System

Abstract

A system is provided.SOLUTION: This system is provided with a terminal for inputting and transmitting the health data of a user, a server for receiving, storing and analyzing the health data of the user, and a server for acquiring and storing the latest merchandise information from a trend merchandise database on the Internet, and executes a matching algorithm based on the health data of the user and the acquired trend merchandise information. A system comprising: a server for selecting an optimal product for an individual user; a server for listing the selected product and sending it to the user's terminal; and a server for receiving the user's feedback, updating a database, and running a machine learning algorithm to improve the accuracy of future recommendations.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] As our society ages, it is becoming increasingly difficult for users (especially the elderly) to choose products that best suit their health condition. In particular, when elderly people try to incorporate new trendy products, their choices may have a negative impact on their health, so they must choose products that take their health condition into consideration. However, it is not easy for elderly people to make this decision on their own. Under these circumstances, there is a need for a system that allows elderly people to enjoy the latest trends while maintaining their health. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a terminal for inputting and transmitting a user's health data, a server for receiving, storing, and analyzing the user's health data, a server for acquiring and storing the latest product information from a trend product database on the Internet, a server for executing a matching algorithm based on the user's health data and the acquired trend product information to select the most suitable products for each user, a server for listing the selected products and sending the list to the user's terminal, and a server for receiving user feedback, updating the database, and executing a machine learning algorithm to improve the accuracy of suggestions from the next time onwards.

[0006] A "terminal" is an electronic device that allows a user to input health data and transmit it to a server.

[0007] "Health data" refers to biometric information and health checkup results that indicate the user's health condition.

[0008] The "server" is a computer system that receives, stores, and analyzes health data, and retrieves and stores the latest product information from a trending product database on the Internet.

[0009] The "trend product database" is a database for collecting and storing the latest product information.

[0010] The "matching algorithm" is an algorithm that selects the most suitable product for the user based on health data and trending product information.

[0011] A "shopping list" is a list of products selected by a matching algorithm.

[0012] "Feedback" refers to the user's ratings and opinions regarding the products they have purchased.

[0013] A "machine learning algorithm" is an algorithm that uses feedback data to improve the accuracy of the system's suggestions. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention relates to a system for proposing the latest trend products based on a user's health data. Hereinafter, an embodiment of the present invention will be described.

[0036] This system is mainly composed of a user's "terminal" and a linked "server." The terminal and server communicate via the Internet.

[0037] First, the user inputs their own health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[0038] The server receives, stores, and analyzes the health data. The server then retrieves and stores the latest product information from trending product databases on the Internet and from e-commerce site APIs. The retrieved product information is organized by category.

[0039] The server then runs a matching algorithm based on the health data and the retrieved trending product information, selecting, for example, low-salt foods for a user with high blood pressure or low-impact fitness equipment for a user with arthritis.

[0040] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[0041] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[0042] As a concrete example, let's say Person A has high blood pressure and arthritis. Person A enters their blood pressure and the condition of their joints into their device every day. The device sends this data to a server, which analyzes Person A's data and suggests low-salt foods and fitness equipment that puts less strain on the joints. The suggested products are listed and sent to Person A's device, where Person A can check the list and make a purchase. Person A then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[0043] This completes the embodiment of the present invention. This system allows users to enjoy new trendy products while maintaining their health.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) into the terminal.

[0047] Step 2:

[0048] The terminal encrypts the entered health data and sends it to the server.

[0049] Step 3:

[0050] The server receives the transmitted health data and stores it in a database.

[0051] Step 4:

[0052] The server analyzes the health data and evaluates the user's health status.

[0053] Step 5:

[0054] The server retrieves the latest product information from trending product databases on the Internet and from e-commerce site APIs.

[0055] Step 6:

[0056] The server organizes the acquired product information by category and stores it in a database.

[0057] Step 7:

[0058] The server runs a matching algorithm based on the user's health data and organized trend product information.

[0059] Step 8:

[0060] The server selects products that are best suited to the user's health condition and generates a shopping list.

[0061] Step 9:

[0062] The server transmits the generated shopping list to the terminal.

[0063] Step 10:

[0064] The terminal notifies the user of the received shopping list.

[0065] Step 11:

[0066] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[0067] Step 12:

[0068] The user inputs feedback on the purchased product into the terminal.

[0069] Step 13:

[0070] The terminal sends the feedback to the server.

[0071] Step 14:

[0072] The server updates the database based on the feedback received.

[0073] Step 15:

[0074] The server uses machine learning algorithms to analyze the data and improve the accuracy of future suggestions.

[0075] The above is a specific processing flow in the system of the present invention.

[0076] Example 1

[0077] 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."

[0078] While conventional health management systems can collect users' health data, they lack the functionality to suggest products tailored to each individual user based on that data. This results in users spending a great deal of time and effort to find the products they need. Furthermore, the list of suggested products does not necessarily meet the user's needs, resulting in low user satisfaction. Furthermore, the implementation of machine learning algorithms to effectively collect user feedback and improve the accuracy of future suggestions is insufficient. A new system to solve this issue is needed.

[0079] 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.

[0080] In this invention, the server includes a device for inputting and transmitting biometric information; a device for receiving, storing, and analyzing the biometric information; and a device for acquiring and storing the latest product information from a database. This enables optimal product recommendations based on the user's health data. Furthermore, the conventional problem can be effectively solved by combining a means for performing a matching process, listing selected products, and sending them to the user's device, and a device for receiving user feedback, updating the database, and executing an algorithm to improve the accuracy of future recommendations. Specifically, by incorporating a means for organizing and storing the latest product information by category, the accuracy of recommending optimal products to each individual user is improved. Furthermore, by transmitting and receiving data using encrypted communications, personal information protection and data security are enhanced. This allows users to efficiently discover and purchase the latest trendy products while maintaining their health.

[0081] "Biometric information" refers to data related to the user's health condition, such as heart rate, blood pressure, and blood sugar level.

[0082] "Device" refers to equipment that allows a user to input, send, receive, and display biometric information.

[0083] A "server" is a computer system that communicates with the device via the Internet, receives, stores, and analyzes biometric information, and acquires and manages product information.

[0084] A "database" is a storage system that allows a server to systematically store and manage information.

[0085] "Matching processing" is a process of selecting products that are suitable for the user's biometric information using a matching algorithm.

[0086] "Product information" is information about the latest trending products obtained from the database and organized by category.

[0087] "Listing" is the process by which the server organizes the selected products into an easy-to-read format so that the user can view them.

[0088] An "algorithm" is a set of calculation procedures or rules that run on a server and are used for data analysis, matching, and machine learning.

[0089] "Encrypted communication" is a technology that encrypts data in order to securely send and receive data over the Internet.

[0090] The present invention relates to a system that proposes optimal products based on a user's biometric information. The following describes an embodiment of the present invention. This system is primarily composed of a user's "terminal" and a linked "server." The terminal and server communicate with each other via the Internet.

[0091] Hardware configuration

[0092] Device: A device used by the user, such as a smartphone or tablet, on which a dedicated health management application is installed.

[0093] Server: A high-performance cloud server will be used to analyze data, execute matching algorithms, and manage the database. For example, AWS or Google Cloud can be used.

[0094] Software configuration

[0095] Terminal application: Has the function to input and transmit the user's biometric information. Data is transmitted securely using encrypted communication (HTTPS).

[0096] Server-side program: Consists of the following modules:

[0097] 1. Data Reception Module: Receives and decodes biometric information sent from the device. It uses an encrypted communication protocol (AES-256).

[0098] 2. Data analysis module: Analyzes the received data and stores it in a database.

[0099] 3. Product information acquisition module: Acquires the latest product information from a trending product database or an e-commerce site API (e.g., Amazon API, Rakuten API).

[0100] 4. Matching module: Executes a matching algorithm (e.g., KNN: nearest neighbor algorithm) based on biometric information and product information to select the most suitable product for each individual user.

[0101] 5. List generation module: Compiles the selected products into a shopping list, converts it into JSON format, and sends it to the terminal.

[0102] 6. Feedback collection module: Receives user feedback and updates the database. It uses machine learning algorithms (e.g., random forest) to improve the accuracy of future suggestions.

[0103] System Operation

[0104] 1. User biometric information input and transmission:

[0105] A user uses a dedicated health management app to input biometric information such as heart rate, blood pressure, and blood glucose level. For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[0106] The input data is encrypted within the terminal and sent to the server.

[0107] 2. Server data reception and analysis:

[0108] The server decrypts the received encrypted data and stores it in a database.

[0109] At the same time, the latest trending product information is obtained from databases and APIs on the Internet, and is organized and stored by category.

[0110] 3. Matching biometric information with product information:

[0111] The server selects the most suitable product based on biometric and product information, for example, recommending low-salt foods to a user with high blood pressure and fitness products that are gentle on the joints to a user with arthritis.

[0112] 4. Shopping list generation and notification:

[0113] The server creates a list of selected products, converts it into JSON format, and sends it to the terminal.

[0114] The terminal receives the list and notifies the user that a new product list has arrived.

[0115] 5. User feedback and database updates:

[0116] The user inputs feedback about the purchased product and sends it to the server from the terminal. For example, the user might say, "The low-salt food tasted good, but I'd like the packaging to be improved."

[0117] The server receives the feedback and uses machine learning algorithms to update the database and improve the accuracy of the suggestions.

[0118] Prompt Sentence Examples

[0119] "Based on my health data (blood pressure: 130 / 85, joint pain level: 4), please suggest the latest low-sodium foods and joint-friendly fitness gear."

[0120] This allows users to efficiently discover new trendy products and maintain their health. The seamless process of inputting biometric information, suggesting products, and collecting feedback also increases user satisfaction.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Enter and send user biometric information

[0123] A user uses a dedicated health management app to input biometric information (e.g., heart rate, blood pressure, blood glucose level, etc.). For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[0124] Input: Biometric information such as heart rate, blood pressure, and blood sugar level

[0125] The device encrypts this data using the AES-256 encryption algorithm and sends it to the server using HTTPS.

[0126] Output: Encrypted biometric information

[0127] Step 2: Receiving and storing biometric information on the server

[0128] The server receives the encrypted data sent from the terminal and ensures security using TLS (Transport Layer Security).

[0129] Input: Encrypted biometric information

[0130] The server decrypts the received data using AES-256 and stores the decrypted data in a health database.

[0131] Output: Stored biometric information

[0132] Step 3: Obtain and organize trending product information

[0133] The server periodically retrieves the latest product information from trending product databases on the Internet or from e-commerce site APIs, such as Amazon API or Rakuten API.

[0134] Input: Latest product information from trending product databases and e-commerce site APIs

[0135] The server classifies the acquired product information by category and stores it in a product database.

[0136] Output: Product information organized by category

[0137] Step 4: Matching biometric information with product information

[0138] The server runs a matching algorithm based on the stored biometric information and product information, using KNN (Kind Neighbor Neighborhood Algorithm) to select the product that best suits the user's needs.

[0139] Input: Stored biometric information, product information organized by category

[0140] Output: A list of products that are best suited for the user

[0141] Step 5: Generate and submit your shopping list

[0142] The server compiles the selected items into a shopping list and converts it into JSON format.

[0143] Input: A list of products that are best suited for the user

[0144] The server sends the shopping list in JSON format to the device using HTTPS.

[0145] Output: Shopping list in JSON format

[0146] Step 6: Shopping list notification and purchase process

[0147] The device displays the shopping list received from the server on the user interface (UI), for example, displaying product images, descriptions, prices, purchase buttons, etc. on the app screen.

[0148] Input: Shopping list in JSON format

[0149] The user reviews the list, selects the products they are interested in, and a purchase link to the e-commerce site is generated.

[0150] Output: User selected products and corresponding purchase links

[0151] Step 7: User feedback and submission

[0152] The user inputs feedback about the purchased product into the terminal. For example, the user inputs feedback such as "The low-salt food tasted good, but I would like the packaging to be improved."

[0153] Input: User feedback

[0154] The device encrypts the feedback data and sends it to the server using HTTPS.

[0155] Output: Encrypted feedback data

[0156] Step 8: Receive feedback and update the database

[0157] The server receives the encrypted feedback sent from the device and decrypts it using AES-256.

[0158] Input: Encrypted feedback data

[0159] The decoded data is stored in a feedback database and analyzed using machine learning algorithms to improve the accuracy of future suggestions.

[0160] Output: Updated feedback data, learning results for improved accuracy

[0161] The above are the specific processing steps of the system program. This system allows users to efficiently discover new trend products and manage their health.

[0162] (Application example 1)

[0163] 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."

[0164] In today's world, many consumers are concerned about their health, but there are only a limited number of systems that provide optimal products to individual users based on their health data. Consumers have difficulty finding the right products for them from the vast number of products available, and there is also a lack of information about new trendy products. For this reason, there is a need for a system that recommends optimal health and trendy products to individual users based on their health data, and allows them to seamlessly purchase them.

[0165] 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.

[0166] In this invention, the server includes a terminal with a function that allows the user to purchase suggested products, a means for executing a matching algorithm based on health data and product information, a means for organizing and saving the acquired trend product information by category, and a means for notifying the user of a shopping list, thereby enabling the user to easily find and purchase the most suitable products based on their own health data.

[0167] A "terminal" is a device for inputting a user's health data and sending it to a server.

[0168] The "server" is a system that receives, stores, and analyzes users' health data, and also obtains the latest product information from a trending product database on the Internet.

[0169] The "Internet Trending Product Database" is an online database that collects the latest product information.

[0170] The "matching algorithm" is a calculation method for selecting the most suitable products for each individual user based on the user's health data and acquired trend product information.

[0171] A "shopping list" is a list of selected products that is sent to the user's terminal.

[0172] "Feedback" is information that allows users to input their impressions and ratings of purchased products.

[0173] A "machine learning algorithm" is an algorithm that updates the database based on user feedback and improves the accuracy of suggestions from the next time onwards.

[0174] "Organizing by category" means classifying and saving the acquired trending product information by type.

[0175] "Encrypted communication" is a method of communicating the input, transmission, and reception of a user's health data as encrypted data between the terminal and the server.

[0176] The "function to purchase products suggested to the user" is a function that allows the user to select the suggested products on the device and complete the purchase procedure.

[0177] MODE FOR CARRYING OUT THE INVENTION

[0178] The present invention relates to a system that suggests the latest trend products based on a user's health data, and detailed embodiments thereof will be described below.

[0179] System configuration

[0180] This system mainly consists of the following elements:

[0181] 1. Terminal: A device for inputting the user's health data and sending it to the server. In this system, a smartphone is used.

[0182] 2. Server: This system receives, stores, and analyzes users' health data. It also retrieves and stores the latest product information from a trending product database on the Internet.

[0183] 3. Online trending product database: An online database that collects the latest product information.

[0184] 4. Matching algorithm: A calculation method for selecting the most suitable product for each individual user based on the user's health data and acquired trend product information.

[0185] 5. Shopping list: A list of selected products is created and sent to the user's device.

[0186] 6. Feedback: Information for users to enter their impressions and ratings of the products they have purchased.

[0187] 7. Machine learning algorithm: This algorithm updates the database based on user feedback and improves the accuracy of future suggestions.

[0188] 8. A means of organizing by category: This is a means of classifying and saving the acquired trending product information by type.

[0189] 9. Encrypted communication: A method in which the input, transmission, and reception of a user's health data is communicated as encrypted data between the terminal and the server.

[0190] 10. Ability to purchase products suggested to users: This function allows users to select suggested products on their device and complete the purchase process.

[0191] Implementation method

[0192] The user enters their own health data into the device. The entered health data is encrypted and sent to a server via the Internet using the Fernet encryption method. The server receives, stores, and analyzes this data. The server also retrieves and stores the latest product information from a trending product database on the Internet and from e-commerce site APIs.

[0193] The server organizes and stores the acquired product information by category. The server then runs a matching algorithm based on the health data and product information to select the most suitable products for each individual user. The selected products are then compiled into a shopping list and sent to the user's device.

[0194] Users receive this shopping list on their device and can select and purchase items that interest them. The device also has a built-in function for carrying out the purchase process. Furthermore, if the user enters feedback on the purchased item, it is sent to the server. The server updates the database based on this feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[0195] Specific examples

[0196] For example, if a user has high blood pressure and arthritis, the user enters their blood pressure and the condition of their joints into their smartphone every day. The device encrypts this data and sends it to a server. The server analyzes the data and suggests low-salt foods and fitness equipment that are gentle on the joints that are best suited to the user. A list of suggested products is then sent to the user's device. The user can review the list and purchase any products that interest them. After that, the user can enter their thoughts on the products, and the feedback is sent to the server, which updates the database.

[0197] Prompt Sentence Examples

[0198] We would like to build a system that recommends the latest trending products based on a user's health data (heart rate, blood pressure, blood sugar level, etc.). The relevant data will be obtained from an online trending product database or an e-commerce site API, and will be compared with the health data to suggest products. Please provide an algorithm that will encrypt the JSON-formatted health data using Fernet and send it to the server.

[0199] This invention allows users to easily find and purchase the latest trending products while maintaining their health. The server constantly analyzes product information and health data to improve the accuracy of recommendations, providing users with the best options.

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] The user inputs health data into the device, such as heart rate, blood pressure, and blood sugar levels, through a smartphone application, and the data is converted into JSON format.

[0203] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[0204] Output: Health data in JSON format

[0205] Step 2:

[0206] The device encrypts the entered health data using the Fernet encryption method, and the data is protected by an encryption key.

[0207] Input: Health data in JSON format, encryption key

[0208] Output: Encrypted health data (binary format)

[0209] Step 3:

[0210] The device sends the encrypted health data to the server via a POST request over the internet.

[0211] Input: Encrypted health data

[0212] Output: Data transmission status to the server (success / failure)

[0213] Step 4:

[0214] The server stores the received health data and performs analysis, processing the data to evaluate the user's health status.

[0215] Input: Encrypted health data

[0216] Output: Analysis results (health status evaluation data)

[0217] Step 5:

[0218] The server retrieves and stores the latest product information from a trending product database on the Internet. Product information is retrieved using an API and organized by category.

[0219] Input: API request (endpoint and parameters)

[0220] Output: Product information organized by category

[0221] Step 6:

[0222] The server runs a matching algorithm based on health data and product information to select the best products for each individual user. The algorithm filters out appropriate products based on health status.

[0223] Input: Analysis results, product information

[0224] Output: A list of selected products

[0225] Step 7:

[0226] The server creates a shopping list of the selected items and sends it to the user's device. The list is then displayed on the device via a notification function.

[0227] Input: List of selected products

[0228] Output: Send shopping list to device (notification)

[0229] Step 8:

[0230] The user checks the shopping list, selects the items they are interested in, and purchases them. The purchase process is completed through the device application.

[0231] Input: Shopping list

[0232] Output: Purchase completion status

[0233] Step 9:

[0234] Users enter feedback on the product they have purchased, and the device sends that feedback to the server, where the ratings and comments are encrypted again and sent to the server.

[0235] Input: User feedback

[0236] Output: Feedback data sent to server

[0237] Step 10:

[0238] The server updates the database based on the feedback it receives and uses machine learning algorithms to improve the accuracy of future suggestions.

[0239] Input: Feedback data

[0240] Output: Updated database, improved proposed algorithm

[0241] 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.

[0242] The present invention relates to a system for proposing the latest trend products based on health data and emotion data of a user. Hereinafter, an embodiment of the present invention will be described.

[0243] This system is primarily composed of the user's "terminal," a linked "server," and an "emotion engine" that recognizes the user's emotions. The terminal and server communicate via the Internet, and the emotion engine is installed on either the terminal or the server.

[0244] First, the user inputs health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[0245] The server receives the health data, stores it in a database, and analyzes it. In parallel, the emotion engine analyzes the user's emotions from facial expressions, voice, text input, etc., and sends the emotion data to the server.

[0246] Next, the server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[0247] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure who is stressed might be matched with low-salt foods and relaxation products.

[0248] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[0249] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[0250] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[0251] The above is an embodiment of the present invention. This system allows users to maintain their health, improve their mental stability, and enjoy new trendy products.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) and emotional data (text, voice, facial expression, etc.) into the terminal.

[0255] Step 2:

[0256] The terminal encrypts the input health data and emotion data and transmits them to the server.

[0257] Step 3:

[0258] The server receives the transmitted health data and emotion data and stores them in respective databases.

[0259] Step 4:

[0260] The server analyzes the health data to assess the user's health status.

[0261] Step 5:

[0262] An emotion engine analyzes the transmitted emotion data and evaluates the user's emotional state.

[0263] Step 6:

[0264] The server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[0265] Step 7:

[0266] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure and stress might be recommended low-salt foods and relaxation products.

[0267] Step 8:

[0268] The server generates a shopping list of the selected items and transmits it to the user's terminal.

[0269] Step 9:

[0270] The terminal notifies the user of the received shopping list.

[0271] Step 10:

[0272] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[0273] Step 11:

[0274] The user inputs feedback about the purchased item into the terminal.

[0275] Step 12:

[0276] The terminal sends the feedback to the server.

[0277] Step 13:

[0278] The server updates the database based on the feedback received.

[0279] Step 14:

[0280] The server uses machine learning algorithms to analyze the feedback data and improve the accuracy of future suggestions.

[0281] The above is a specific processing flow in the system of the present invention.

[0282] Example 2

[0283] 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."

[0284] Conventional health management systems only utilize users' health data and do not take into account their emotional data when making suggestions. This makes it difficult to suggest products that are appropriate for the user's mental state, making it difficult to improve overall satisfaction. Furthermore, the accuracy of trending product suggestions is low, and there is a lack of a mechanism for effectively incorporating user feedback into future suggestions. There is a need to solve these problems and improve the quality of product suggestions to users.

[0285] 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.

[0286] In this invention, the server includes means for receiving, storing, and analyzing the user's health data, means for acquiring and storing information from the latest product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data to select the most suitable product for each user. This makes it possible to propose the most suitable product from trending products, taking into consideration the user's health and emotional state comprehensively.

[0287] "Health data" refers to information that represents a user's physical condition, such as heart rate, blood pressure, and blood sugar level.

[0288] "Device" means an electronic device used by a user to input and receive health data and emotional data.

[0289] "Server" means a central computer system that stores and analyzes received data, and retrieves and suggests product information.

[0290] The "emotion analysis engine" is a function for analyzing emotional data from users' facial expressions, voice, text input, etc.

[0291] The "Trend Product Database" is a database that stores the latest product information available on the Internet.

[0292] A "matching algorithm" is a calculation method for selecting the most suitable product based on the user's health and emotional data.

[0293] A "machine learning algorithm" is a self-learning algorithm that improves the accuracy of suggestions based on feedback from users.

[0294] "Encrypted communication" is a method of communicating by encrypting information in order to send and receive data safely.

[0295] "Feedback" refers to information such as ratings and impressions provided by users regarding purchased products.

[0296] A "shopping list" is a list that displays selected products in a list format so that the user can check and purchase them.

[0297] The present invention relates to a system that suggests the latest trending products based on a user's health data and emotional data. The system is primarily composed of the user's "terminal," a linked "server," and an "emotion analysis engine" that recognizes the user's emotions. The following describes in detail the mode for carrying out the invention.

[0298] First, the user inputs their health data (e.g., heart rate, blood pressure, blood sugar level) using a dedicated application. The device then encrypts the input health data using the AES-256 encryption algorithm and transmits it to a server via the Internet.

[0299] The server decrypts the received health data and stores it in a health data database, which is organized for each user and comprehensively manages the health status of the entire system.

[0300] In parallel, the emotion analysis engine collects and analyzes emotional data in real time from the user's facial expressions, voice, and text input. This emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology. The obtained emotional data is stored on the device or server.

[0301] The server accesses the latest product databases on the Internet and the APIs of e-commerce sites to obtain the latest product information. This information is also organized by category and stored in the trend product database.

[0302] Next, the server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the user's health data and emotion data to select the most suitable products for the user. The selected products are compiled into a shopping list and sent to the device. The device receives this shopping list and notifies the user.

[0303] The user can check the list of suggested products on the terminal, select the product they are interested in, and purchase it. After completing the purchase procedure, the user provides feedback on the purchased product, which is then sent back to the server via the terminal.

[0304] The server stores the received feedback in a database, analyzes it, and uses machine learning algorithms to improve the accuracy of future suggestions.

[0305] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[0306] Examples of prompts include:

[0307] - "Please explain the algorithm of the system that suggests low-salt foods and relaxation products to users who are stressed due to high blood pressure."

[0308] - "Explain the overall flow of a system that suggests trending products suitable for users based on their health data (e.g., heart rate, blood pressure, blood sugar level) and emotional data (facial expression, voice, text)."

[0309] This system allows users to enjoy the latest trendy products while maintaining their health and mental well-being.

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Step 1:

[0312] The user enters health data (e.g., heart rate, blood pressure, blood glucose level) using a dedicated application. The entered data is collected in form fields in the application. The entered data is then stored on the device.

[0313] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[0314] Output: Health data stored on the device before encryption

[0315] Step 2:

[0316] The terminal encrypts the entered health data using the AES-256 encryption algorithm in order to send the data securely to the server.

[0317] Input: Health data before encryption

[0318] Output: Encrypted health data

[0319] Step 3:

[0320] The device transmits the encrypted health data to the server via the Internet. This transmission operation is to ensure that the data reaches the server while maintaining its confidentiality.

[0321] Input: Encrypted health data

[0322] Output: A request to send data to the server

[0323] Step 4:

[0324] The server decrypts the received data from its encrypted state. The decrypted data is stored in a health data database. The data is organized for each user.

[0325] Input: Encrypted health data

[0326] Output: Decrypted health data, stored in a database

[0327] Step 5:

[0328] In parallel, the emotion analysis engine collects the user's facial expressions, voice, and text input in real time. By analyzing this data, the emotion analysis engine identifies the user's emotional state. The emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology.

[0329] Input: User's facial expression data, voice data, text data

[0330] Output: Parsed emotion data

[0331] Step 6:

[0332] The emotion analysis engine sends the analyzed emotion data to the server, where it is stored in an emotion database.

[0333] Input: Parsed emotion data

[0334] Output: Send data to the server and save it in the emotion database

[0335] Step 7:

[0336] The server accesses the latest product databases on the Internet and EC site APIs to obtain the latest product information. The obtained data is organized by category and saved in a trending product database.

[0337] Input: API request

[0338] Output: Latest product information, saved in trend product database

[0339] Step 8:

[0340] The server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the stored health data and emotion data, and the best product for the user is selected.

[0341] Input: Health data, emotion data

[0342] Output: Selected product information

[0343] Step 9:

[0344] The server compiles the selected product information into a shopping list and sends it to the terminal, which receives the shopping list and notifies the user.

[0345] Input: Selected product information

[0346] Output: Shopping list, notifications to device

[0347] Step 10:

[0348] The user checks the shopping list on the device, selects the products they are interested in, and then completes the purchase process. The purchase information is saved on the device.

[0349] Input: Shopping list

[0350] Output: Purchased product information, saved on the device

[0351] Step 11:

[0352] When a user inputs feedback on a product, the terminal transmits the feedback to the server, and the feedback data is stored in the database of the server.

[0353] Input: User feedback

[0354] Output: Send data to server, save feedback in database

[0355] Step 12:

[0356] The server analyzes the received feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[0357] Input: Feedback data

[0358] Output: Updated machine learning model, improved suggestion accuracy

[0359] This processing step allows users to efficiently receive recommendations for trending products that fit their health and emotional state, and allows post-purchase feedback to be reflected in future recommendations.

[0360] (Application example 2)

[0361] 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."

[0362] Conventional systems only suggest products based on the user's health data, and are unable to consider the user's emotional state when making product suggestions, making it difficult to fully meet the user's needs. Furthermore, the accuracy of the feedback system used to improve the suitability of the suggested products is limited, preventing the system from increasing user satisfaction.

[0363] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving, saving, and analyzing the user's health data and emotional data, means for acquiring and saving the latest product information from a trend product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data and the acquired trend product information to select the product that is best suited to each user. This makes it possible to suggest products that are more suitable for each user, taking into account not only the user's health condition but also their emotional state.

[0364] A "terminal" is a device that allows a user to input their own health data and emotional data and transmit this to a server.

[0365] The "server" is a device that stores and analyzes the user's health data and emotional data received from the terminal, and processes product selection and user feedback.

[0366] "Emotional data" refers to data relating to a user's emotional state analyzed from the user's facial expressions, voice, text input, etc.

[0367] The "trend product database" is a database that manages the latest product information obtained from the Internet.

[0368] The "matching algorithm" is an algorithm that selects the most suitable product based on the user's health and emotional data.

[0369] A "machine learning algorithm" is an algorithm that continuously learns from user feedback and improves the accuracy of product suggestions.

[0370] The "Emotion AI Engine" is an artificial intelligence engine that analyzes a user's facial expressions, voice, and text to generate emotional data.

[0371] A "generative AI model" is a technology that uses natural language processing technology to generate prompts based on a user's emotional and health data and make product suggestions.

[0372] A "prompt sentence" is a natural language sentence that the generative AI model generates based on the user's emotional and health data, and serves as the starting point for product suggestions.

[0373] The present invention relates to a system that suggests the latest trending products based on a user's health and emotional data. The system is mainly composed of a terminal, a server, and an Emotion AI engine.

[0374] The terminal is a device through which users input health data such as heart rate, blood pressure, and blood sugar levels, as well as emotional data such as facial expressions, voice, and text input, and transmits this data to a server. The server stores and analyzes the health and emotional data received from the terminal, and acquires and stores the latest trending product information. The server also analyzes emotional states using an Emotion AI engine and generates emotional data.

[0375] The server runs a matching algorithm based on the health and emotional data received from the user to select the most suitable products. The selected products are then listed and sent to the user's device. The user can then review the suggested products on their device, select the products they are interested in, and purchase them.

[0376] The server also receives user feedback, stores this feedback data in a database, and updates it. The server then uses machine learning algorithms to improve the accuracy of future recommendations based on this feedback data, enabling it to recommend products that are more suitable for the user.

[0377] As a concrete example, in the case of User B, who suffers from high blood pressure and feels stressed, User B inputs health data such as heart rate and blood pressure, as well as emotional data via text input, into the device every day. The device sends this data to the server, which analyzes User B's data and suggests low-salt foods and relaxation products. The server also generates prompts using a generative AI model and uses natural language processing to suggest products.

[0378] For example, the following prompt sentence is fed into a generative AI model:

[0379] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[0380] The system allows users to receive customized product recommendations based on their health and emotional state, providing a higher quality shopping experience.

[0381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0382] Step 1:

[0383] The user inputs health and emotional data into the device. Health data includes heart rate, blood pressure, blood sugar level, etc., while emotional data includes text input, voice data, and facial expression data. This data is temporarily stored on the device. The input data is then encrypted in the next step.

[0384] Step 2:

[0385] The device encrypts the health and emotion data entered and sends it to the server. Encryption ensures data security and prevents information sent from the device to the server via the Internet from being intercepted by third parties. The device encrypts the data using encryption technology such as AES (Advanced Encryption Standard) and sends it to the server.

[0386] Step 3:

[0387] The server receives the health and emotion data sent from the device and stores it in a database. The server decrypts the received encrypted data, stores it in the database, and then begins analysis. The database also stores past health and emotion data, which are referenced for analysis.

[0388] Step 4:

[0389] The server uses the Emotion AI engine to analyze the user's emotional data. Specifically, it performs text analysis, voice analysis, and facial expression analysis to identify the user's emotional state. In this process, the emotion engine uses a deep learning model to determine the emotion and generates new emotional data based on the results. It outputs emotional states such as "stress" or "joy" from the input text, voice, and facial expression data.

[0390] Step 5:

[0391] The server retrieves the latest product information from a trending product database on the Internet, organizes it by category, and saves it. The server periodically accesses databases on the Internet and various APIs to retrieve the latest product information. The retrieved product information is organized by category (e.g., food, wellness goods, etc.), and the organized information is saved in a local database.

[0392] Step 6:

[0393] The server runs a matching algorithm based on the user's health and emotional data and the acquired trending product information to select the most suitable products. Specifically, low-salt foods and relaxation goods are selected based on the user's health condition (e.g., high blood pressure) and emotional state (e.g., stress). The algorithm uses a machine learning model, and accuracy is improved based on past data and feedback.

[0394] Step 7:

[0395] The server creates a list of selected products and sends it to the user's device. The server creates a list of selected products and sends it to the user's device. The user can check the proposed product list on the device and select the products that interest them.

[0396] Step 8:

[0397] The user enters product feedback. The user enters their impressions of the purchased product and their level of satisfaction into the terminal using text input or a rating system, and the data is then sent back to the server. The feedback uses recommended prompts such as "How effective is this product?"

[0398] Step 9:

[0399] The server receives the feedback and updates the database. After receiving the feedback data, the server stores it in its database and uses machine learning algorithms to improve the accuracy of future recommendations. This enables product recommendations that are more tailored to the user's needs.

[0400] As a concrete example of how it works, input the following prompt sentence into the generative AI model:

[0401] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[0402] This allows the Emotion AI engine to identify the user's emotional state and suggest appropriate trending products.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Second embodiment]

[0407] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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."

[0419] The present invention relates to a system for proposing the latest trend products based on a user's health data. Hereinafter, an embodiment of the present invention will be described.

[0420] This system is mainly composed of a user's "terminal" and a linked "server." The terminal and server communicate via the Internet.

[0421] First, the user inputs their own health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[0422] The server receives, stores, and analyzes the health data. The server then retrieves and stores the latest product information from trending product databases on the Internet and from e-commerce site APIs. The retrieved product information is organized by category.

[0423] The server then runs a matching algorithm based on the health data and the retrieved trending product information, selecting, for example, low-salt foods for a user with high blood pressure or low-impact fitness equipment for a user with arthritis.

[0424] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[0425] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[0426] As a concrete example, let's say Person A has high blood pressure and arthritis. Person A enters their blood pressure and the condition of their joints into their device every day. The device sends this data to a server, which analyzes Person A's data and suggests low-salt foods and fitness equipment that puts less strain on the joints. The suggested products are listed and sent to Person A's device, where Person A can check the list and make a purchase. Person A then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[0427] This completes the embodiment of the present invention. This system allows users to enjoy new trendy products while maintaining their health.

[0428] The processing flow will be explained below.

[0429] Step 1:

[0430] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) into the terminal.

[0431] Step 2:

[0432] The terminal encrypts the entered health data and sends it to the server.

[0433] Step 3:

[0434] The server receives the transmitted health data and stores it in a database.

[0435] Step 4:

[0436] The server analyzes the health data and evaluates the user's health status.

[0437] Step 5:

[0438] The server retrieves the latest product information from trending product databases on the Internet and from e-commerce site APIs.

[0439] Step 6:

[0440] The server organizes the acquired product information by category and stores it in a database.

[0441] Step 7:

[0442] The server runs a matching algorithm based on the user's health data and organized trend product information.

[0443] Step 8:

[0444] The server selects products that are best suited to the user's health condition and generates a shopping list.

[0445] Step 9:

[0446] The server transmits the generated shopping list to the terminal.

[0447] Step 10:

[0448] The terminal notifies the user of the received shopping list.

[0449] Step 11:

[0450] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[0451] Step 12:

[0452] The user inputs feedback on the purchased product into the terminal.

[0453] Step 13:

[0454] The terminal sends the feedback to the server.

[0455] Step 14:

[0456] The server updates the database based on the feedback received.

[0457] Step 15:

[0458] The server uses machine learning algorithms to analyze the data and improve the accuracy of future suggestions.

[0459] The above is a specific processing flow in the system of the present invention.

[0460] Example 1

[0461] 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."

[0462] While conventional health management systems can collect users' health data, they lack the functionality to suggest products tailored to each individual user based on that data. This results in users spending a great deal of time and effort to find the products they need. Furthermore, the list of suggested products does not necessarily meet the user's needs, resulting in low user satisfaction. Furthermore, the implementation of machine learning algorithms to effectively collect user feedback and improve the accuracy of future suggestions is insufficient. A new system to solve this issue is needed.

[0463] 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.

[0464] In this invention, the server includes a device for inputting and transmitting biometric information; a device for receiving, storing, and analyzing the biometric information; and a device for acquiring and storing the latest product information from a database. This enables optimal product recommendations based on the user's health data. Furthermore, the conventional problem can be effectively solved by combining a means for performing a matching process, listing selected products, and sending them to the user's device, and a device for receiving user feedback, updating the database, and executing an algorithm to improve the accuracy of future recommendations. Specifically, by incorporating a means for organizing and storing the latest product information by category, the accuracy of recommending optimal products to each individual user is improved. Furthermore, by transmitting and receiving data using encrypted communications, personal information protection and data security are enhanced. This allows users to efficiently discover and purchase the latest trendy products while maintaining their health.

[0465] "Biometric information" refers to data related to the user's health condition, such as heart rate, blood pressure, and blood sugar level.

[0466] "Device" refers to equipment that allows a user to input, send, receive, and display biometric information.

[0467] A "server" is a computer system that communicates with the device via the Internet, receives, stores, and analyzes biometric information, and acquires and manages product information.

[0468] A "database" is a storage system that allows a server to systematically store and manage information.

[0469] "Matching processing" is a process of selecting products that are suitable for the user's biometric information using a matching algorithm.

[0470] "Product information" is information about the latest trending products obtained from the database and organized by category.

[0471] "Listing" is the process by which the server organizes the selected products into an easy-to-read format so that the user can view them.

[0472] An "algorithm" is a set of calculation procedures or rules that run on a server and are used for data analysis, matching, and machine learning.

[0473] "Encrypted communication" is a technology that encrypts data in order to securely send and receive data over the Internet.

[0474] The present invention relates to a system that proposes optimal products based on a user's biometric information. The following describes an embodiment of the present invention. This system is primarily composed of a user's "terminal" and a linked "server." The terminal and server communicate with each other via the Internet.

[0475] Hardware configuration

[0476] Device: A device used by the user, such as a smartphone or tablet, on which a dedicated health management application is installed.

[0477] Server: A high-performance cloud server will be used to analyze data, execute matching algorithms, and manage the database. For example, AWS or Google Cloud can be used.

[0478] Software configuration

[0479] Terminal application: Has the function to input and transmit the user's biometric information. Data is transmitted securely using encrypted communication (HTTPS).

[0480] Server-side program: Consists of the following modules:

[0481] 1. Data Reception Module: Receives and decodes biometric information sent from the device. It uses an encrypted communication protocol (AES-256).

[0482] 2. Data analysis module: Analyzes the received data and stores it in a database.

[0483] 3. Product information acquisition module: Acquires the latest product information from a trending product database or an e-commerce site API (e.g., Amazon API, Rakuten API).

[0484] 4. Matching module: Executes a matching algorithm (e.g., KNN: nearest neighbor algorithm) based on biometric information and product information to select the most suitable product for each individual user.

[0485] 5. List generation module: Compiles the selected products into a shopping list, converts it into JSON format, and sends it to the terminal.

[0486] 6. Feedback collection module: Receives user feedback and updates the database. It uses machine learning algorithms (e.g., random forest) to improve the accuracy of future suggestions.

[0487] System Operation

[0488] 1. User biometric information input and transmission:

[0489] A user uses a dedicated health management app to input biometric information such as heart rate, blood pressure, and blood glucose level. For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[0490] The input data is encrypted within the terminal and sent to the server.

[0491] 2. Server data reception and analysis:

[0492] The server decrypts the received encrypted data and stores it in a database.

[0493] At the same time, the latest trending product information is obtained from databases and APIs on the Internet, and is organized and stored by category.

[0494] 3. Matching biometric information with product information:

[0495] The server selects the most suitable product based on biometric and product information, for example, recommending low-salt foods to a user with high blood pressure and fitness products that are gentle on the joints to a user with arthritis.

[0496] 4. Shopping list generation and notification:

[0497] The server creates a list of selected products, converts it into JSON format, and sends it to the terminal.

[0498] The terminal receives the list and notifies the user that a new product list has arrived.

[0499] 5. User feedback and database updates:

[0500] The user inputs feedback about the purchased product and sends it to the server from the terminal. For example, the user might say, "The low-salt food tasted good, but I'd like the packaging to be improved."

[0501] The server receives the feedback and uses machine learning algorithms to update the database and improve the accuracy of the suggestions.

[0502] Prompt Sentence Examples

[0503] "Based on my health data (blood pressure: 130 / 85, joint pain level: 4), please suggest the latest low-sodium foods and joint-friendly fitness gear."

[0504] This allows users to efficiently discover new trendy products and maintain their health. The seamless process of inputting biometric information, suggesting products, and collecting feedback also increases user satisfaction.

[0505] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0506] Step 1: Enter and send user biometric information

[0507] A user uses a dedicated health management app to input biometric information (e.g., heart rate, blood pressure, blood glucose level, etc.). For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[0508] Input: Biometric information such as heart rate, blood pressure, and blood sugar level

[0509] The device encrypts this data using the AES-256 encryption algorithm and sends it to the server using HTTPS.

[0510] Output: Encrypted biometric information

[0511] Step 2: Receiving and storing biometric information on the server

[0512] The server receives the encrypted data sent from the terminal and ensures security using TLS (Transport Layer Security).

[0513] Input: Encrypted biometric information

[0514] The server decrypts the received data using AES-256 and stores the decrypted data in a health database.

[0515] Output: Stored biometric information

[0516] Step 3: Obtain and organize trending product information

[0517] The server periodically retrieves the latest product information from trending product databases on the Internet or from e-commerce site APIs, such as Amazon API or Rakuten API.

[0518] Input: Latest product information from trending product databases and e-commerce site APIs

[0519] The server classifies the acquired product information by category and stores it in a product database.

[0520] Output: Product information organized by category

[0521] Step 4: Matching biometric information with product information

[0522] The server runs a matching algorithm based on the stored biometric information and product information, using KNN (Kind Neighbor Neighborhood Algorithm) to select the product that best suits the user's needs.

[0523] Input: Stored biometric information, product information organized by category

[0524] Output: A list of products that are best suited for the user

[0525] Step 5: Generate and submit your shopping list

[0526] The server compiles the selected items into a shopping list and converts it into JSON format.

[0527] Input: A list of products that are best suited for the user

[0528] The server sends the shopping list in JSON format to the device using HTTPS.

[0529] Output: Shopping list in JSON format

[0530] Step 6: Shopping list notification and purchase process

[0531] The device displays the shopping list received from the server on the user interface (UI), for example, displaying product images, descriptions, prices, purchase buttons, etc. on the app screen.

[0532] Input: Shopping list in JSON format

[0533] The user reviews the list, selects the products they are interested in, and a purchase link to the e-commerce site is generated.

[0534] Output: User selected products and corresponding purchase links

[0535] Step 7: User feedback and submission

[0536] The user inputs feedback about the purchased product into the terminal. For example, the user inputs feedback such as "The low-salt food tasted good, but I would like the packaging to be improved."

[0537] Input: User feedback

[0538] The device encrypts the feedback data and sends it to the server using HTTPS.

[0539] Output: Encrypted feedback data

[0540] Step 8: Receive feedback and update the database

[0541] The server receives the encrypted feedback sent from the device and decrypts it using AES-256.

[0542] Input: Encrypted feedback data

[0543] The decoded data is stored in a feedback database and analyzed using machine learning algorithms to improve the accuracy of future suggestions.

[0544] Output: Updated feedback data, learning results for improved accuracy

[0545] The above are the specific processing steps of the system program. This system allows users to efficiently discover new trend products and manage their health.

[0546] (Application example 1)

[0547] 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."

[0548] In today's world, many consumers are concerned about their health, but there are only a limited number of systems that provide optimal products to individual users based on their health data. Consumers have difficulty finding the right products for them from the vast number of products available, and there is also a lack of information about new trendy products. For this reason, there is a need for a system that recommends optimal health and trendy products to individual users based on their health data, and allows them to seamlessly purchase them.

[0549] 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.

[0550] In this invention, the server includes a terminal with a function that allows the user to purchase suggested products, a means for executing a matching algorithm based on health data and product information, a means for organizing and saving the acquired trend product information by category, and a means for notifying the user of a shopping list, thereby enabling the user to easily find and purchase the most suitable products based on their own health data.

[0551] A "terminal" is a device for inputting a user's health data and sending it to a server.

[0552] The "server" is a system that receives, stores, and analyzes users' health data, and also obtains the latest product information from a trending product database on the Internet.

[0553] The "Internet Trending Product Database" is an online database that collects the latest product information.

[0554] The "matching algorithm" is a calculation method for selecting the most suitable products for each individual user based on the user's health data and acquired trend product information.

[0555] A "shopping list" is a list of selected products that is sent to the user's terminal.

[0556] "Feedback" is information that allows users to input their impressions and ratings of purchased products.

[0557] A "machine learning algorithm" is an algorithm that updates the database based on user feedback and improves the accuracy of suggestions from the next time onwards.

[0558] "Organizing by category" means classifying and saving the acquired trending product information by type.

[0559] "Encrypted communication" is a method of communicating the input, transmission, and reception of a user's health data as encrypted data between the terminal and the server.

[0560] The "function to purchase products suggested to the user" is a function that allows the user to select the suggested products on the device and complete the purchase procedure.

[0561] MODE FOR CARRYING OUT THE INVENTION

[0562] The present invention relates to a system that suggests the latest trend products based on a user's health data, and detailed embodiments thereof will be described below.

[0563] System configuration

[0564] This system mainly consists of the following elements:

[0565] 1. Terminal: A device for inputting the user's health data and sending it to the server. In this system, a smartphone is used.

[0566] 2. Server: This system receives, stores, and analyzes users' health data. It also retrieves and stores the latest product information from a trending product database on the Internet.

[0567] 3. Online trending product database: An online database that collects the latest product information.

[0568] 4. Matching algorithm: A calculation method for selecting the most suitable product for each individual user based on the user's health data and acquired trend product information.

[0569] 5. Shopping list: A list of selected products is created and sent to the user's device.

[0570] 6. Feedback: Information for users to enter their impressions and ratings of the products they have purchased.

[0571] 7. Machine learning algorithm: This algorithm updates the database based on user feedback and improves the accuracy of future suggestions.

[0572] 8. A means of organizing by category: This is a means of classifying and saving the acquired trending product information by type.

[0573] 9. Encrypted communication: A method in which the input, transmission, and reception of a user's health data is communicated as encrypted data between the terminal and the server.

[0574] 10. Ability to purchase products suggested to users: This function allows users to select suggested products on their device and complete the purchase process.

[0575] Implementation method

[0576] The user enters their own health data into the device. The entered health data is encrypted and sent to a server via the Internet using the Fernet encryption method. The server receives, stores, and analyzes this data. The server also retrieves and stores the latest product information from a trending product database on the Internet and from e-commerce site APIs.

[0577] The server organizes and stores the acquired product information by category. The server then runs a matching algorithm based on the health data and product information to select the most suitable products for each individual user. The selected products are then compiled into a shopping list and sent to the user's device.

[0578] Users receive this shopping list on their device and can select and purchase items that interest them. The device also has a built-in function for carrying out the purchase process. Furthermore, if the user enters feedback on the purchased item, it is sent to the server. The server updates the database based on this feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[0579] Specific examples

[0580] For example, if a user has high blood pressure and arthritis, the user enters their blood pressure and the condition of their joints into their smartphone every day. The device encrypts this data and sends it to a server. The server analyzes the data and suggests low-salt foods and fitness equipment that are gentle on the joints that are best suited to the user. A list of suggested products is then sent to the user's device. The user can review the list and purchase any products that interest them. After that, the user can enter their thoughts on the products, and the feedback is sent to the server, which updates the database.

[0581] Prompt Sentence Examples

[0582] We would like to build a system that recommends the latest trending products based on a user's health data (heart rate, blood pressure, blood sugar level, etc.). The relevant data will be obtained from an online trending product database or an e-commerce site API, and will be compared with the health data to suggest products. Please provide an algorithm that will encrypt the JSON-formatted health data using Fernet and send it to the server.

[0583] This invention allows users to easily find and purchase the latest trending products while maintaining their health. The server constantly analyzes product information and health data to improve the accuracy of recommendations, providing users with the best options.

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] The user inputs health data into the device, such as heart rate, blood pressure, and blood sugar levels, through a smartphone application, and the data is converted into JSON format.

[0587] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[0588] Output: Health data in JSON format

[0589] Step 2:

[0590] The device encrypts the entered health data using the Fernet encryption method, and the data is protected by an encryption key.

[0591] Input: Health data in JSON format, encryption key

[0592] Output: Encrypted health data (binary format)

[0593] Step 3:

[0594] The device sends the encrypted health data to the server via a POST request over the internet.

[0595] Input: Encrypted health data

[0596] Output: Data transmission status to the server (success / failure)

[0597] Step 4:

[0598] The server stores the received health data and performs analysis, processing the data to evaluate the user's health status.

[0599] Input: Encrypted health data

[0600] Output: Analysis results (health status evaluation data)

[0601] Step 5:

[0602] The server retrieves and stores the latest product information from a trending product database on the Internet. Product information is retrieved using an API and organized by category.

[0603] Input: API request (endpoint and parameters)

[0604] Output: Product information organized by category

[0605] Step 6:

[0606] The server runs a matching algorithm based on health data and product information to select the best products for each individual user. The algorithm filters out appropriate products based on health status.

[0607] Input: Analysis results, product information

[0608] Output: A list of selected products

[0609] Step 7:

[0610] The server creates a shopping list of the selected items and sends it to the user's device. The list is then displayed on the device via a notification function.

[0611] Input: List of selected products

[0612] Output: Send shopping list to device (notification)

[0613] Step 8:

[0614] The user checks the shopping list, selects the items they are interested in, and purchases them. The purchase process is completed through the device application.

[0615] Input: Shopping list

[0616] Output: Purchase completion status

[0617] Step 9:

[0618] Users enter feedback on the product they have purchased, and the device sends that feedback to the server, where the ratings and comments are encrypted again and sent to the server.

[0619] Input: User feedback

[0620] Output: Feedback data sent to server

[0621] Step 10:

[0622] The server updates the database based on the feedback it receives and uses machine learning algorithms to improve the accuracy of future suggestions.

[0623] Input: Feedback data

[0624] Output: Updated database, improved proposed algorithm

[0625] 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.

[0626] The present invention relates to a system for proposing the latest trend products based on health data and emotion data of a user. Hereinafter, an embodiment of the present invention will be described.

[0627] This system is primarily composed of the user's "terminal," a linked "server," and an "emotion engine" that recognizes the user's emotions. The terminal and server communicate via the Internet, and the emotion engine is installed on either the terminal or the server.

[0628] First, the user inputs health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[0629] The server receives the health data, stores it in a database, and analyzes it. In parallel, the emotion engine analyzes the user's emotions from facial expressions, voice, text input, etc., and sends the emotion data to the server.

[0630] Next, the server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[0631] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure who is stressed might be matched with low-salt foods and relaxation products.

[0632] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[0633] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[0634] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[0635] The above is an embodiment of the present invention. This system allows users to maintain their health, improve their mental stability, and enjoy new trendy products.

[0636] The processing flow will be explained below.

[0637] Step 1:

[0638] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) and emotional data (text, voice, facial expression, etc.) into the terminal.

[0639] Step 2:

[0640] The terminal encrypts the input health data and emotion data and transmits them to the server.

[0641] Step 3:

[0642] The server receives the transmitted health data and emotion data and stores them in respective databases.

[0643] Step 4:

[0644] The server analyzes the health data to assess the user's health status.

[0645] Step 5:

[0646] An emotion engine analyzes the transmitted emotion data and evaluates the user's emotional state.

[0647] Step 6:

[0648] The server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[0649] Step 7:

[0650] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure and stress might be recommended low-salt foods and relaxation products.

[0651] Step 8:

[0652] The server generates a shopping list of the selected items and transmits it to the user's terminal.

[0653] Step 9:

[0654] The terminal notifies the user of the received shopping list.

[0655] Step 10:

[0656] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[0657] Step 11:

[0658] The user inputs feedback about the purchased item into the terminal.

[0659] Step 12:

[0660] The terminal sends the feedback to the server.

[0661] Step 13:

[0662] The server updates the database based on the feedback received.

[0663] Step 14:

[0664] The server uses machine learning algorithms to analyze the feedback data and improve the accuracy of future suggestions.

[0665] The above is a specific processing flow in the system of the present invention.

[0666] Example 2

[0667] 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."

[0668] Conventional health management systems only utilize users' health data and do not take into account their emotional data when making suggestions. This makes it difficult to suggest products that are appropriate for the user's mental state, making it difficult to improve overall satisfaction. Furthermore, the accuracy of trending product suggestions is low, and there is a lack of a mechanism for effectively incorporating user feedback into future suggestions. There is a need to solve these problems and improve the quality of product suggestions to users.

[0669] 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.

[0670] In this invention, the server includes means for receiving, storing, and analyzing the user's health data, means for acquiring and storing information from the latest product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data to select the most suitable product for each user. This makes it possible to propose the most suitable product from trending products, taking into consideration the user's health and emotional state comprehensively.

[0671] "Health data" refers to information that represents a user's physical condition, such as heart rate, blood pressure, and blood sugar level.

[0672] "Device" means an electronic device used by a user to input and receive health data and emotional data.

[0673] "Server" means a central computer system that stores and analyzes received data, and retrieves and suggests product information.

[0674] The "emotion analysis engine" is a function for analyzing emotional data from users' facial expressions, voice, text input, etc.

[0675] The "Trend Product Database" is a database that stores the latest product information available on the Internet.

[0676] A "matching algorithm" is a calculation method for selecting the most suitable product based on the user's health and emotional data.

[0677] A "machine learning algorithm" is a self-learning algorithm that improves the accuracy of suggestions based on feedback from users.

[0678] "Encrypted communication" is a method of communicating by encrypting information in order to send and receive data safely.

[0679] "Feedback" refers to information such as ratings and impressions provided by users regarding purchased products.

[0680] A "shopping list" is a list that displays selected products in a list format so that the user can check and purchase them.

[0681] The present invention relates to a system that suggests the latest trending products based on a user's health data and emotional data. The system is primarily composed of the user's "terminal," a linked "server," and an "emotion analysis engine" that recognizes the user's emotions. The following describes in detail the mode for carrying out the invention.

[0682] First, the user inputs their health data (e.g., heart rate, blood pressure, blood sugar level) using a dedicated application. The device then encrypts the input health data using the AES-256 encryption algorithm and transmits it to a server via the Internet.

[0683] The server decrypts the received health data and stores it in a health data database, which is organized for each user and comprehensively manages the health status of the entire system.

[0684] In parallel, the emotion analysis engine collects and analyzes emotional data in real time from the user's facial expressions, voice, and text input. This emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology. The obtained emotional data is stored on the device or server.

[0685] The server accesses the latest product databases on the Internet and the APIs of e-commerce sites to obtain the latest product information. This information is also organized by category and stored in the trend product database.

[0686] Next, the server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the user's health data and emotion data to select the most suitable products for the user. The selected products are compiled into a shopping list and sent to the device. The device receives this shopping list and notifies the user.

[0687] The user can check the list of suggested products on the terminal, select the product they are interested in, and purchase it. After completing the purchase procedure, the user provides feedback on the purchased product, which is then sent back to the server via the terminal.

[0688] The server stores the received feedback in a database, analyzes it, and uses machine learning algorithms to improve the accuracy of future suggestions.

[0689] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[0690] Examples of prompts include:

[0691] - "Please explain the algorithm of the system that suggests low-salt foods and relaxation products to users who are stressed due to high blood pressure."

[0692] - "Explain the overall flow of a system that suggests trending products suitable for users based on their health data (e.g., heart rate, blood pressure, blood sugar level) and emotional data (facial expression, voice, text)."

[0693] This system allows users to enjoy the latest trendy products while maintaining their health and mental well-being.

[0694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0695] Step 1:

[0696] The user enters health data (e.g., heart rate, blood pressure, blood glucose level) using a dedicated application. The entered data is collected in form fields in the application. The entered data is then stored on the device.

[0697] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[0698] Output: Health data stored on the device before encryption

[0699] Step 2:

[0700] The terminal encrypts the entered health data using the AES-256 encryption algorithm in order to send the data securely to the server.

[0701] Input: Health data before encryption

[0702] Output: Encrypted health data

[0703] Step 3:

[0704] The device transmits the encrypted health data to the server via the Internet. This transmission operation is to ensure that the data reaches the server while maintaining its confidentiality.

[0705] Input: Encrypted health data

[0706] Output: A request to send data to the server

[0707] Step 4:

[0708] The server decrypts the received data from its encrypted state. The decrypted data is stored in a health data database. The data is organized for each user.

[0709] Input: Encrypted health data

[0710] Output: Decrypted health data, stored in a database

[0711] Step 5:

[0712] In parallel, the emotion analysis engine collects the user's facial expressions, voice, and text input in real time. By analyzing this data, the emotion analysis engine identifies the user's emotional state. The emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology.

[0713] Input: User's facial expression data, voice data, text data

[0714] Output: Parsed emotion data

[0715] Step 6:

[0716] The emotion analysis engine sends the analyzed emotion data to the server, where it is stored in an emotion database.

[0717] Input: Parsed emotion data

[0718] Output: Send data to the server and save it in the emotion database

[0719] Step 7:

[0720] The server accesses the latest product databases on the Internet and EC site APIs to obtain the latest product information. The obtained data is organized by category and saved in a trending product database.

[0721] Input: API request

[0722] Output: Latest product information, saved in trend product database

[0723] Step 8:

[0724] The server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the stored health data and emotion data, and the best product for the user is selected.

[0725] Input: Health data, emotion data

[0726] Output: Selected product information

[0727] Step 9:

[0728] The server compiles the selected product information into a shopping list and sends it to the terminal, which receives the shopping list and notifies the user.

[0729] Input: Selected product information

[0730] Output: Shopping list, notifications to device

[0731] Step 10:

[0732] The user checks the shopping list on the device, selects the products they are interested in, and then completes the purchase process. The purchase information is saved on the device.

[0733] Input: Shopping list

[0734] Output: Purchased product information, saved on the device

[0735] Step 11:

[0736] When a user inputs feedback on a product, the terminal transmits the feedback to the server, and the feedback data is stored in the database of the server.

[0737] Input: User feedback

[0738] Output: Send data to server, save feedback in database

[0739] Step 12:

[0740] The server analyzes the received feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[0741] Input: Feedback data

[0742] Output: Updated machine learning model, improved suggestion accuracy

[0743] This processing step allows users to efficiently receive recommendations for trending products that fit their health and emotional state, and allows post-purchase feedback to be reflected in future recommendations.

[0744] (Application example 2)

[0745] 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."

[0746] Conventional systems only suggest products based on the user's health data, and are unable to consider the user's emotional state when making product suggestions, making it difficult to fully meet the user's needs. Furthermore, the accuracy of the feedback system used to improve the suitability of the suggested products is limited, preventing the system from increasing user satisfaction.

[0747] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving, saving, and analyzing the user's health data and emotional data, means for acquiring and saving the latest product information from a trend product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data and the acquired trend product information to select the product that is best suited to each user. This makes it possible to suggest products that are more suitable for each user, taking into account not only the user's health condition but also their emotional state.

[0748] A "terminal" is a device that allows a user to input their own health data and emotional data and transmit this to a server.

[0749] The "server" is a device that stores and analyzes the user's health data and emotional data received from the terminal, and processes product selection and user feedback.

[0750] "Emotional data" refers to data relating to a user's emotional state analyzed from the user's facial expressions, voice, text input, etc.

[0751] The "trend product database" is a database that manages the latest product information obtained from the Internet.

[0752] The "matching algorithm" is an algorithm that selects the most suitable product based on the user's health and emotional data.

[0753] A "machine learning algorithm" is an algorithm that continuously learns from user feedback and improves the accuracy of product suggestions.

[0754] The "Emotion AI Engine" is an artificial intelligence engine that analyzes a user's facial expressions, voice, and text to generate emotional data.

[0755] A "generative AI model" is a technology that uses natural language processing technology to generate prompts based on a user's emotional and health data and make product suggestions.

[0756] A "prompt sentence" is a natural language sentence that the generative AI model generates based on the user's emotional and health data, and serves as the starting point for product suggestions.

[0757] The present invention relates to a system that suggests the latest trending products based on a user's health and emotional data. The system is mainly composed of a terminal, a server, and an Emotion AI engine.

[0758] The terminal is a device through which users input health data such as heart rate, blood pressure, and blood sugar levels, as well as emotional data such as facial expressions, voice, and text input, and transmits this data to a server. The server stores and analyzes the health and emotional data received from the terminal, and acquires and stores the latest trending product information. The server also analyzes emotional states using an Emotion AI engine and generates emotional data.

[0759] The server runs a matching algorithm based on the health and emotional data received from the user to select the most suitable products. The selected products are then listed and sent to the user's device. The user can then review the suggested products on their device, select the products they are interested in, and purchase them.

[0760] The server also receives user feedback, stores this feedback data in a database, and updates it. The server then uses machine learning algorithms to improve the accuracy of future recommendations based on this feedback data, enabling it to recommend products that are more suitable for the user.

[0761] As a concrete example, in the case of User B, who suffers from high blood pressure and feels stressed, User B inputs health data such as heart rate and blood pressure, as well as emotional data via text input, into the device every day. The device sends this data to the server, which analyzes User B's data and suggests low-salt foods and relaxation products. The server also generates prompts using a generative AI model and uses natural language processing to suggest products.

[0762] For example, the following prompt sentence is fed into a generative AI model:

[0763] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[0764] The system allows users to receive customized product recommendations based on their health and emotional state, providing a higher quality shopping experience.

[0765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0766] Step 1:

[0767] The user inputs health and emotional data into the device. Health data includes heart rate, blood pressure, blood sugar level, etc., while emotional data includes text input, voice data, and facial expression data. This data is temporarily stored on the device. The input data is then encrypted in the next step.

[0768] Step 2:

[0769] The device encrypts the health and emotion data entered and sends it to the server. Encryption ensures data security and prevents information sent from the device to the server via the Internet from being intercepted by third parties. The device encrypts the data using encryption technology such as AES (Advanced Encryption Standard) and sends it to the server.

[0770] Step 3:

[0771] The server receives the health and emotion data sent from the device and stores it in a database. The server decrypts the received encrypted data, stores it in the database, and then begins analysis. The database also stores past health and emotion data, which are referenced for analysis.

[0772] Step 4:

[0773] The server uses the Emotion AI engine to analyze the user's emotional data. Specifically, it performs text analysis, voice analysis, and facial expression analysis to identify the user's emotional state. In this process, the emotion engine uses a deep learning model to determine the emotion and generates new emotional data based on the results. It outputs emotional states such as "stress" or "joy" from the input text, voice, and facial expression data.

[0774] Step 5:

[0775] The server retrieves the latest product information from a trending product database on the Internet, organizes it by category, and saves it. The server periodically accesses databases on the Internet and various APIs to retrieve the latest product information. The retrieved product information is organized by category (e.g., food, wellness goods, etc.), and the organized information is saved in a local database.

[0776] Step 6:

[0777] The server runs a matching algorithm based on the user's health and emotional data and the acquired trending product information to select the most suitable products. Specifically, low-salt foods and relaxation goods are selected based on the user's health condition (e.g., high blood pressure) and emotional state (e.g., stress). The algorithm uses a machine learning model, and accuracy is improved based on past data and feedback.

[0778] Step 7:

[0779] The server creates a list of selected products and sends it to the user's device. The server creates a list of selected products and sends it to the user's device. The user can check the proposed product list on the device and select the products that interest them.

[0780] Step 8:

[0781] The user enters product feedback. The user enters their impressions of the purchased product and their level of satisfaction into the terminal using text input or a rating system, and the data is then sent back to the server. The feedback uses recommended prompts such as "How effective is this product?"

[0782] Step 9:

[0783] The server receives the feedback and updates the database. After receiving the feedback data, the server stores it in its database and uses machine learning algorithms to improve the accuracy of future recommendations. This enables product recommendations that are more tailored to the user's needs.

[0784] As a concrete example of how it works, input the following prompt sentence into the generative AI model:

[0785] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[0786] This allows the Emotion AI engine to identify the user's emotional state and suggest appropriate trending products.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] [Third embodiment]

[0791] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0792] 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.

[0793] 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).

[0794] 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.

[0795] 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.

[0796] 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).

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] The present invention relates to a system for proposing the latest trend products based on a user's health data. Hereinafter, an embodiment of the present invention will be described.

[0804] This system is mainly composed of a user's "terminal" and a linked "server." The terminal and server communicate via the Internet.

[0805] First, the user inputs their own health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[0806] The server receives, stores, and analyzes the health data. The server then retrieves and stores the latest product information from trending product databases on the Internet and from e-commerce site APIs. The retrieved product information is organized by category.

[0807] The server then runs a matching algorithm based on the health data and the retrieved trending product information, selecting, for example, low-salt foods for a user with high blood pressure or low-impact fitness equipment for a user with arthritis.

[0808] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[0809] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[0810] As a concrete example, let's say Person A has high blood pressure and arthritis. Person A enters their blood pressure and the condition of their joints into their device every day. The device sends this data to a server, which analyzes Person A's data and suggests low-salt foods and fitness equipment that puts less strain on the joints. The suggested products are listed and sent to Person A's device, where Person A can check the list and make a purchase. Person A then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[0811] This completes the embodiment of the present invention. This system allows users to enjoy new trendy products while maintaining their health.

[0812] The processing flow will be explained below.

[0813] Step 1:

[0814] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) into the terminal.

[0815] Step 2:

[0816] The terminal encrypts the entered health data and sends it to the server.

[0817] Step 3:

[0818] The server receives the transmitted health data and stores it in a database.

[0819] Step 4:

[0820] The server analyzes the health data and evaluates the user's health status.

[0821] Step 5:

[0822] The server retrieves the latest product information from trending product databases on the Internet and from e-commerce site APIs.

[0823] Step 6:

[0824] The server organizes the acquired product information by category and stores it in a database.

[0825] Step 7:

[0826] The server runs a matching algorithm based on the user's health data and organized trend product information.

[0827] Step 8:

[0828] The server selects products that are best suited to the user's health condition and generates a shopping list.

[0829] Step 9:

[0830] The server transmits the generated shopping list to the terminal.

[0831] Step 10:

[0832] The terminal notifies the user of the received shopping list.

[0833] Step 11:

[0834] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[0835] Step 12:

[0836] The user inputs feedback on the purchased product into the terminal.

[0837] Step 13:

[0838] The terminal sends the feedback to the server.

[0839] Step 14:

[0840] The server updates the database based on the feedback received.

[0841] Step 15:

[0842] The server uses machine learning algorithms to analyze the data and improve the accuracy of future suggestions.

[0843] The above is a specific processing flow in the system of the present invention.

[0844] Example 1

[0845] 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."

[0846] While conventional health management systems can collect users' health data, they lack the functionality to suggest products tailored to each individual user based on that data. This results in users spending a great deal of time and effort to find the products they need. Furthermore, the list of suggested products does not necessarily meet the user's needs, resulting in low user satisfaction. Furthermore, the implementation of machine learning algorithms to effectively collect user feedback and improve the accuracy of future suggestions is insufficient. A new system to solve this issue is needed.

[0847] 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.

[0848] In this invention, the server includes a device for inputting and transmitting biometric information, a device for receiving, storing, and analyzing the biometric information, and a device for acquiring and storing the latest product information from a database. This enables optimal product recommendations based on the user's health data. Furthermore, the conventional problem can be effectively solved by combining a means for performing a matching process, listing selected products, and sending them to the user's device, and a device for receiving user feedback, updating the database, and executing an algorithm to improve the accuracy of future recommendations. Specifically, by incorporating a means for organizing and storing the latest product information by category, the accuracy of product recommendations for each individual user is improved. Furthermore, data is sent and received using encrypted communications, enhancing the protection of personal information and data security. This allows users to efficiently discover and purchase the latest trendy products while maintaining their health.

[0849] "Biometric information" refers to data related to the user's health condition, such as heart rate, blood pressure, and blood sugar level.

[0850] "Device" refers to equipment that allows a user to input, send, receive, and display biometric information.

[0851] A "server" is a computer system that communicates with the device via the Internet, receives, stores, and analyzes biometric information, and acquires and manages product information.

[0852] A "database" is a storage system that allows a server to systematically store and manage information.

[0853] "Matching processing" is a process of selecting products that are suitable for the user's biometric information using a matching algorithm.

[0854] "Product information" is information about the latest trending products obtained from the database and organized by category.

[0855] "Listing" is the process by which the server organizes the selected products into an easy-to-read format so that the user can view them.

[0856] An "algorithm" is a set of calculation procedures or rules that run on a server and are used for data analysis, matching, and machine learning.

[0857] "Encrypted communication" is a technology that encrypts data in order to securely send and receive data over the Internet.

[0858] The present invention relates to a system that proposes optimal products based on a user's biometric information. The following describes an embodiment of the present invention. This system is primarily composed of a user's "terminal" and a linked "server." The terminal and server communicate with each other via the Internet.

[0859] Hardware configuration

[0860] Device: A device used by the user, such as a smartphone or tablet, on which a dedicated health management application is installed.

[0861] Server: A high-performance cloud server will be used to analyze data, execute matching algorithms, and manage the database. For example, AWS or Google Cloud can be used.

[0862] Software configuration

[0863] Terminal application: Has the function to input and transmit the user's biometric information. Data is transmitted securely using encrypted communication (HTTPS).

[0864] Server-side program: Consists of the following modules:

[0865] 1. Data Reception Module: Receives and decodes biometric information sent from the device. It uses an encrypted communication protocol (AES-256).

[0866] 2. Data analysis module: Analyzes the received data and stores it in a database.

[0867] 3. Product information acquisition module: Acquires the latest product information from a trending product database or an e-commerce site API (e.g., Amazon API, Rakuten API).

[0868] 4. Matching module: Executes a matching algorithm (e.g., KNN: nearest neighbor algorithm) based on biometric information and product information to select the most suitable product for each individual user.

[0869] 5. List generation module: Compiles the selected products into a shopping list, converts it into JSON format, and sends it to the terminal.

[0870] 6. Feedback collection module: Receives user feedback and updates the database. It uses machine learning algorithms (e.g., random forest) to improve the accuracy of future suggestions.

[0871] System Operation

[0872] 1. User biometric information input and transmission:

[0873] A user uses a dedicated health management app to input biometric information such as heart rate, blood pressure, and blood glucose level. For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[0874] The input data is encrypted within the terminal and sent to the server.

[0875] 2. Server data reception and analysis:

[0876] The server decrypts the received encrypted data and stores it in a database.

[0877] At the same time, the latest trending product information is obtained from databases and APIs on the Internet, and is organized and stored by category.

[0878] 3. Matching biometric information with product information:

[0879] The server selects the most suitable product based on biometric and product information, for example, recommending low-salt foods to a user with high blood pressure and fitness products that are gentle on the joints to a user with arthritis.

[0880] 4. Shopping list generation and notification:

[0881] The server creates a list of selected products, converts it into JSON format, and sends it to the terminal.

[0882] The terminal receives the list and notifies the user that a new product list has arrived.

[0883] 5. User feedback and database updates:

[0884] The user inputs feedback about the purchased product and sends it to the server from the terminal. For example, the user might say, "The low-salt food tasted good, but I'd like the packaging to be improved."

[0885] The server receives the feedback and uses machine learning algorithms to update the database and improve the accuracy of the suggestions.

[0886] Prompt Sentence Examples

[0887] "Based on my health data (blood pressure: 130 / 85, joint pain level: 4), please suggest the latest low-sodium foods and joint-friendly fitness gear."

[0888] This allows users to efficiently discover new trendy products and maintain their health. The seamless process of inputting biometric information, suggesting products, and collecting feedback also increases user satisfaction.

[0889] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0890] Step 1: Enter and send user biometric information

[0891] A user uses a dedicated health management app to input biometric information (e.g., heart rate, blood pressure, blood glucose level, etc.). For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[0892] Input: Biometric information such as heart rate, blood pressure, and blood sugar level

[0893] The device encrypts this data using the AES-256 encryption algorithm and sends it to the server using HTTPS.

[0894] Output: Encrypted biometric information

[0895] Step 2: Receiving and storing biometric information on the server

[0896] The server receives the encrypted data sent from the terminal and ensures security using TLS (Transport Layer Security).

[0897] Input: Encrypted biometric information

[0898] The server decrypts the received data using AES-256 and stores the decrypted data in a health database.

[0899] Output: Stored biometric information

[0900] Step 3: Obtain and organize trending product information

[0901] The server periodically retrieves the latest product information from trending product databases on the Internet or from e-commerce site APIs, such as Amazon API or Rakuten API.

[0902] Input: Latest product information from trending product databases and e-commerce site APIs

[0903] The server classifies the acquired product information by category and stores it in a product database.

[0904] Output: Product information organized by category

[0905] Step 4: Matching biometric information with product information

[0906] The server runs a matching algorithm based on the stored biometric information and product information, using KNN (Kind Neighbor Neighborhood Algorithm) to select the product that best suits the user's needs.

[0907] Input: Stored biometric information, product information organized by category

[0908] Output: A list of products that are best suited for the user

[0909] Step 5: Generate and submit your shopping list

[0910] The server compiles the selected items into a shopping list and converts it into JSON format.

[0911] Input: A list of products that are best suited for the user

[0912] The server sends the shopping list in JSON format to the device using HTTPS.

[0913] Output: Shopping list in JSON format

[0914] Step 6: Shopping list notification and purchase process

[0915] The device displays the shopping list received from the server on the user interface (UI), for example, displaying product images, descriptions, prices, purchase buttons, etc. on the app screen.

[0916] Input: Shopping list in JSON format

[0917] The user reviews the list, selects the products they are interested in, and a purchase link to the e-commerce site is generated.

[0918] Output: User selected products and corresponding purchase links

[0919] Step 7: User feedback and submission

[0920] The user inputs feedback about the purchased product into the terminal. For example, the user inputs feedback such as "The low-salt food tasted good, but I would like the packaging to be improved."

[0921] Input: User feedback

[0922] The device encrypts the feedback data and sends it to the server using HTTPS.

[0923] Output: Encrypted feedback data

[0924] Step 8: Receive feedback and update the database

[0925] The server receives the encrypted feedback sent from the device and decrypts it using AES-256.

[0926] Input: Encrypted feedback data

[0927] The decoded data is stored in a feedback database and analyzed using machine learning algorithms to improve the accuracy of future suggestions.

[0928] Output: Updated feedback data, learning results for improved accuracy

[0929] The above are the specific processing steps of the system program. This system allows users to efficiently discover new trend products and manage their health.

[0930] (Application example 1)

[0931] 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."

[0932] In today's world, many consumers are concerned about their health, but there are only a limited number of systems that provide optimal products to individual users based on their health data. Consumers have difficulty finding the right products for them from the vast number of products available, and there is also a lack of information about new trendy products. For this reason, there is a need for a system that recommends optimal health and trendy products to individual users based on their health data, and allows them to seamlessly purchase them.

[0933] 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.

[0934] In this invention, the server includes a terminal with a function that allows the user to purchase suggested products, a means for executing a matching algorithm based on health data and product information, a means for organizing and saving the acquired trend product information by category, and a means for notifying the user of a shopping list, thereby enabling the user to easily find and purchase the most suitable products based on their own health data.

[0935] A "terminal" is a device for inputting a user's health data and sending it to a server.

[0936] The "server" is a system that receives, stores, and analyzes users' health data, and also obtains the latest product information from a trending product database on the Internet.

[0937] The "Internet Trending Product Database" is an online database that collects the latest product information.

[0938] The "matching algorithm" is a calculation method for selecting the most suitable products for each individual user based on the user's health data and acquired trend product information.

[0939] A "shopping list" is a list of selected products that is sent to the user's terminal.

[0940] "Feedback" is information that allows users to input their impressions and ratings of purchased products.

[0941] A "machine learning algorithm" is an algorithm that updates the database based on user feedback and improves the accuracy of suggestions from the next time onwards.

[0942] "Organizing by category" means classifying and saving the acquired trending product information by type.

[0943] "Encrypted communication" is a method of communicating the input, transmission, and reception of a user's health data as encrypted data between the terminal and the server.

[0944] The "function to purchase products suggested to the user" is a function that allows the user to select the suggested products on the device and complete the purchase procedure.

[0945] MODE FOR CARRYING OUT THE INVENTION

[0946] The present invention relates to a system that suggests the latest trend products based on a user's health data, and detailed embodiments thereof will be described below.

[0947] System configuration

[0948] This system mainly consists of the following elements:

[0949] 1. Terminal: A device for inputting the user's health data and sending it to the server. In this system, a smartphone is used.

[0950] 2. Server: This system receives, stores, and analyzes users' health data. It also retrieves and stores the latest product information from a trending product database on the Internet.

[0951] 3. Online trending product database: An online database that collects the latest product information.

[0952] 4. Matching algorithm: A calculation method for selecting the most suitable product for each individual user based on the user's health data and acquired trend product information.

[0953] 5. Shopping list: A list of selected products is created and sent to the user's device.

[0954] 6. Feedback: Information for users to enter their impressions and ratings of the products they have purchased.

[0955] 7. Machine learning algorithm: This algorithm updates the database based on user feedback and improves the accuracy of future suggestions.

[0956] 8. A means of organizing by category: This is a means of classifying and saving the acquired trending product information by type.

[0957] 9. Encrypted communication: A method in which the input, transmission, and reception of a user's health data is communicated as encrypted data between the terminal and the server.

[0958] 10. Ability to purchase products suggested to users: This function allows users to select suggested products on their device and complete the purchase process.

[0959] Implementation method

[0960] The user enters their own health data into the device. The entered health data is encrypted and sent to a server via the Internet using the Fernet encryption method. The server receives, stores, and analyzes this data. The server also retrieves and stores the latest product information from a trending product database on the Internet and from e-commerce site APIs.

[0961] The server organizes and stores the acquired product information by category. The server then runs a matching algorithm based on the health data and product information to select the most suitable products for each individual user. The selected products are then compiled into a shopping list and sent to the user's device.

[0962] Users receive this shopping list on their device and can select and purchase items that interest them. The device also has a built-in function for carrying out the purchase process. Furthermore, if the user enters feedback on the purchased item, it is sent to the server. The server updates the database based on this feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[0963] Specific examples

[0964] For example, if a user has high blood pressure and arthritis, the user enters their blood pressure and the condition of their joints into their smartphone every day. The device encrypts this data and sends it to a server. The server analyzes the data and suggests low-salt foods and fitness equipment that are gentle on the joints that are best suited to the user. A list of suggested products is then sent to the user's device. The user can review the list and purchase any products that interest them. After that, the user can enter their thoughts on the products, and the feedback is sent to the server, which updates the database.

[0965] Prompt Sentence Examples

[0966] We would like to build a system that recommends the latest trending products based on a user's health data (heart rate, blood pressure, blood sugar level, etc.). The relevant data will be obtained from an online trending product database or an e-commerce site API, and will be compared with the health data to suggest products. Please provide an algorithm that will encrypt the JSON-formatted health data using Fernet and send it to the server.

[0967] This invention allows users to easily find and purchase the latest trending products while maintaining their health. The server constantly analyzes product information and health data to improve the accuracy of recommendations, providing users with the best options.

[0968] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0969] Step 1:

[0970] The user inputs health data into the device, such as heart rate, blood pressure, and blood sugar levels, through a smartphone application, and the data is converted into JSON format.

[0971] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[0972] Output: Health data in JSON format

[0973] Step 2:

[0974] The device encrypts the entered health data using the Fernet encryption method, and the data is protected by an encryption key.

[0975] Input: Health data in JSON format, encryption key

[0976] Output: Encrypted health data (binary format)

[0977] Step 3:

[0978] The device sends the encrypted health data to the server via a POST request over the internet.

[0979] Input: Encrypted health data

[0980] Output: Data transmission status to the server (success / failure)

[0981] Step 4:

[0982] The server stores the received health data and performs analysis, processing the data to evaluate the user's health status.

[0983] Input: Encrypted health data

[0984] Output: Analysis results (health status evaluation data)

[0985] Step 5:

[0986] The server retrieves and stores the latest product information from a trending product database on the Internet. Product information is retrieved using an API and organized by category.

[0987] Input: API request (endpoint and parameters)

[0988] Output: Product information organized by category

[0989] Step 6:

[0990] The server runs a matching algorithm based on health data and product information to select the best products for each individual user. The algorithm filters out appropriate products based on health status.

[0991] Input: Analysis results, product information

[0992] Output: A list of selected products

[0993] Step 7:

[0994] The server creates a shopping list of the selected items and sends it to the user's device. The list is then displayed on the device via a notification function.

[0995] Input: List of selected products

[0996] Output: Send shopping list to device (notification)

[0997] Step 8:

[0998] The user checks the shopping list, selects the items they are interested in, and purchases them. The purchase process is completed through the device application.

[0999] Input: Shopping list

[1000] Output: Purchase completion status

[1001] Step 9:

[1002] Users enter feedback on the product they have purchased, and the device sends that feedback to the server, where the ratings and comments are encrypted again and sent to the server.

[1003] Input: User feedback

[1004] Output: Feedback data sent to server

[1005] Step 10:

[1006] The server updates the database based on the feedback it receives and uses machine learning algorithms to improve the accuracy of future suggestions.

[1007] Input: Feedback data

[1008] Output: Updated database, improved proposed algorithm

[1009] 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.

[1010] The present invention relates to a system for proposing the latest trend products based on health data and emotion data of a user. Hereinafter, an embodiment of the present invention will be described.

[1011] This system is primarily composed of the user's "terminal," a linked "server," and an "emotion engine" that recognizes the user's emotions. The terminal and server communicate via the Internet, and the emotion engine is installed on either the terminal or the server.

[1012] First, the user inputs health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[1013] The server receives the health data, stores it in a database, and analyzes it. In parallel, the emotion engine analyzes the user's emotions from facial expressions, voice, text input, etc., and sends the emotion data to the server.

[1014] Next, the server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[1015] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure who is stressed might be matched with low-salt foods and relaxation products.

[1016] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[1017] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[1018] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[1019] The above is an embodiment of the present invention. This system allows users to maintain their health, improve their mental stability, and enjoy new trendy products.

[1020] The processing flow will be explained below.

[1021] Step 1:

[1022] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) and emotional data (text, voice, facial expression, etc.) into the terminal.

[1023] Step 2:

[1024] The terminal encrypts the input health data and emotion data and transmits them to the server.

[1025] Step 3:

[1026] The server receives the transmitted health data and emotion data and stores them in respective databases.

[1027] Step 4:

[1028] The server analyzes the health data to assess the user's health status.

[1029] Step 5:

[1030] An emotion engine analyzes the transmitted emotion data and evaluates the user's emotional state.

[1031] Step 6:

[1032] The server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[1033] Step 7:

[1034] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure and stress might be recommended low-salt foods and relaxation products.

[1035] Step 8:

[1036] The server generates a shopping list of the selected items and transmits it to the user's terminal.

[1037] Step 9:

[1038] The terminal notifies the user of the received shopping list.

[1039] Step 10:

[1040] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[1041] Step 11:

[1042] The user inputs feedback about the purchased item into the terminal.

[1043] Step 12:

[1044] The terminal sends the feedback to the server.

[1045] Step 13:

[1046] The server updates the database based on the feedback received.

[1047] Step 14:

[1048] The server uses machine learning algorithms to analyze the feedback data and improve the accuracy of future suggestions.

[1049] The above is a specific processing flow in the system of the present invention.

[1050] Example 2

[1051] 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."

[1052] Conventional health management systems only utilize users' health data and do not take into account their emotional data when making suggestions. This makes it difficult to suggest products that are appropriate for the user's mental state, making it difficult to improve overall satisfaction. Furthermore, the accuracy of trending product suggestions is low, and there is a lack of a mechanism for effectively incorporating user feedback into future suggestions. There is a need to solve these problems and improve the quality of product suggestions to users.

[1053] 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.

[1054] In this invention, the server includes means for receiving, storing, and analyzing the user's health data, means for acquiring and storing information from the latest product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data to select the most suitable product for each user. This makes it possible to propose the most suitable product from trending products, taking into consideration the user's health and emotional state comprehensively.

[1055] "Health data" refers to information that represents a user's physical condition, such as heart rate, blood pressure, and blood sugar level.

[1056] "Device" means an electronic device used by a user to input and receive health data and emotional data.

[1057] "Server" means a central computer system that stores and analyzes received data, and retrieves and suggests product information.

[1058] The "emotion analysis engine" is a function for analyzing emotional data from users' facial expressions, voice, text input, etc.

[1059] The "Trend Product Database" is a database that stores the latest product information available on the Internet.

[1060] A "matching algorithm" is a calculation method for selecting the most suitable product based on the user's health and emotional data.

[1061] A "machine learning algorithm" is a self-learning algorithm that improves the accuracy of suggestions based on feedback from users.

[1062] "Encrypted communication" is a method of communicating by encrypting information in order to send and receive data safely.

[1063] "Feedback" refers to information such as ratings and impressions provided by users regarding purchased products.

[1064] A "shopping list" is a list that displays selected products in a list format so that the user can check and purchase them.

[1065] The present invention relates to a system that suggests the latest trending products based on a user's health data and emotional data. The system is primarily composed of the user's "terminal," a linked "server," and an "emotion analysis engine" that recognizes the user's emotions. The following describes in detail the mode for carrying out the invention.

[1066] First, the user inputs their health data (e.g., heart rate, blood pressure, blood sugar level) using a dedicated application. The device then encrypts the input health data using the AES-256 encryption algorithm and transmits it to a server via the Internet.

[1067] The server decrypts the received health data and stores it in a health data database, which is organized for each user and comprehensively manages the health status of the entire system.

[1068] In parallel, the emotion analysis engine collects and analyzes emotional data in real time from the user's facial expressions, voice, and text input. This emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology. The obtained emotional data is stored on the device or server.

[1069] The server accesses the latest product databases on the Internet and the APIs of e-commerce sites to obtain the latest product information. This information is also organized by category and stored in the trend product database.

[1070] Next, the server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the user's health data and emotion data to select the most suitable products for the user. The selected products are compiled into a shopping list and sent to the device. The device receives this shopping list and notifies the user.

[1071] The user can check the list of suggested products on the terminal, select the product they are interested in, and purchase it. After completing the purchase procedure, the user provides feedback on the purchased product, which is then sent back to the server via the terminal.

[1072] The server stores the received feedback in a database, analyzes it, and uses machine learning algorithms to improve the accuracy of future suggestions.

[1073] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[1074] Examples of prompts include:

[1075] - "Please explain the algorithm of the system that suggests low-salt foods and relaxation products to users who are stressed due to high blood pressure."

[1076] - "Explain the overall flow of a system that suggests trending products suitable for users based on their health data (e.g., heart rate, blood pressure, blood sugar level) and emotional data (facial expression, voice, text)."

[1077] This system allows users to enjoy the latest trendy products while maintaining their health and mental well-being.

[1078] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1079] Step 1:

[1080] The user enters health data (e.g., heart rate, blood pressure, blood glucose level) using a dedicated application. The entered data is collected in form fields in the application. The entered data is then stored on the device.

[1081] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[1082] Output: Health data stored on the device before encryption

[1083] Step 2:

[1084] The terminal encrypts the entered health data using the AES-256 encryption algorithm in order to send the data securely to the server.

[1085] Input: Health data before encryption

[1086] Output: Encrypted health data

[1087] Step 3:

[1088] The device transmits the encrypted health data to the server via the Internet. This transmission operation is to ensure that the data reaches the server while maintaining its confidentiality.

[1089] Input: Encrypted health data

[1090] Output: A request to send data to the server

[1091] Step 4:

[1092] The server decrypts the received data from its encrypted state. The decrypted data is stored in a health data database. The data is organized for each user.

[1093] Input: Encrypted health data

[1094] Output: Decrypted health data, stored in a database

[1095] Step 5:

[1096] In parallel, the emotion analysis engine collects the user's facial expressions, voice, and text input in real time. By analyzing this data, the emotion analysis engine identifies the user's emotional state. The emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology.

[1097] Input: User's facial expression data, voice data, text data

[1098] Output: Parsed emotion data

[1099] Step 6:

[1100] The emotion analysis engine sends the analyzed emotion data to the server, where it is stored in an emotion database.

[1101] Input: Parsed emotion data

[1102] Output: Send data to the server and save it in the emotion database

[1103] Step 7:

[1104] The server accesses the latest product databases on the Internet and EC site APIs to obtain the latest product information. The obtained data is organized by category and saved in a trending product database.

[1105] Input: API request

[1106] Output: Latest product information, saved in trend product database

[1107] Step 8:

[1108] The server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the stored health data and emotion data, and the best product for the user is selected.

[1109] Input: Health data, emotion data

[1110] Output: Selected product information

[1111] Step 9:

[1112] The server compiles the selected product information into a shopping list and sends it to the terminal, which receives the shopping list and notifies the user.

[1113] Input: Selected product information

[1114] Output: Shopping list, notifications to device

[1115] Step 10:

[1116] The user checks the shopping list on the device, selects the products they are interested in, and then completes the purchase process. The purchase information is saved on the device.

[1117] Input: Shopping list

[1118] Output: Purchased product information, saved on the device

[1119] Step 11:

[1120] When a user inputs feedback on a product, the terminal transmits the feedback to the server, and the feedback data is stored in the database of the server.

[1121] Input: User feedback

[1122] Output: Send data to server, save feedback in database

[1123] Step 12:

[1124] The server analyzes the received feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[1125] Input: Feedback data

[1126] Output: Updated machine learning model, improved suggestion accuracy

[1127] This processing step allows users to efficiently receive recommendations for trending products that fit their health and emotional state, and allows post-purchase feedback to be reflected in future recommendations.

[1128] (Application example 2)

[1129] 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."

[1130] Conventional systems only suggest products based on the user's health data, and are unable to consider the user's emotional state when making product suggestions, making it difficult to fully meet the user's needs. Furthermore, the accuracy of the feedback system used to improve the suitability of the suggested products is limited, preventing the system from increasing user satisfaction.

[1131] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving, saving, and analyzing the user's health data and emotional data, means for acquiring and saving the latest product information from a trend product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data and the acquired trend product information to select the product that is best suited to each user. This makes it possible to suggest products that are more suitable for each user, taking into account not only the user's health condition but also their emotional state.

[1132] A "terminal" is a device that allows a user to input their own health data and emotional data and transmit this to a server.

[1133] The "server" is a device that stores and analyzes the user's health data and emotional data received from the terminal, and processes product selection and user feedback.

[1134] "Emotional data" refers to data relating to a user's emotional state analyzed from the user's facial expressions, voice, text input, etc.

[1135] The "trend product database" is a database that manages the latest product information obtained from the Internet.

[1136] The "matching algorithm" is an algorithm that selects the most suitable product based on the user's health and emotional data.

[1137] A "machine learning algorithm" is an algorithm that continuously learns from user feedback and improves the accuracy of product suggestions.

[1138] The "Emotion AI Engine" is an artificial intelligence engine that analyzes a user's facial expressions, voice, and text to generate emotional data.

[1139] A "generative AI model" is a technology that uses natural language processing technology to generate prompts based on a user's emotional and health data and make product suggestions.

[1140] A "prompt sentence" is a natural language sentence that the generative AI model generates based on the user's emotional and health data, and serves as the starting point for product suggestions.

[1141] The present invention relates to a system that suggests the latest trending products based on a user's health and emotional data. The system is mainly composed of a terminal, a server, and an Emotion AI engine.

[1142] The terminal is a device through which users input health data such as heart rate, blood pressure, and blood sugar levels, as well as emotional data such as facial expressions, voice, and text input, and transmits this data to a server. The server stores and analyzes the health and emotional data received from the terminal, and acquires and stores the latest trending product information. The server also analyzes emotional states using an Emotion AI engine and generates emotional data.

[1143] The server runs a matching algorithm based on the health and emotional data received from the user to select the most suitable products. The selected products are then listed and sent to the user's device. The user can then review the suggested products on their device, select the products they are interested in, and purchase them.

[1144] The server also receives user feedback, stores this feedback data in a database, and updates it. The server then uses machine learning algorithms to improve the accuracy of future recommendations based on this feedback data, enabling it to recommend products that are more suitable for the user.

[1145] As a concrete example, in the case of User B, who suffers from high blood pressure and feels stressed, User B inputs health data such as heart rate and blood pressure, as well as emotional data via text input, into the device every day. The device sends this data to the server, which analyzes User B's data and suggests low-salt foods and relaxation products. The server also generates prompts using a generative AI model and uses natural language processing to suggest products.

[1146] For example, the following prompt sentence is fed into a generative AI model:

[1147] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[1148] The system allows users to receive customized product recommendations based on their health and emotional state, providing a higher quality shopping experience.

[1149] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1150] Step 1:

[1151] The user inputs health and emotional data into the device. Health data includes heart rate, blood pressure, blood sugar level, etc., while emotional data includes text input, voice data, and facial expression data. This data is temporarily stored on the device. The input data is then encrypted in the next step.

[1152] Step 2:

[1153] The device encrypts the health and emotion data entered and sends it to the server. Encryption ensures data security and prevents information sent from the device to the server via the Internet from being intercepted by third parties. The device encrypts the data using encryption technology such as AES (Advanced Encryption Standard) and sends it to the server.

[1154] Step 3:

[1155] The server receives the health and emotion data sent from the device and stores it in a database. The server decrypts the received encrypted data, stores it in the database, and then begins analysis. The database also stores past health and emotion data, which are referenced for analysis.

[1156] Step 4:

[1157] The server uses the Emotion AI engine to analyze the user's emotional data. Specifically, it performs text analysis, voice analysis, and facial expression analysis to identify the user's emotional state. In this process, the emotion engine uses a deep learning model to determine the emotion and generates new emotional data based on the results. It outputs emotional states such as "stress" or "joy" from the input text, voice, and facial expression data.

[1158] Step 5:

[1159] The server retrieves the latest product information from a trending product database on the Internet, organizes it by category, and saves it. The server periodically accesses databases on the Internet and various APIs to retrieve the latest product information. The retrieved product information is organized by category (e.g., food, wellness goods, etc.), and the organized information is saved in a local database.

[1160] Step 6:

[1161] The server runs a matching algorithm based on the user's health and emotional data and the acquired trending product information to select the most suitable products. Specifically, low-salt foods and relaxation goods are selected based on the user's health condition (e.g., high blood pressure) and emotional state (e.g., stress). The algorithm uses a machine learning model, and accuracy is improved based on past data and feedback.

[1162] Step 7:

[1163] The server creates a list of selected products and sends it to the user's device. The server creates a list of selected products and sends it to the user's device. The user can check the proposed product list on the device and select the products that interest them.

[1164] Step 8:

[1165] The user enters product feedback. The user enters their impressions of the purchased product and their level of satisfaction into the terminal using text input or a rating system, and the data is then sent back to the server. The feedback uses recommended prompts such as "How effective is this product?"

[1166] Step 9:

[1167] The server receives the feedback and updates the database. After receiving the feedback data, the server stores it in its database and uses machine learning algorithms to improve the accuracy of future recommendations. This enables product recommendations that are more tailored to the user's needs.

[1168] As a concrete example of how it works, input the following prompt sentence into the generative AI model:

[1169] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[1170] This allows the Emotion AI engine to identify the user's emotional state and suggest appropriate trending products.

[1171] 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.

[1172] 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.

[1173] 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.

[1174] [Fourth embodiment]

[1175] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1176] 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.

[1177] 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).

[1178] 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.

[1179] 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.

[1180] 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).

[1181] 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.

[1182] 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.

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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."

[1188] The present invention relates to a system for proposing the latest trend products based on a user's health data. Hereinafter, an embodiment of the present invention will be described.

[1189] This system is mainly composed of a user's "terminal" and a linked "server." The terminal and server communicate via the Internet.

[1190] First, the user inputs their own health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[1191] The server receives, stores, and analyzes the health data. The server then retrieves and stores the latest product information from trending product databases on the Internet and from e-commerce site APIs. The retrieved product information is organized by category.

[1192] The server then runs a matching algorithm based on the health data and the retrieved trending product information, selecting, for example, low-salt foods for a user with high blood pressure or low-impact fitness equipment for a user with arthritis.

[1193] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[1194] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[1195] As a concrete example, let's say Person A has high blood pressure and arthritis. Person A enters their blood pressure and the condition of their joints into their device every day. The device sends this data to a server, which analyzes Person A's data and suggests low-salt foods and fitness equipment that puts less strain on the joints. The suggested products are listed and sent to Person A's device, where Person A can check the list and make a purchase. Person A then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[1196] This completes the embodiment of the present invention. This system allows users to enjoy new trendy products while maintaining their health.

[1197] The processing flow will be explained below.

[1198] Step 1:

[1199] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) into the terminal.

[1200] Step 2:

[1201] The terminal encrypts the entered health data and sends it to the server.

[1202] Step 3:

[1203] The server receives the transmitted health data and stores it in a database.

[1204] Step 4:

[1205] The server analyzes the health data and evaluates the user's health status.

[1206] Step 5:

[1207] The server retrieves the latest product information from trending product databases on the Internet and from e-commerce site APIs.

[1208] Step 6:

[1209] The server organizes the acquired product information by category and stores it in a database.

[1210] Step 7:

[1211] The server runs a matching algorithm based on the user's health data and organized trend product information.

[1212] Step 8:

[1213] The server selects products that are best suited to the user's health condition and generates a shopping list.

[1214] Step 9:

[1215] The server transmits the generated shopping list to the terminal.

[1216] Step 10:

[1217] The terminal notifies the user of the received shopping list.

[1218] Step 11:

[1219] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[1220] Step 12:

[1221] The user inputs feedback on the purchased product into the terminal.

[1222] Step 13:

[1223] The terminal sends the feedback to the server.

[1224] Step 14:

[1225] The server updates the database based on the feedback received.

[1226] Step 15:

[1227] The server uses machine learning algorithms to analyze the data and improve the accuracy of future suggestions.

[1228] The above is a specific processing flow in the system of the present invention.

[1229] Example 1

[1230] 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."

[1231] While conventional health management systems can collect users' health data, they lack the functionality to suggest products tailored to each individual user based on that data. This results in users spending a great deal of time and effort to find the products they need. Furthermore, the list of suggested products does not necessarily meet the user's needs, resulting in low user satisfaction. Furthermore, the implementation of machine learning algorithms to effectively collect user feedback and improve the accuracy of future suggestions is insufficient. A new system to solve this issue is needed.

[1232] 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.

[1233] In this invention, the server includes a device for inputting and transmitting biometric information; a device for receiving, storing, and analyzing the biometric information; and a device for acquiring and storing the latest product information from a database. This enables optimal product recommendations based on the user's health data. Furthermore, the conventional problem can be effectively solved by combining a means for performing a matching process, listing selected products, and sending them to the user's device, and a device for receiving user feedback, updating the database, and executing an algorithm to improve the accuracy of future recommendations. Specifically, by incorporating a means for organizing and storing the latest product information by category, the accuracy of recommending optimal products to each individual user is improved. Furthermore, by transmitting and receiving data using encrypted communications, personal information protection and data security are enhanced. This allows users to efficiently discover and purchase the latest trendy products while maintaining their health.

[1234] "Biometric information" refers to data related to the user's health condition, such as heart rate, blood pressure, and blood sugar level.

[1235] "Device" refers to equipment that allows a user to input, send, receive, and display biometric information.

[1236] A "server" is a computer system that communicates with the device via the Internet, receives, stores, and analyzes biometric information, and acquires and manages product information.

[1237] A "database" is a storage system that allows a server to systematically store and manage information.

[1238] "Matching processing" is a process of selecting products that are suitable for the user's biometric information using a matching algorithm.

[1239] "Product information" is information about the latest trending products obtained from the database and organized by category.

[1240] "Listing" is the process by which the server organizes the selected products into an easy-to-read format so that the user can view them.

[1241] An "algorithm" is a set of calculation procedures or rules that run on a server and are used for data analysis, matching, and machine learning.

[1242] "Encrypted communication" is a technology that encrypts data in order to securely send and receive data over the Internet.

[1243] The present invention relates to a system that proposes optimal products based on a user's biometric information. The following describes an embodiment of the present invention. This system is primarily composed of a user's "terminal" and a linked "server." The terminal and server communicate with each other via the Internet.

[1244] Hardware configuration

[1245] Device: A device used by the user, such as a smartphone or tablet, on which a dedicated health management application is installed.

[1246] Server: A high-performance cloud server will be used to analyze data, execute matching algorithms, and manage the database. For example, AWS or Google Cloud can be used.

[1247] Software configuration

[1248] Terminal application: Has the function to input and transmit the user's biometric information. Data is transmitted securely using encrypted communication (HTTPS).

[1249] Server-side program: Consists of the following modules:

[1250] 1. Data Reception Module: Receives and decodes biometric information sent from the device. It uses an encrypted communication protocol (AES-256).

[1251] 2. Data analysis module: Analyzes the received data and stores it in a database.

[1252] 3. Product information acquisition module: Acquires the latest product information from a trending product database or an e-commerce site API (e.g., Amazon API, Rakuten API).

[1253] 4. Matching module: Executes a matching algorithm (e.g., KNN: nearest neighbor algorithm) based on biometric information and product information to select the most suitable product for each individual user.

[1254] 5. List generation module: Compiles the selected products into a shopping list, converts it into JSON format, and sends it to the terminal.

[1255] 6. Feedback collection module: Receives user feedback and updates the database. It uses machine learning algorithms (e.g., random forest) to improve the accuracy of future suggestions.

[1256] System Operation

[1257] 1. User biometric information input and transmission:

[1258] A user uses a dedicated health management app to input biometric information such as heart rate, blood pressure, and blood glucose level. For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[1259] The input data is encrypted within the terminal and sent to the server.

[1260] 2. Server data reception and analysis:

[1261] The server decrypts the received encrypted data and stores it in a database.

[1262] At the same time, the latest trending product information is obtained from databases and APIs on the Internet, and is organized and stored by category.

[1263] 3. Matching biometric information with product information:

[1264] The server selects the most suitable product based on biometric and product information, for example, recommending low-salt foods to a user with high blood pressure and fitness products that are gentle on the joints to a user with arthritis.

[1265] 4. Shopping list generation and notification:

[1266] The server creates a list of selected products, converts it into JSON format, and sends it to the terminal.

[1267] The terminal receives the list and notifies the user that a new product list has arrived.

[1268] 5. User feedback and database updates:

[1269] The user inputs feedback about the purchased product and sends it to the server from the terminal. For example, the user might say, "The low-salt food tasted good, but I'd like the packaging to be improved."

[1270] The server receives the feedback and uses machine learning algorithms to update the database and improve the accuracy of the suggestions.

[1271] Prompt Sentence Examples

[1272] "Based on my health data (blood pressure: 130 / 85, joint pain level: 4), please suggest the latest low-sodium foods and joint-friendly fitness gear."

[1273] This allows users to efficiently discover new trendy products and maintain their health. The seamless process of inputting biometric information, suggesting products, and collecting feedback also increases user satisfaction.

[1274] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1275] Step 1: Enter and send user biometric information

[1276] A user uses a dedicated health management app to input biometric information (e.g., heart rate, blood pressure, blood glucose level, etc.). For example, the user inputs "Today's heart rate is 120, blood pressure is 130 / 85, and blood glucose level is 110 mg / dL."

[1277] Input: Biometric information such as heart rate, blood pressure, and blood sugar level

[1278] The device encrypts this data using the AES-256 encryption algorithm and sends it to the server using HTTPS.

[1279] Output: Encrypted biometric information

[1280] Step 2: Receiving and storing biometric information on the server

[1281] The server receives the encrypted data sent from the terminal and ensures security using TLS (Transport Layer Security).

[1282] Input: Encrypted biometric information

[1283] The server decrypts the received data using AES-256 and stores the decrypted data in a health database.

[1284] Output: Stored biometric information

[1285] Step 3: Obtain and organize trending product information

[1286] The server periodically retrieves the latest product information from trending product databases on the Internet or from e-commerce site APIs, such as Amazon API or Rakuten API.

[1287] Input: Latest product information from trending product databases and e-commerce site APIs

[1288] The server classifies the acquired product information by category and stores it in a product database.

[1289] Output: Product information organized by category

[1290] Step 4: Matching biometric information with product information

[1291] The server runs a matching algorithm based on the stored biometric information and product information, using KNN (Kind Neighbor Neighborhood Algorithm) to select the product that best suits the user's needs.

[1292] Input: Stored biometric information, product information organized by category

[1293] Output: A list of products that are best suited for the user

[1294] Step 5: Generate and submit your shopping list

[1295] The server compiles the selected items into a shopping list and converts it into JSON format.

[1296] Input: A list of products that are best suited for the user

[1297] The server sends the shopping list in JSON format to the device using HTTPS.

[1298] Output: Shopping list in JSON format

[1299] Step 6: Shopping list notification and purchase process

[1300] The device displays the shopping list received from the server on the user interface (UI), for example, displaying product images, descriptions, prices, purchase buttons, etc. on the app screen.

[1301] Input: Shopping list in JSON format

[1302] The user reviews the list, selects the products they are interested in, and a purchase link to the e-commerce site is generated.

[1303] Output: User selected products and corresponding purchase links

[1304] Step 7: User feedback and submission

[1305] The user inputs feedback about the purchased product into the terminal. For example, the user inputs feedback such as "The low-salt food tasted good, but I would like the packaging to be improved."

[1306] Input: User feedback

[1307] The device encrypts the feedback data and sends it to the server using HTTPS.

[1308] Output: Encrypted feedback data

[1309] Step 8: Receive feedback and update the database

[1310] The server receives the encrypted feedback sent from the device and decrypts it using AES-256.

[1311] Input: Encrypted feedback data

[1312] The decoded data is stored in a feedback database and analyzed using machine learning algorithms to improve the accuracy of future suggestions.

[1313] Output: Updated feedback data, learning results for improved accuracy

[1314] The above are the specific processing steps of the system program. This system allows users to efficiently discover new trend products and manage their health.

[1315] (Application example 1)

[1316] 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."

[1317] In today's world, many consumers are concerned about their health, but there are only a limited number of systems that provide optimal products to individual users based on their health data. Consumers have difficulty finding the right products for them from the vast number of products available, and there is also a lack of information about new trendy products. For this reason, there is a need for a system that recommends optimal health and trendy products to individual users based on their health data, and allows them to seamlessly purchase them.

[1318] 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.

[1319] In this invention, the server includes a terminal with a function that allows the user to purchase suggested products, a means for executing a matching algorithm based on health data and product information, a means for organizing and saving the acquired trend product information by category, and a means for notifying the user of a shopping list, thereby enabling the user to easily find and purchase the most suitable products based on their own health data.

[1320] A "terminal" is a device for inputting a user's health data and sending it to a server.

[1321] The "server" is a system that receives, stores, and analyzes users' health data, and also obtains the latest product information from a trending product database on the Internet.

[1322] The "Internet Trending Product Database" is an online database that collects the latest product information.

[1323] The "matching algorithm" is a calculation method for selecting the most suitable products for each individual user based on the user's health data and acquired trend product information.

[1324] A "shopping list" is a list of selected products that is sent to the user's terminal.

[1325] "Feedback" is information that allows users to input their impressions and ratings of purchased products.

[1326] A "machine learning algorithm" is an algorithm that updates the database based on user feedback and improves the accuracy of suggestions from the next time onwards.

[1327] "Organizing by category" means classifying and saving the acquired trending product information by type.

[1328] "Encrypted communication" is a method of communicating the input, transmission, and reception of a user's health data as encrypted data between the terminal and the server.

[1329] The "function to purchase products suggested to the user" is a function that allows the user to select the suggested products on the device and complete the purchase procedure.

[1330] MODE FOR CARRYING OUT THE INVENTION

[1331] The present invention relates to a system that suggests the latest trend products based on a user's health data, and detailed embodiments thereof will be described below.

[1332] System configuration

[1333] This system mainly consists of the following elements:

[1334] 1. Terminal: A device for inputting the user's health data and sending it to the server. In this system, a smartphone is used.

[1335] 2. Server: This system receives, stores, and analyzes users' health data. It also retrieves and stores the latest product information from a trending product database on the Internet.

[1336] 3. Online trending product database: An online database that collects the latest product information.

[1337] 4. Matching algorithm: A calculation method for selecting the most suitable product for each individual user based on the user's health data and acquired trend product information.

[1338] 5. Shopping list: A list of selected products is created and sent to the user's device.

[1339] 6. Feedback: Information for users to enter their impressions and ratings of the products they have purchased.

[1340] 7. Machine learning algorithm: This algorithm updates the database based on user feedback and improves the accuracy of future suggestions.

[1341] 8. A means of organizing by category: This is a means of classifying and saving the acquired trending product information by type.

[1342] 9. Encrypted communication: A method in which the input, transmission, and reception of a user's health data is communicated as encrypted data between the terminal and the server.

[1343] 10. Ability to purchase products suggested to users: This function allows users to select suggested products on their device and complete the purchase process.

[1344] Implementation method

[1345] The user enters their own health data into the device. The entered health data is encrypted and sent to a server via the Internet using the Fernet encryption method. The server receives, stores, and analyzes this data. The server also retrieves and stores the latest product information from a trending product database on the Internet and from e-commerce site APIs.

[1346] The server organizes and stores the acquired product information by category. The server then runs a matching algorithm based on the health data and product information to select the most suitable products for each individual user. The selected products are then compiled into a shopping list and sent to the user's device.

[1347] Users receive this shopping list on their device and can select and purchase items that interest them. The device also has a built-in function for carrying out the purchase process. Furthermore, if the user enters feedback on the purchased item, it is sent to the server. The server updates the database based on this feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[1348] Specific examples

[1349] For example, if a user has high blood pressure and arthritis, the user enters their blood pressure and the condition of their joints into their smartphone every day. The device encrypts this data and sends it to a server. The server analyzes the data and suggests low-salt foods and fitness equipment that are gentle on the joints that are best suited to the user. A list of suggested products is then sent to the user's device. The user can review the list and purchase any products that interest them. After that, the user can enter their thoughts on the products, and the feedback is sent to the server, which updates the database.

[1350] Prompt Sentence Examples

[1351] We would like to build a system that recommends the latest trending products based on a user's health data (heart rate, blood pressure, blood sugar level, etc.). The relevant data will be obtained from an online trending product database or an e-commerce site API, and will be compared with the health data to suggest products. Please provide an algorithm that will encrypt the JSON-formatted health data using Fernet and send it to the server.

[1352] This invention allows users to easily find and purchase the latest trending products while maintaining their health. The server constantly analyzes product information and health data to improve the accuracy of recommendations, providing users with the best options.

[1353] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1354] Step 1:

[1355] The user inputs health data into the device, such as heart rate, blood pressure, and blood sugar levels, through a smartphone application, and the data is converted into JSON format.

[1356] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[1357] Output: Health data in JSON format

[1358] Step 2:

[1359] The device encrypts the entered health data using the Fernet encryption method, and the data is protected by an encryption key.

[1360] Input: Health data in JSON format, encryption key

[1361] Output: Encrypted health data (binary format)

[1362] Step 3:

[1363] The device sends the encrypted health data to the server via a POST request over the internet.

[1364] Input: Encrypted health data

[1365] Output: Data transmission status to the server (success / failure)

[1366] Step 4:

[1367] The server stores the received health data and performs analysis, processing the data to evaluate the user's health status.

[1368] Input: Encrypted health data

[1369] Output: Analysis results (health status evaluation data)

[1370] Step 5:

[1371] The server retrieves and stores the latest product information from a trending product database on the Internet. Product information is retrieved using an API and organized by category.

[1372] Input: API request (endpoint and parameters)

[1373] Output: Product information organized by category

[1374] Step 6:

[1375] The server runs a matching algorithm based on health data and product information to select the best products for each individual user. The algorithm filters out appropriate products based on health status.

[1376] Input: Analysis results, product information

[1377] Output: A list of selected products

[1378] Step 7:

[1379] The server creates a shopping list of the selected items and sends it to the user's device. The list is then displayed on the device via a notification function.

[1380] Input: List of selected products

[1381] Output: Send shopping list to device (notification)

[1382] Step 8:

[1383] The user checks the shopping list, selects the items they are interested in, and purchases them. The purchase process is completed through the device application.

[1384] Input: Shopping list

[1385] Output: Purchase completion status

[1386] Step 9:

[1387] Users enter feedback on the product they have purchased, and the device sends that feedback to the server, where the ratings and comments are encrypted again and sent to the server.

[1388] Input: User feedback

[1389] Output: Feedback data sent to server

[1390] Step 10:

[1391] The server updates the database based on the feedback it receives and uses machine learning algorithms to improve the accuracy of future suggestions.

[1392] Input: Feedback data

[1393] Output: Updated database, improved proposed algorithm

[1394] 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.

[1395] The present invention relates to a system for proposing the latest trend products based on health data and emotion data of a user. Hereinafter, an embodiment of the present invention will be described.

[1396] This system is primarily composed of the user's "terminal," a linked "server," and an "emotion engine" that recognizes the user's emotions. The terminal and server communicate via the Internet, and the emotion engine is installed on either the terminal or the server.

[1397] First, the user inputs health data (e.g., heart rate, blood pressure, blood sugar level, etc.) into the device. The device then encrypts the input health data and sends it to the server.

[1398] The server receives the health data, stores it in a database, and analyzes it. In parallel, the emotion engine analyzes the user's emotions from facial expressions, voice, text input, etc., and sends the emotion data to the server.

[1399] Next, the server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[1400] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure who is stressed might be matched with low-salt foods and relaxation products.

[1401] The selected products are compiled into a shopping list and sent from the server to the user's device. The device receives this shopping list and notifies the user. The user can then check the list of suggested products on their device, select the products they are interested in, and purchase them.

[1402] Furthermore, when a user enters feedback on a purchased item, the device sends this feedback back to the server, which then updates the database based on the received feedback and uses machine learning algorithms to improve the accuracy of future recommendations.

[1403] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[1404] The above is an embodiment of the present invention. This system allows users to maintain their health, improve their mental stability, and enjoy new trendy products.

[1405] The processing flow will be explained below.

[1406] Step 1:

[1407] The user inputs health data (heart rate, blood pressure, blood sugar level, etc.) and emotional data (text, voice, facial expression, etc.) into the terminal.

[1408] Step 2:

[1409] The terminal encrypts the input health data and emotion data and transmits them to the server.

[1410] Step 3:

[1411] The server receives the transmitted health data and emotion data and stores them in respective databases.

[1412] Step 4:

[1413] The server analyzes the health data to assess the user's health status.

[1414] Step 5:

[1415] An emotion engine analyzes the transmitted emotion data and evaluates the user's emotional state.

[1416] Step 6:

[1417] The server retrieves the latest product information from trending product databases on the Internet and e-commerce site APIs, organizes it by category, and stores it in a database.

[1418] Step 7:

[1419] The server runs a matching algorithm based on the user's health and emotional data. For example, a user with high blood pressure and stress might be recommended low-salt foods and relaxation products.

[1420] Step 8:

[1421] The server generates a shopping list of the selected items and transmits it to the user's terminal.

[1422] Step 9:

[1423] The terminal notifies the user of the received shopping list.

[1424] Step 10:

[1425] The user checks the list of suggested products on the terminal, selects the products that interest them, and purchases them.

[1426] Step 11:

[1427] The user inputs feedback about the purchased item into the terminal.

[1428] Step 12:

[1429] The terminal sends the feedback to the server.

[1430] Step 13:

[1431] The server updates the database based on the feedback received.

[1432] Step 14:

[1433] The server uses machine learning algorithms to analyze the feedback data and improve the accuracy of future suggestions.

[1434] The above is a specific processing flow in the system of the present invention.

[1435] Example 2

[1436] 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."

[1437] Conventional health management systems only utilize users' health data and do not take into account their emotional data when making suggestions. This makes it difficult to suggest products that are appropriate for the user's mental state, making it difficult to improve overall satisfaction. Furthermore, the accuracy of trending product suggestions is low, and there is a lack of a mechanism for effectively incorporating user feedback into future suggestions. There is a need to solve these problems and improve the quality of product suggestions to users.

[1438] 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.

[1439] In this invention, the server includes means for receiving, storing, and analyzing the user's health data, means for acquiring and storing information from the latest product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data to select the most suitable product for each user. This makes it possible to propose the most suitable product from trending products, taking into consideration the user's health and emotional state comprehensively.

[1440] "Health data" refers to information that represents a user's physical condition, such as heart rate, blood pressure, and blood sugar level.

[1441] "Device" means an electronic device used by a user to input and receive health data and emotional data.

[1442] "Server" means a central computer system that stores and analyzes received data, and retrieves and suggests product information.

[1443] The "emotion analysis engine" is a function for analyzing emotional data from users' facial expressions, voice, text input, etc.

[1444] The "Trend Product Database" is a database that stores the latest product information available on the Internet.

[1445] A "matching algorithm" is a calculation method for selecting the most suitable product based on the user's health and emotional data.

[1446] A "machine learning algorithm" is a self-learning algorithm that improves the accuracy of suggestions based on feedback from users.

[1447] "Encrypted communication" is a method of communicating by encrypting information in order to send and receive data safely.

[1448] "Feedback" refers to information such as ratings and impressions provided by users regarding purchased products.

[1449] A "shopping list" is a list that displays selected products in a list format so that the user can check and purchase them.

[1450] The present invention relates to a system that suggests the latest trending products based on a user's health data and emotional data. The system is primarily composed of the user's "terminal," a linked "server," and an "emotion analysis engine" that recognizes the user's emotions. The following describes in detail the mode for carrying out the invention.

[1451] First, the user inputs their health data (e.g., heart rate, blood pressure, blood sugar level) using a dedicated application. The device then encrypts the input health data using the AES-256 encryption algorithm and transmits it to a server via the Internet.

[1452] The server decrypts the received health data and stores it in a health data database, which is organized for each user and comprehensively manages the health status of the entire system.

[1453] In parallel, the emotion analysis engine collects and analyzes emotional data in real time from the user's facial expressions, voice, and text input. This emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology. The obtained emotional data is stored on the device or server.

[1454] The server accesses the latest product databases on the Internet and the APIs of e-commerce sites to obtain the latest product information. This information is also organized by category and stored in the trend product database.

[1455] Next, the server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the user's health data and emotion data to select the most suitable products for the user. The selected products are compiled into a shopping list and sent to the device. The device receives this shopping list and notifies the user.

[1456] The user can check the list of suggested products on the terminal, select the product they are interested in, and purchase it. After completing the purchase procedure, the user provides feedback on the purchased product, which is then sent back to the server via the terminal.

[1457] The server stores the received feedback in a database, analyzes it, and uses machine learning algorithms to improve the accuracy of future suggestions.

[1458] As a concrete example, let's say Person B has high blood pressure and is feeling stressed. Person B enters their blood pressure data and emotional state into their device every day. The device sends this data to a server, which analyzes Person B's data and suggests low-salt foods and relaxation goods. The suggested products are listed and sent to Person B's device, where they can check the list and purchase. Person B then enters their thoughts on the products, and that feedback is sent to the server and reflected in the next suggestions.

[1459] Examples of prompts include:

[1460] - "Please explain the algorithm of the system that suggests low-salt foods and relaxation products to users who are stressed due to high blood pressure."

[1461] - "Explain the overall flow of a system that suggests trending products suitable for users based on their health data (e.g., heart rate, blood pressure, blood sugar level) and emotional data (facial expression, voice, text)."

[1462] This system allows users to enjoy the latest trendy products while maintaining their health and mental well-being.

[1463] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1464] Step 1:

[1465] The user enters health data (e.g., heart rate, blood pressure, blood glucose level) using a dedicated application. The entered data is collected in form fields in the application. The entered data is then stored on the device.

[1466] Input: Health data such as heart rate, blood pressure, and blood sugar levels

[1467] Output: Health data stored on the device before encryption

[1468] Step 2:

[1469] The terminal encrypts the entered health data using the AES-256 encryption algorithm in order to send the data securely to the server.

[1470] Input: Health data before encryption

[1471] Output: Encrypted health data

[1472] Step 3:

[1473] The device transmits the encrypted health data to the server via the Internet. This transmission operation is to ensure that the data reaches the server while maintaining its confidentiality.

[1474] Input: Encrypted health data

[1475] Output: A request to send data to the server

[1476] Step 4:

[1477] The server decrypts the received data from its encrypted state. The decrypted data is stored in a health data database. The data is organized for each user.

[1478] Input: Encrypted health data

[1479] Output: Decrypted health data, stored in a database

[1480] Step 5:

[1481] In parallel, the emotion analysis engine collects the user's facial expressions, voice, and text input in real time. By analyzing this data, the emotion analysis engine identifies the user's emotional state. The emotion analysis engine uses facial recognition technology, voice analysis technology, and natural language processing technology.

[1482] Input: User's facial expression data, voice data, text data

[1483] Output: Parsed emotion data

[1484] Step 6:

[1485] The emotion analysis engine sends the analyzed emotion data to the server, where it is stored in an emotion database.

[1486] Input: Parsed emotion data

[1487] Output: Send data to the server and save it in the emotion database

[1488] Step 7:

[1489] The server accesses the latest product databases on the Internet and EC site APIs to obtain the latest product information. The obtained data is organized by category and saved in a trending product database.

[1490] Input: API request

[1491] Output: Latest product information, saved in trend product database

[1492] Step 8:

[1493] The server runs a matching algorithm (e.g., k-nearest neighbor or deep learning) based on both the stored health data and emotion data, and the best product for the user is selected.

[1494] Input: Health data, emotion data

[1495] Output: Selected product information

[1496] Step 9:

[1497] The server compiles the selected product information into a shopping list and sends it to the terminal, which receives the shopping list and notifies the user.

[1498] Input: Selected product information

[1499] Output: Shopping list, notifications to device

[1500] Step 10:

[1501] The user checks the shopping list on the device, selects the products they are interested in, and then completes the purchase process. The purchase information is saved on the device.

[1502] Input: Shopping list

[1503] Output: Purchased product information, saved on the device

[1504] Step 11:

[1505] When a user inputs feedback on a product, the terminal transmits the feedback to the server, and the feedback data is stored in the database of the server.

[1506] Input: User feedback

[1507] Output: Send data to server, save feedback in database

[1508] Step 12:

[1509] The server analyzes the received feedback and uses machine learning algorithms to improve the accuracy of future suggestions.

[1510] Input: Feedback data

[1511] Output: Updated machine learning model, improved suggestion accuracy

[1512] This processing step allows users to efficiently receive recommendations for trending products that fit their health and emotional state, and allows post-purchase feedback to be reflected in future recommendations.

[1513] (Application example 2)

[1514] 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."

[1515] Conventional systems only suggest products based on the user's health data, and are unable to consider the user's emotional state when making product suggestions, making it difficult to fully meet the user's needs. Furthermore, the accuracy of the feedback system used to improve the suitability of the suggested products is limited, preventing the system from increasing user satisfaction.

[1516] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving, saving, and analyzing the user's health data and emotional data, means for acquiring and saving the latest product information from a trend product database on the Internet, and means for executing a matching algorithm based on the user's health data and emotional data and the acquired trend product information to select the product that is best suited to each user. This makes it possible to suggest products that are more suitable for each user, taking into account not only the user's health condition but also their emotional state.

[1517] A "terminal" is a device that allows a user to input their own health data and emotional data and transmit this to a server.

[1518] The "server" is a device that stores and analyzes the user's health data and emotional data received from the terminal, and processes product selection and user feedback.

[1519] "Emotional data" refers to data relating to a user's emotional state analyzed from the user's facial expressions, voice, text input, etc.

[1520] The "trend product database" is a database that manages the latest product information obtained from the Internet.

[1521] The "matching algorithm" is an algorithm that selects the most suitable product based on the user's health and emotional data.

[1522] A "machine learning algorithm" is an algorithm that continuously learns from user feedback and improves the accuracy of product suggestions.

[1523] The "Emotion AI Engine" is an artificial intelligence engine that analyzes a user's facial expressions, voice, and text to generate emotional data.

[1524] A "generative AI model" is a technology that uses natural language processing technology to generate prompts based on a user's emotional and health data and make product suggestions.

[1525] A "prompt sentence" is a natural language sentence that the generative AI model generates based on the user's emotional and health data, and serves as the starting point for product suggestions.

[1526] The present invention relates to a system that suggests the latest trending products based on a user's health and emotional data. The system is mainly composed of a terminal, a server, and an Emotion AI engine.

[1527] The terminal is a device through which users input health data such as heart rate, blood pressure, and blood sugar levels, as well as emotional data such as facial expressions, voice, and text input, and transmits this data to a server. The server stores and analyzes the health and emotional data received from the terminal, and acquires and stores the latest trending product information. The server also analyzes emotional states using an Emotion AI engine and generates emotional data.

[1528] The server runs a matching algorithm based on the health and emotional data received from the user to select the most suitable products. The selected products are then listed and sent to the user's device. The user can then review the suggested products on their device, select the products they are interested in, and purchase them.

[1529] The server also receives user feedback, stores this feedback data in a database, and updates it. The server then uses machine learning algorithms to improve the accuracy of future recommendations based on this feedback data, enabling it to recommend products that are more suitable for the user.

[1530] As a concrete example, in the case of User B, who suffers from high blood pressure and feels stressed, User B inputs health data such as heart rate and blood pressure, as well as emotional data via text input, into the device every day. The device sends this data to the server, which analyzes User B's data and suggests low-salt foods and relaxation products. The server also generates prompts using a generative AI model and uses natural language processing to suggest products.

[1531] For example, the following prompt sentence is fed into a generative AI model:

[1532] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[1533] The system allows users to receive customized product recommendations based on their health and emotional state, providing a higher quality shopping experience.

[1534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1535] Step 1:

[1536] The user inputs health and emotional data into the device. Health data includes heart rate, blood pressure, blood sugar level, etc., while emotional data includes text input, voice data, and facial expression data. This data is temporarily stored on the device. The input data is then encrypted in the next step.

[1537] Step 2:

[1538] The device encrypts the health and emotion data entered and sends it to the server. Encryption ensures data security and prevents information sent from the device to the server via the Internet from being intercepted by third parties. The device encrypts the data using encryption technology such as AES (Advanced Encryption Standard) and sends it to the server.

[1539] Step 3:

[1540] The server receives the health and emotion data sent from the device and stores it in a database. The server decrypts the received encrypted data, stores it in the database, and then begins analysis. The database also stores past health and emotion data, which are referenced for analysis.

[1541] Step 4:

[1542] The server uses the Emotion AI engine to analyze the user's emotional data. Specifically, it performs text analysis, voice analysis, and facial expression analysis to identify the user's emotional state. In this process, the emotion engine uses a deep learning model to determine the emotion and generates new emotional data based on the results. It outputs emotional states such as "stress" or "joy" from the input text, voice, and facial expression data.

[1543] Step 5:

[1544] The server retrieves the latest product information from a trending product database on the Internet, organizes it by category, and saves it. The server periodically accesses databases on the Internet and various APIs to retrieve the latest product information. The retrieved product information is organized by category (e.g., food, wellness goods, etc.), and the organized information is saved in a local database.

[1545] Step 6:

[1546] The server runs a matching algorithm based on the user's health and emotional data and the acquired trending product information to select the most suitable products. Specifically, low-salt foods and relaxation goods are selected based on the user's health condition (e.g., high blood pressure) and emotional state (e.g., stress). The algorithm uses a machine learning model, and accuracy is improved based on past data and feedback.

[1547] Step 7:

[1548] The server creates a list of selected products and sends it to the user's device. The server creates a list of selected products and sends it to the user's device. The user can check the proposed product list on the device and select the products that interest them.

[1549] Step 8:

[1550] The user enters product feedback. The user enters their impressions of the purchased product and their level of satisfaction into the terminal using text input or a rating system, and the data is then sent back to the server. The feedback uses recommended prompts such as "How effective is this product?"

[1551] Step 9:

[1552] The server receives the feedback and updates the database. After receiving the feedback data, the server stores it in its database and uses machine learning algorithms to improve the accuracy of future recommendations. This enables product recommendations that are more tailored to the user's needs.

[1553] As a concrete example of how it works, input the following prompt sentence into the generative AI model:

[1554] "I'm feeling very tired and stressed today. Please suggest products that suit me based on these emotions."

[1555] This allows the Emotion AI engine to identify the user's emotional state and suggest appropriate trending products.

[1556] 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.

[1557] 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.

[1558] 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 robot 414.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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).

[1563] 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.

[1564] 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."

[1565] 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.

[1566] 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).

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] The following is further disclosed regarding the above embodiment.

[1578] (Claim 1)

[1579] a terminal for inputting and transmitting health data of a user;

[1580] a server for receiving, storing, and analyzing users' health data;

[1581] a server for acquiring and storing the latest product information from a trending product database on the Internet;

[1582] A server that executes a matching algorithm based on the user's health data and the acquired trend product information to select the most suitable product for each user;

[1583] a server for listing the selected products and transmitting the list to the user's terminal;

[1584] a server that receives user feedback, updates the database, and runs machine learning algorithms to improve the accuracy of future suggestions;

[1585] A system including:

[1586] (Claim 2)

[1587] 2. The system according to claim 1, further comprising: means for organizing and storing the acquired trend product information by category; and means for executing a matching algorithm.

[1588] (Claim 3)

[1589] 2. The system of claim 1, further comprising means for inputting, transmitting, and receiving the user's health data using encrypted communication between the terminal and the server.

[1590] "Example 1"

[1591] (Claim 1)

[1592] a device for inputting and transmitting biometric information of a user;

[1593] a device for receiving, storing, and analyzing a user's biometric information;

[1594] a device for retrieving and storing the latest product information from the database;

[1595] a device for executing a matching process based on the biometric information of the user and the acquired product information to select the most suitable product for each user;

[1596] a device for listing the selected products and transmitting the listing to a user's device;

[1597] a device that receives user feedback, updates the database, and executes algorithms to improve the accuracy of future suggestions;

[1598] A system including:

[1599] (Claim 2)

[1600] 2. The system according to claim 1, further comprising: means for organizing and storing the acquired product information by category; and means for executing a matching process.

[1601] (Claim 3)

[1602] 10. The system according to claim 1, further comprising means for inputting, transmitting, and receiving the user's biometric information using encrypted communication between the devices.

[1603] "Application Example 1"

[1604] (Claim 1)

[1605] a terminal for inputting and transmitting health data of a user;

[1606] a server for receiving, storing, and analyzing users' health data;

[1607] a server for acquiring and storing the latest product information from a trending product database on the Internet;

[1608] A server that executes a matching algorithm based on the user's health data and the acquired trend product information to select the most suitable product for each user;

[1609] a server for listing the selected products and transmitting the list to the user's terminal;

[1610] a server that receives user feedback, updates the database, and runs machine learning algorithms to improve the accuracy of future suggestions;

[1611] a terminal including a function for allowing a user to purchase products suggested to the user;

[1612] means for executing a matching algorithm based on health data and product information;

[1613] A system including:

[1614] (Claim 2)

[1615] 2. The system according to claim 1, further comprising: means for organizing and saving the acquired trend product information by category; and means for notifying the user of a shopping list.

[1616] (Claim 3)

[1617] 2. The system of claim 1, further comprising means for inputting, transmitting, and receiving the user's health data using encrypted communication between the terminal and the server.

[1618] "Example 2: Combining Emotion Engines"

[1619] (Claim 1)

[1620] a terminal for inputting and transmitting health data of a user;

[1621] a server for receiving, storing, and analyzing users' health data;

[1622] a server for retrieving and storing information from the latest product database on the Internet;

[1623] A server that executes a matching algorithm based on the user's health data and emotional data to select the most suitable product for each user;

[1624] an emotion analysis engine for analyzing user emotions and transmitting the data to a server;

[1625] a server for listing the selected products and transmitting the list to the user's terminal;

[1626] a server that receives user feedback, updates the database, and runs machine learning algorithms to improve the accuracy of future suggestions;

[1627] A system including:

[1628] (Claim 2)

[1629] 2. The system according to claim 1, further comprising: means for organizing and storing the acquired latest product information by category; and means for executing a matching algorithm.

[1630] (Claim 3)

[1631] 2. The system according to claim 1, further comprising means for inputting, transmitting, and receiving the user's health data and emotion data using encrypted communication between the terminal and the server.

[1632] "Application example 2 when combining emotion engines"

[1633] (Claim 1)

[1634] a terminal for inputting and transmitting health data and emotional data of a user;

[1635] a server for receiving, storing, and analyzing the user's health data and emotion data;

[1636] a server for acquiring and storing the latest product information from a trending product database on the Internet;

[1637] A server that executes a matching algorithm based on the health and emotional data of users and the acquired trend product information to select the most suitable product for each user;

[1638] a server for listing the selected products and transmitting the list to the user's terminal;

[1639] a server that receives user feedback, updates the database, and runs machine learning algorithms to improve the accuracy of future suggestions;

[1640] A means for analyzing the user's emotional state and generating emotional data by utilizing the database and Emotion AI engine;

[1641] A means for generating prompt sentences based on the user's emotional data and health data using a generative AI model and for making product suggestions using natural language processing;

[1642] A system including:

[1643] (Claim 2)

[1644] 2. The system according to claim 1, further comprising: means for organizing and storing the acquired trend product information by category; and means for executing a matching algorithm.

[1645] (Claim 3)

[1646] 2. The system according to claim 1, further comprising means for inputting, transmitting, and receiving the user's health data and emotion data using encrypted communication between the terminal and the server. [Explanation of symbols]

[1647] 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 terminal for inputting and transmitting health data of a user; a server for receiving, storing, and analyzing users' health data; a server for acquiring and storing the latest product information from a trending product database on the Internet; A server that executes a matching algorithm based on the user's health data and the acquired trend product information to select the most suitable product for each user; a server for listing the selected products and transmitting the list to the user's terminal; a server that receives user feedback, updates the database, and runs machine learning algorithms to improve the accuracy of future suggestions; A system including:

2. 2. The system according to claim 1, further comprising: means for organizing and storing the acquired trend product information by category; and means for executing a matching algorithm.

3. 2. The system according to claim 1, further comprising means for inputting, transmitting, and receiving the user's health data using encrypted communication between the terminal and the server.

Images