input: Generative AI model and user information

[1766] Output: Preparation for providing individual services

[1767] How it works: Your smartphone collects data using the camera, microphone, and biometric sensors, which the emotion engine analyzes.

[1768] Step 5:

[1769] The emotion engine analyzes the user's emotional state in real time and dynamically adjusts the security level based on the results. If the user is in a state of stress or fear, the edge computer will send an alert.

[1770] Input: Sentiment analysis data

[1771] Output: Dynamically adjusted security levels and alerts

[1772] Specific operation: The emotion engine performs facial expression and voice analysis, and if an abnormality is detected, an alert is sent to an emergency contact using the Twilio API, etc.

[1773] In this way, a system can be realized that monitors the emotional state of a user in real time and dynamically provides necessary security measures.

[1774] 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.

[1775] 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.

[1776] 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.

[1777] 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.

[1778] 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.

[1779] 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.

[1780] 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).

[1781] 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.

[1782] 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."

[1783] 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.

[1784] 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).

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1791] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1792] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1793] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1794] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1795] The following is further disclosed regarding the above embodiment.

[1796] (Claim 1)

[1797] A device means for transmitting a specific ID carried by a user;

[1798] A means for an edge computer to receive an ID of the device means;

[1799] A means for the edge computer to transmit the ID to a server and obtain information related to the user and a generating AI model;

[1800] A means for providing a user with personalized services using the information acquired by the edge computer and the generated AI model;

[1801] A system including:

[1802] (Claim 2)

[1803] 10. The system of claim 1, wherein the edge computer is installed in an autonomous vehicle and further comprises means for adjusting autonomous driving patterns and in-vehicle environment to suit user preferences using information related to the user and the generative AI model.

[1804] (Claim 3)

[1805] 10. The system of claim 1, wherein the edge computer is connected to smart home devices, and further comprising means for automatically adjusting settings and device operation within the smart home using information related to the user and the generative AI model.

[1806] "Example 1"

[1807] (Claim 1)

[1808] A device means for transmitting a specific ID carried by a user;

[1809] A means for an edge computer to receive an ID of the device means;

[1810] A means for the edge computer to transmit the ID to a server and obtain information related to the user and a generating AI model;

[1811] A means for performing real-time processing using the information acquired by the edge computer and the generated AI model to provide personalized services to users;

[1812] A system including:

[1813] (Claim 2)

[1814] The system of claim 1, wherein the edge computer is installed in an autonomous vehicle and further comprises means for optimizing a driving route and automatically adjusting an in-vehicle environment using information related to the user and a generative AI model.

[1815] (Claim 3)

[1816] 10. The system of claim 1, wherein the edge computer is connected to a smart home system, and further comprises means for automatically controlling smart home devices such as lighting, air conditioning, and entertainment devices using information related to the user and the generative AI model.

[1817] "Application Example 1"

[1818] (Claim 1)

[1819] A device means for transmitting a specific ID carried by a user;

[1820] A means for an edge computer to receive an ID of the device means;

[1821] A means for the edge computer to transmit the ID to a server and obtain information related to the user and a generating AI model;

[1822] A means for providing a user with personalized services using the information acquired by the edge computer and the generated AI model;

[1823] means for communicating with a server via the edge computer when a user enters a store and providing product suggestions and benefit information in real time based on the user's past purchase history and preferences;

[1824] A system including:

[1825] (Claim 2)

[1826] 10. The system of claim 1, wherein the edge computer is installed in an autonomous vehicle and further comprises means for adjusting autonomous driving patterns and in-vehicle environment to suit user preferences using information related to the user and the generative AI model.

[1827] (Claim 3)

[1828] 10. The system of claim 1, wherein the edge computer is connected to smart home devices, and further comprising means for automatically adjusting settings and device operation within the smart home using information related to the user and the generative AI model.

[1829] "Example 2: Combining Emotion Engines"

[1830] (Claim 1)

[1831] A device means for transmitting a specific ID carried by a user;

[1832] A means for an edge computer to receive an ID of the device means;

[1833] A means for the edge computer to transmit the ID to a server and obtain information related to the user and a generating AI model;

[1834] A means for providing a user with personalized services using the information acquired by the edge computer and the generated AI model;

[1835] The edge computer is equipped with an emotion engine and is configured to analyze a user's facial expression, voice, and biological information to recognize the user's emotional state;

[1836] means for dynamically adjusting service content according to said emotional state;

[1837] A system including:

[1838] (Claim 2)

[1839] 10. The system of claim 1, wherein the edge computer is installed in an autonomous vehicle and further comprises means for adjusting autonomous driving patterns and in-vehicle environment to suit user preferences using information related to the user and the generative AI model.

[1840] (Claim 3)

[1841] 10. The system of claim 1, wherein the edge computer is connected to smart home devices, and further comprising means for automatically adjusting settings and device operation within the smart home using information related to the user and the generative AI model.

[1842] "Application example 2 when combining emotion engines"

[1843] (Claim 1)

[1844] A device means for transmitting a specific ID carried by a user;

[1845] A means for an edge computer to receive an ID of the device means;

[1846] A means for the edge computer to transmit the ID to a server and obtain information related to the user and a generating AI model;

[1847] A means for providing a user with personalized services using the information acquired by the edge computer and the generated AI model;

[1848] The edge computer is installed on a smartphone, and an emotion engine analyzes the user's emotional state in real time using a camera or a microphone of the smartphone;

[1849] A means for dynamically adjusting the security level based on the user's emotional state and issuing an alert when necessary;

[1850] A system including:

[1851] (Claim 2)

[1852] 10. The system of claim 1, wherein the edge computer is installed in an autonomous vehicle and further comprises means for adjusting autonomous driving patterns and in-vehicle environment to suit user preferences using information related to the user and the generative AI model.

[1853] (Claim 3)

[1854] 10. The system of claim 1, wherein the edge computer is connected to smart home devices, and further comprising means for automatically adjusting settings and device operation within the smart home using information related to the user and the generative AI model. [Explanation of symbols]

[1855] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A device means for transmitting a specific ID carried by a user; A means for an edge computer to receive an ID of the device means; A means for the edge computer to transmit the ID to a server and obtain information related to the user and a generating AI model; A means for providing a user with personalized services using the information acquired by the edge computer and the generated AI model; A system including:

2. 2. The system of claim 1, wherein the edge computer is installed in an autonomous vehicle, and further comprises means for adjusting autonomous driving patterns and in-vehicle environment to suit user preferences using information related to the user and the generative AI model.

3. 10. The system of claim 1, wherein the edge computer is connected to smart home devices, and further comprising means for automatically adjusting settings and device operation within the smart home using information related to the user and the generative AI model.

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