system

Abstract

We provide the system. [Solution] A device for collecting the user's biometric data during and after exercise, A terminal for sending collected data to a server equipped with AI for generating data, The generating AI analyzes the collected data and identifies nutrients that the user is lacking, A means for selecting the optimal beverage from multiple beverages in nearby vending machines based on identified nutrients, A means of notifying the user's terminal of the selected beverage information, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, there is an increasing trend towards health consciousness, and it has become common for individuals to exercise daily. Although many people use smart devices to collect body data, it is difficult to accurately understand what their body needs after exercise. In particular, it is difficult for ordinary people to understand which nutrients are lacking after exercise and to appropriately replenish them. Against this background, it has been an issue that nutritional supplementation after exercise is insufficient and the hard-won exercise effects cannot be maximally exerted.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides the following means. First, a smartwatch or the like is used as a device to collect the user's biometric data during and after exercise. Next, a smartphone is used as a terminal to transmit the collected data to a server equipped with a generating AI. The generating AI then analyzes the collected data and identifies nutrients that the user is lacking. Based on these identified nutrients, the generating AI selects the most suitable beverage from several beverages available in a nearby vending machine and notifies the terminal of this selection information. This system allows the user to easily replenish their optimal nutrition. Furthermore, because the generating AI selects beverages based on the user's location information, users can easily purchase appropriate beverages from nearby vending machines. This enables effective nutritional replenishment after exercise.

[0006] "Biometric data" is a general term for data about the user's body collected during and after exercise, such as heart rate, calories burned, exercise time, and sweat volume.

[0007] "Device" refers to electronic devices such as smartwatches and exercise trackers used to collect users' biometric data.

[0008] "Terminal" refers to communication devices such as smartphones and tablets used to transmit collected biometric data to servers equipped with AI for generating data.

[0009] "Generative AI" refers to artificial intelligence technology that analyzes biometric data to identify nutrients that a user is lacking.

[0010] A "server" refers to a computer system where a generating AI operates and analyzes biometric data transmitted from a terminal.

[0011] "Nutrients" refer to the components necessary to maintain the body, such as vitamins, minerals, and water, which need to be replenished after exercise.

[0012] A "vending machine" is a machine for selling beverages, and in this invention, it refers to a place where you can purchase the beverage that has been determined to be optimal by the generating AI.

[0013] "Beverages" refer to drinks that users purchase from vending machines to replenish nutrients that their bodies are lacking.

[0014] "Notification" refers to a communication message sent to the user's device to inform them about the optimal beverage. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that collects biometric data, analyzes that data using generated AI, identifies deficient nutrients, and selects the most suitable beverage from nearby vending machines. This system is designed to allow users to efficiently replenish the necessary nutrients. The specific form is described below.

[0037] System Configuration

[0038] The system of the present invention consists of the following main components.

[0039] 1. Biometric data collection devices

[0040] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0041] 2. Data transmission terminal

[0042] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[0043] 3. Server for data analysis

[0044] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[0045] 4. Methods for selecting the optimal beverage

[0046] Server: Based on the analysis results, the generating AI uses user location information to access a database of nearby vending machines. This allows it to select the most suitable beverage from those offered by the vending machines.

[0047] 5. Means of notification

[0048] Server: Notifies the user's smartphone of the optimal beverage selection result.

[0049] Device: A smartphone notifies the user of the selected beverage information.

[0050] Program processing

[0051] The program for this system works as follows:

[0052] 1. User-driven data collection

[0053] User: Collects biometric data such as heart rate, calories burned, exercise time, and sweat volume using a smartwatch during and after exercise.

[0054] 2. Data transmission

[0055] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[0056] Terminal: A smartphone sends data to a server via the internet.

[0057] 3. Data analysis using generative AI

[0058] Server: The generating AI analyzes the received biometric data to identify nutrients that are deficient in the user's body. For example, the analysis might determine that there is a deficiency in water, B vitamins, sodium, etc.

[0059] 4. Selection of the optimal beverage

[0060] Server: The generating AI uses the user's location information to refer to a database of nearby vending machines and selects the most suitable beverage for nutritional replenishment from those offered.

[0061] 5. Notification to the user

[0062] Server: Sends selected beverage information to the user's smartphone.

[0063] Terminal: A smartphone notifies the user of beverage information, for example, displaying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[0064] Specific example

[0065] 1. User: Mr. Tanaka recorded his heart rate, calories burned, exercise time, and sweat volume using a smartwatch during his morning jog.

[0066] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[0067] 3. Server: The smartphone sent data to the server via the internet.

[0068] 4. Server: The generating AI analyzed Tanaka's data and determined that he was deficient in water and vitamin B.

[0069] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B.

[0070] 6. Server: The server sent information about "DrinkX" to Tanaka's smartphone.

[0071] 7. Device: The smartphone notified Ms. Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[0072] 8. User: Mr. Tanaka checked the notification and purchased "DrinkX" from a nearby vending machine.

[0073] In this way, users can effectively replenish nutrients that are lacking in their bodies, and it can also contribute to maintaining their physical condition after exercise.

[0074] The following describes the processing flow.

[0075] Step 1:

[0076] User: During and after exercise, use a smartwatch to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0077] Step 2:

[0078] Device: The smartwatch collects biometric data and transmits it to the user's smartphone via Bluetooth.

[0079] Step 3:

[0080] Device: The smartphone transmits biometric data to the server via the internet. If necessary, the user's location information is also transmitted at this time.

[0081] Step 4:

[0082] Server: The generating AI receives the transmitted biometric data and location information.

[0083] Step 5:

[0084] Server: The generating AI analyzes the received biometric data. This analysis identifies nutrients that are deficient in the user's body based on factors such as heart rate variability, calorie expenditure, exercise intensity, and sweat volume. For example, it may determine that the user is deficient in water, B vitamins, sodium, potassium, etc.

[0085] Step 6:

[0086] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[0087] Step 7:

[0088] Server: Compares the analysis results with the beverage information from the vending machine to select the optimal beverage for the user. For example, it might determine that "Sports Drink A" is optimal because it contains a lot of sodium and potassium.

[0089] Step 8:

[0090] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[0091] Step 9:

[0092] Terminal: The smartphone receives the selection information and notifies the user. For example, it displays a notification saying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[0093] Step 10:

[0094] User: Checks the notification and heads to a nearby vending machine.

[0095] Step 11:

[0096] User: Check the beverage displayed on the vending machine and purchase it.

[0097] (Example 1)

[0098] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In today's busy lifestyle, it is difficult for users to efficiently and effectively replenish their body's nutrients. Especially after exercise, users need to quickly replenish deficient nutrients, but there are insufficient means to quickly identify which nutrients are lacking and to provide appropriate nutritional support. Furthermore, there is no system that selects and notifies users of the most suitable nutritional supplements from nearby vending machines, leaving users to gather information and make choices themselves.

[0100] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0101] In this invention, the server includes means for generating AI to analyze collected data and identify nutrients that the user is lacking, means for selecting the optimal nutritional supplement item from multiple nutritional supplement items in nearby vending machines based on the identified nutrients, and means for notifying the user's communication terminal of the selected nutritional supplement item information. As a result, the user can have the optimal nutritional supplement item automatically selected after exercise and quickly replenish any deficient nutrients.

[0102] A "wearable device" is a computer device that a user can wear on their body and use to collect biometric data such as heart rate and calories burned.

[0103] A "communication terminal" is a device used to transmit biometric data to a computer equipped with AI, and primarily refers to mobile devices such as smartphones and tablets.

[0104] "Generative AI" is an artificial intelligence technology that analyzes collected biometric data to identify nutrients that the user is lacking.

[0105] "Nutrients" refer to components such as vitamins, minerals, and water that are needed in the user's body.

[0106] "Nutritional supplement items" are products such as beverages and foods offered in vending machines that are designed to replenish nutrients that are lacking in the user's body.

[0107] A "vending machine" is a machine that allows users to directly operate and purchase nutritional supplements such as beverages and food.

[0108] "Analysis" is the process of evaluating collected data, extracting information, and interpreting the data according to a specific purpose.

[0109] "Notification" refers to the act of informing the user about selected nutritional supplement items, and is primarily done using communication devices.

[0110] This invention is a system that collects a user's biometric data, analyzes that data to identify deficient nutrients, and selects the most suitable nutritional supplement item from a vending machine. The following describes a specific implementation of this system.

[0111] System components

[0112] 1. Wearable devices

[0113] Users wear wearable devices to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. These devices include heart rate monitors and accelerometers (e.g., smartwatches).

[0114] 2. Communication terminals

[0115] Wearable devices use communication methods such as Bluetooth to collect biometric data and transmit it to the user's smartphone. The smartphone then transmits the data via the internet to a server equipped with AI that generates data.

[0116] 3. Server equipped with generation AI

[0117] The server analyzes biometric data received via the internet. This server is equipped with a generating AI (e.g., GPT-4, a proprietary nutritional analysis model) that analyzes the user's data to identify any nutritional deficiencies.

[0118] 4. Nutrient Identification and Optimal Beverage Selection Methods

[0119] Based on the analysis results, the generating AI uses the user's location information to access a database of nearby vending machines. It then selects the most suitable nutritional supplement from the items offered in the vending machines.

[0120] 5. Means of notification

[0121] The server notifies the user's smartphone of the selected nutritional supplement items. The smartphone receives this information and displays it to the user.

[0122] Specific example

[0123] 1. User

[0124] User Tanaka uses a smartwatch to record his heart rate, calories burned, exercise time, and sweat volume during his morning jog.

[0125] Specific example: "Mr. Tanaka recorded his heart rate and calories burned using a smartwatch while jogging in the morning."

[0126] 2. Terminal

[0127] The wearable device collects data and sends it to Mr. Tanaka's smartphone. The smartphone then sends the data to a server via the internet.

[0128] Specific example: "After exercise, the smartwatch collected data and sent it to Mr. Tanaka's smartphone via Bluetooth. The smartphone then sent the data to a server via the internet."

[0129] 3. Server

[0130] The server uses AI to analyze Tanaka's data and determines that he is deficient in water and vitamin B. Then, it refers to data from nearby vending machines and selects "DrinkX," a sports drink rich in vitamin B.

[0131] Specific example: "The server analyzed Mr. Tanaka's biometric data and identified that he was deficient in water and vitamin B. It then consulted information from nearby vending machines and selected 'DrinkX,' a sports drink rich in vitamin B."

[0132] 4. Notification

[0133] The server sends information about "DrinkX" to Tanaka's smartphone, and the smartphone notifies Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[0134] Specific example: "The server sent information about the selected DrinkX to Mr. Tanaka's smartphone, and the smartphone notified Mr. Tanaka, 'We recommend DrinkX that can be purchased from a nearby vending machine.'"

[0135] Example of a prompt

[0136] "We will design a system that collects data such as heart rate and calorie consumption using a smartwatch, analyzes this data with a generating AI to identify deficient nutrients, and then selects the most suitable beverage from a vending machine."

[0137] In this way, users can efficiently and effectively replenish deficient nutrients, which can also contribute to maintaining their physical condition after exercise.

[0138] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0139] Step 1:

[0140] Users wear wearable devices during and after exercise to collect biometric data (heart rate, calories burned, exercise time, and sweat volume).

[0141] Input: User biometric information during and after exercise

[0142] Output: Collected biometric data (heart rate, calories burned, exercise time, sweat volume)

[0143] Specific operation: The user starts jogging while wearing the wearable device, and the sensors record data such as heart rate and calories burned.

[0144] Step 2:

[0145] The device (wearable device) transmits collected biometric data to the user's smartphone via Bluetooth.

[0146] Input: Biometric data collected by wearable devices

[0147] Output: Biometric data sent to smartphone

[0148] Specific operation: After exercise, the wearable device automatically syncs with the smartphone and transfers data such as heart rate and calories burned.

[0149] Step 3:

[0150] The device (smartphone) transmits biometric data to the server via the internet.

[0151] Input: Biometric data stored on a smartphone

[0152] Output: Biometric data sent to the server

[0153] Specific operation: A dedicated smartphone application uploads biometric data to a server using Wi-Fi or mobile data communication.

[0154] Step 4:

[0155] The server uses generated AI to analyze biometric data and identify nutrients that are deficient in the user's body.

[0156] Input: Biometric data sent to the server

[0157] Output: Deficit nutrients identified through analysis

[0158] Specific operation: The generating AI cleans the data and analyzes data such as heart rate, calories burned, exercise time, and sweat volume to determine if there is a deficiency in water or vitamin B.

[0159] Step 5:

[0160] The server uses the user's location information to access a database of nearby vending machines and select the most suitable nutritional supplement item.

[0161] Input: Identified nutrient deficiencies and user location information

[0162] Output: Information on selected nutritional supplement items

[0163] Specific operation: The generating AI lists nearby vending machines based on the user's current location and selects an appropriate nutritional supplement item (e.g., the sports drink "DrinkX") from among them.

[0164] Step 6:

[0165] The server sends information about the selected nutritional supplement items to the user's smartphone.

[0166] Input: Information on selected nutritional supplement items

[0167] Output: Information about nutritional supplements sent to the smartphone

[0168] Specific operation: The server sends the selection information to the user's smartphone and displays it to the user via push notification.

[0169] Step 7:

[0170] The device (smartphone) notifies the user of information about the selected nutritional supplement item.

[0171] Input: Information about nutritional supplement items sent from the server.

[0172] Output: Information on nutritional supplement items notified to the user.

[0173] Specific action: The smartphone uses its push notification function to display a message to the user such as, "We recommend DrinkX, which is available for purchase at a nearby vending machine."

[0174] Step 8:

[0175] The user checks the notification and purchases the recommended nutritional supplement from a nearby vending machine.

[0176] Input: Notification information from smartphone

[0177] Output: Purchased nutritional supplements

[0178] Specific action: The user checks the notification, goes to a nearby vending machine, and purchases the selected nutritional supplement item (e.g., "DrinkX").

[0179] (Application Example 1)

[0180] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0181] Traditional nutritional supplementation methods required users to predict their own nutrient deficiencies and select appropriate foods and beverages, making effective and immediate nutritional supplementation difficult. As a result, users often spent considerable time and effort on proper nutrition. This was particularly problematic when rapid nutritional replenishment was needed, such as after exercise. Furthermore, there was a lack of systems that could suggest optimal foods based on biometric data, highlighting the need for highly accurate recommendations.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0183] In this invention, the server includes a device for collecting biometric data, a terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients that the user is lacking, means for selecting the optimal meal from multiple meals from a partnered food delivery service based on the identified nutrients, and means for notifying the user's terminal of the selected meal information. This enables the user to quickly and efficiently replenish nutrients that are lacking after exercise or in daily life.

[0184] "During and after exercise" refers to the period during which a user is performing physical exercise and the subsequent recovery period.

[0185] "User biometric data" refers to information that indicates the physiological state of the human body, such as heart rate, calories burned, exercise time, and sweat volume.

[0186] A "device" is an instrument used to collect a user's biometric data, and includes smartwatches and exercise trackers.

[0187] A "terminal" is a device used to transmit collected data to a server, and includes the user's smartphone, for example.

[0188] "Generative AI" refers to artificial intelligence technology used to analyze a user's biometric data and identify deficient nutrients.

[0189] "Nutrient deficiencies" refer to nutrients such as vitamins, minerals, and water that are lacking in the user's body.

[0190] A "food delivery service" refers to a company or system that provides a service of delivering specified meals to a user's designated location.

[0191] "Meals" refers to dishes and food items containing nutrients necessary for the user's body, provided by partnered food delivery services.

[0192] "Selecting" refers to the process of determining the optimal foods and beverages based on the user's biometric data and nutritional deficiencies.

[0193] "Notify" refers to the action of informing the user about the selected food or beverages.

[0194] In this invention, the user first wears a device to collect biometric data during and after exercise. Specifically, devices such as smartwatches or exercise trackers are used. This records biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0195] The collected biometric data is transmitted to the user's smartphone using communication methods such as Bluetooth. The smartphone then transfers this data to a server via the internet. The server is equipped with a generative AI model that analyzes the received biometric data to identify nutrients that the user is lacking.

[0196] The generative AI model utilizes cutting-edge technologies such as OpenAI GPT-4 and includes advanced algorithms for analyzing biometric data. This model identifies specific nutrients that the user may be lacking, such as water, vitamins, and minerals.

[0197] Based on the nutritional information identified by the generating AI, the server consults the database of partner food delivery services to select the most suitable meal for the user. The user's location is also considered during the selection process to ensure the meal provides the quickest and most effective nutritional support.

[0198] The selected meal information is sent from the server to the smartphone. The smartphone uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the selected meal information. The notification includes "recommended meal sets and nutritionally balanced menus," which the user can then review.

[0199] Once a user checks the notification and selects a meal, it is delivered by a partnered food delivery service. This allows users to efficiently replenish any nutritional deficiencies they may have.

[0200] As a concrete example, a user records biometric data with a smartwatch while jogging in the morning, and this data is sent to a server via a smartphone. If the AI ​​analyzes the data and determines that the user is deficient in water and vitamin C, a food delivery service will suggest a meal rich in vitamin C (e.g., a spinach and orange salad) based on that information. Once the user selects the meal, it is delivered quickly.

[0201] The following prompts are used as input to the generative AI model:

[0202] "We have received the user's biometric data. Based on heart rate, calories burned, exercise time, and sweat volume, please analyze the nutrients that are lacking and suggest appropriate menu items from a food delivery service based on the user's current location."

[0203] This allows users to achieve a healthier lifestyle.

[0204] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0205] Step 1:

[0206] The user wears a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. The input is the user's biometric data, and the output is the recording of this data on the smartwatch.

[0207] Step 2:

[0208] A smartwatch transmits biometric data collected via Bluetooth to the user's smartphone. The input is the biometric data stored in the smartwatch, and the output is the data transferred to the smartphone. Specifically, data compression and transfer are performed using the Bluetooth communication function.

[0209] Step 3:

[0210] A smartphone transmits biometric data to a server via the internet. The input is the biometric data received by the smartphone, which is then transmitted to the server via the internet. The output is the biometric data stored on the server. Specifically, the data transfer is performed using the HTTPS protocol.

[0211] Step 4:

[0212] A generative AI model installed on the server analyzes the received biometric data to identify deficient nutrients. The input is biometric data stored on the server, and the generative AI processes the data to identify deficient nutrients such as water, vitamin C, and iron based on factors like heart rate and calorie expenditure. The output is a list of deficient nutrients.

[0213] Step 5:

[0214] The server selects the optimal meal by referencing the database of partner food delivery services based on information about missing nutrients. The input is a list of missing nutrients, and the system selects the best meal menu through a database search. The output is a list of nutritionally balanced meals. Specifically, it uses SQL queries to retrieve the necessary information from the database.

[0215] Step 6:

[0216] The server sends the selected meal information to the user's smartphone. The input is the selected meal information, which is sent to the user's smartphone using a push notification system (e.g., Firebase Cloud Messaging). The output is the meal information displayed on the user's smartphone.

[0217] Step 7:

[0218] The user checks a notification displayed on their smartphone and selects a suggested meal. The input is the meal information displayed on the smartphone, and the output is the information of the meal selected by the user. Specifically, confirmation and selection are performed using the smartphone's app interface.

[0219] Step 8:

[0220] A food delivery service picks up the selected meal and delivers it to the user's specified location. The input is information about the meal selected by the user, and the output is the meal delivered to the user. Specifically, order confirmation, cooking, and delivery are all handled using a delivery application.

[0221] These steps allow users to efficiently replenish deficient nutrients and achieve a healthy lifestyle.

[0222] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0223] This invention combines an emotion engine with a system that selects the optimal beverage from nearby vending machines by collecting biometric data, analyzing that data using generated AI, and identifying deficient nutrients. The emotion engine recognizes the user's emotions and incorporates that data into the identification of nutrients, enabling more personalized nutritional supplementation. The specific form is described below.

[0224] System Configuration

[0225] The system of the present invention consists of the following main components.

[0226] 1. Biometric data collection devices

[0227] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0228] 2. Data transmission terminal

[0229] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[0230] 3. Server for data analysis

[0231] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[0232] 4. Emotional Engine

[0233] The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine collects user emotion data using facial recognition and speech recognition technologies.

[0234] 5. Methods for selecting the optimal beverage

[0235] Server: The generating AI uses user location information and sentiment data to refer to a database of nearby vending machines and select the most suitable beverage from those offered.

[0236] 6. Means of notification

[0237] Server: Notifies the user's smartphone of the optimal beverage selection result.

[0238] Device: A smartphone notifies the user of the selected beverage information.

[0239] Program processing

[0240] The program for this system works as follows:

[0241] 1. User-driven data collection

[0242] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume, as well as emotional data using facial recognition and voice recognition technology.

[0243] 2. Data transmission

[0244] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[0245] Device: A smartphone transmits biometric and emotional data to a server via the internet.

[0246] 3. Data analysis using generative AI

[0247] Server: The generating AI analyzes the received biometric and emotional data to identify nutrients that are deficient in the user's body. This analysis determines deficiencies in water, B vitamins, sodium, etc., based on heart rate variability, calorie expenditure, exercise intensity, sweat volume, and the user's emotional state (e.g., under stress or in a relaxed state).

[0248] 4. Selection of the optimal beverage

[0249] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[0250] Server: The server compares the analysis results with the beverage information from the vending machine to select the most suitable beverage for the user. For example, if the emotion engine recognizes the user's stress level, a beverage containing ingredients with relaxing effects will be selected.

[0251] 5. Notification to the user

[0252] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[0253] Device: A smartphone notifies the user of beverage information, for example, displaying, "We recommend beverage A, which has a relaxing effect and can be purchased from a nearby vending machine."

[0254] Specific example

[0255] 1. User: Ms. Tanaka recorded her heart rate, calories burned, exercise time, and sweat volume using a smartwatch during her morning jog. She also collected her emotional data using facial recognition technology.

[0256] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[0257] 3. Device: The smartphone sent data to the server via the internet.

[0258] 4. Server: The generating AI analyzed Tanaka's data and determined that she was deficient in water and vitamin B. Additionally, the emotion engine recognized that Tanaka was experiencing stress.

[0259] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[0260] 6. Server: The server sent information about "DrinkX" and "DrinkY" to Tanaka's smartphone.

[0261] 7. Device: The smartphone notified Ms. Tanaka, "We recommend DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect."

[0262] 8. User: Tanaka checked the notification and purchased "DrinkX" and "DrinkY" from a nearby vending machine.

[0263] In this way, users can effectively replenish nutrients that their bodies lack and maintain both physical and mental health by selecting the appropriate beverage according to their emotional state.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume. It also analyzes facial expressions using facial recognition technology and collects emotional data from voice using voice recognition technology.

[0267] Step 2:

[0268] Device: The smartwatch collects biometric and emotional data via Bluetooth and transmits it to the user's smartphone.

[0269] Step 3:

[0270] Device: A smartphone transmits biometric and emotional data to a server via the internet. Location information is also transmitted simultaneously.

[0271] Step 4:

[0272] Server: The generating AI receives the transmitted biometric data, emotional data, and location information.

[0273] Step 5:

[0274] Server: Analyze the biological data received by the generative AI. In this analysis, identify the nutrients (such as water, B vitamins, sodium, potassium, etc.) lacking in the user's body from factors such as heart rate fluctuations, calories consumed, exercise intensity, and sweat volume.

[0275] Step 6:

[0276] Server: Analyze the emotional data received by the generative AI. For example, in face recognition technology, determine the stress level and relaxation degree from the user's expression, and in voice recognition technology, determine the emotional state from the tone and speed of the voice.

[0277] Step 7:

[0278] Server: Comprehensively judge the nutrients lacking in the user's body and the emotional state by the generative AI, and identify the optimal nutrients and their formulations.

[0279] Step 8:

[0280] Server: Based on the user's location information, the generative AI refers to the database of nearby vending machines. The database contains the nutrient information of each beverage and the effects according to the emotional state. [[ID=..]]

[0281] Step 9:

[0282] Server: Compare the analysis results with the beverage information of the vending machine and select the optimal beverage for the user. For example, when the user's emotional state is stress, a beverage with a relaxation effect is selected.

[0283] Step 10:

[0284] Server: Transmit the selected beverage information to the user's smartphone. This information includes the name of the recommended beverage, its effect, and the location of the purchasable vending machine.

[0285] Step 11:

[0286] Terminal: The smartphone receives the selection information and notifies the user. For example, a notification such as "We recommend Beverage A with a relaxing effect that can be purchased at a nearby vending machine" is displayed.

[0287] Step 12:

[0288] User: Confirms the notification and heads to a nearby vending machine.

[0289] Step 13:

[0290] User: Checks the beverage notified at the vending machine and purchases it.

[0291] (Example 2)

[0292] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0293] Conventional nutrition supplementation systems have only identified nutrients based on biological data and selected the optimal beverage, so they cannot take into account the user's emotional state and mental health. Therefore, there is a need for a system that realizes comprehensive health management by providing personalized nutrition supplementation.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0295] In this invention, the server includes a device for collecting biological data, means for collecting emotional data, means for transmitting the collected data to a terminal equipped with a generation AI, means for the generation AI to analyze the collected data and identify the nutrients and emotional state lacking in the user, means for selecting the optimal beverage from a plurality of beverages in a nearby vending machine based on the identified nutrients and emotional data, and means for notifying the selected beverage information to the user's terminal. Thereby, personalized nutrition supplementation according to the user's emotional state becomes possible.

[0296] "During and after exercise" refers to the time period while a user is performing physical activity and immediately after that activity has finished.

[0297] "Biometric data" refers to information about the user's physical condition, such as heart rate, calories burned, exercise time, and sweat volume.

[0298] A "device" refers to equipment used to collect a user's biometric data, such as smartwatches and exercise trackers.

[0299] A "terminal" refers to a communication device used to transmit biometric and emotional data to a server, such as a smartphone or tablet.

[0300] "Generative AI" refers to artificial intelligence algorithms that analyze collected data to identify a user's nutritional and emotional state.

[0301] "Emotional data" refers to information about a user's emotional state, obtained using facial recognition or speech recognition technologies.

[0302] "Nearby vending machines" refers to beverage vending machines located near the user, based on the user's location information.

[0303] An "optimal beverage" refers to a beverage that provides the most suitable nutritional support for the user, based on identified nutrient and emotional data.

[0304] "Notifying" refers to the act of displaying or transmitting selected beverage information to the user's device.

[0305] This invention is a system that collects biometric and emotional data and analyzes the user's nutritional status using generative AI. Furthermore, it also has a function to select the most suitable beverage from nearby vending machines and notify the user. A specific embodiment of this system is described below.

[0306] Hardware Configuration

[0307] This system consists of the following main components:

[0308] 1. Biometric Data Collection Device

[0309] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0310] 2. Data Transmission Terminal

[0311] Terminal: The smartwatch transmits the biometric data it has collected to the user's smartphone using communication means such as Bluetooth.

[0312] 3. Data Analysis Server

[0313] Server: Receives the biometric data transmitted from the smartphone via the Internet. This server is equipped with a generative AI and has the function of analyzing the received data to identify the nutrients lacking in the user.

[0314] 4. Emotion Engine

[0315] Terminal: It has an emotion engine for recognizing the user's emotions. This emotion engine uses face recognition technology and voice recognition technology to collect the user's emotion data.

[0316] 5. Optimal Beverage Selection Means

[0317] Server: Based on the user location information and emotion data, the generative AI refers to the database of nearby vending machines and selects the optimal beverage from the provided beverages.

[0318] 6. Notification Means

[0319] Server: Notifies the user's smartphone of the optimal beverage selection result.

[0320] Device: A smartphone notifies the user of the selected beverage information.

[0321] Software Configuration

[0322] 1. The biometric data collection device has an application installed that records heart rate, calories burned, exercise time, and sweat volume in real time.

[0323] 2. The data transmission terminal includes a communication application for sending biometric and emotional data to the server.

[0324] 3. The data analysis server is equipped with a generative AI model that analyzes the collected data, utilizing machine learning frameworks such as Python and TensorFlow.

[0325] 4. The emotion engine is built using face recognition libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[0326] Specific example

[0327] 1. User: Use a smartwatch during exercise to record heart rate, calories burned, exercise time, and sweat volume. After exercise, use your smartphone's camera to scan your face or add emotional data via voice input.

[0328] 2. Terminal: The smartwatch sends the data it collects via Bluetooth to the smartphone, and the smartphone sends the data to the server via the internet.

[0329] 3. Server: Analyzes the received data and identifies that the user is deficient in vitamin B. The emotion engine also determines that the user's emotional state indicates a high stress level.

[0330] 4. Server: Referencing a database of nearby vending machines, the generating AI selects "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[0331] 5. Server: Sends the selected beverage information to the user's smartphone.

[0332] 6. Device: The smartphone notifies the user that "DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect, are recommended."

[0333] This system allows users to effectively replenish nutrients that their bodies lack and select appropriate beverages according to their emotional state, thereby maintaining both physical and mental health.

[0334] Example of a prompt

[0335] "I'm looking for a relaxing drink. Could you tell me what I can buy from a nearby vending machine?"

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] User: Wear a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0339] Input: User's physical condition during and after exercise.

[0340] Output: Biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0341] Specific operation: The smartwatch's sensors record this data in real time.

[0342] Step 2:

[0343] User: Uses facial recognition and speech recognition technologies to collect their own emotional data.

[0344] Input: User's facial expressions and voice.

[0345] Output: Emotional data (e.g., stress, relaxation, joy, etc.).

[0346] Specific operation: Uses the smartphone's camera and microphone to analyze facial expressions and voice and generate emotion data.

[0347] Step 3:

[0348] Device: The smartwatch collects biometric data via Bluetooth and transmits it to the user's smartphone.

[0349] Input: Biometric data stored on a smartwatch.

[0350] Output: Biometric data transferred to a smartphone.

[0351] Specific operation: Data is transferred from the smartwatch to the smartphone using a Bluetooth communication module.

[0352] Step 4:

[0353] Device: A smartphone transmits collected biometric and emotional data to a server via the internet.

[0354] Input: Biometric data and emotional data stored on a smartphone.

[0355] Output: Biometric and emotional data transferred to the server.

[0356] Specific operation: Data is sent to the server using an API. The data is typically sent in JSON format.

[0357] Step 5:

[0358] Server: Analyzes received biometric and emotional data to identify nutrients lacking in the user's body and their emotional state.

[0359] Input: Biometric and emotional data stored on the server.

[0360] Output: Identification of the user's nutritional deficiencies and emotional state.

[0361] Specific operation: The generating AI analyzes fluctuating data such as heart rate and calorie expenditure to identify deficiencies in vitamins B, sodium, water, etc. Simultaneously, the emotion engine analyzes emotional data to determine stress levels and relaxation levels.

[0362] Step 6:

[0363] Server: Based on the user's location information, it refers to a database of nearby vending machines and selects the most suitable beverage.

[0364] Input: Location information, biometric data analysis results, emotional data analysis results.

[0365] Output: Selection results for the optimal beverage.

[0366] Specific operation: The generating AI uses location information to search a database and identify beverages that are rich in vitamin B or have a relaxing effect.

[0367] Step 7:

[0368] Server: Notifies the user's smartphone of the selected optimal beverage information.

[0369] Input: Selection results for the optimal beverage.

[0370] Output: Beverage information sent to the smartphone.

[0371] Specific operation: The server sends the selection results to the smartphone in JSON format.

[0372] Step 8:

[0373] Device: A smartphone notifies the user of the selected beverage information.

[0374] Input: Beverage information sent from the server.

[0375] Output: Beverage information displayed to the user.

[0376] Specific action: Using the smartphone's notification function, display a message saying, "We recommend DrinkX, available from a nearby vending machine, and DrinkY, which has a relaxing effect."

[0377] This series of processing steps allows users to effectively replenish nutrients that their bodies are lacking and select the appropriate beverage according to their emotional state.

[0378] (Application Example 2)

[0379] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0380] Conventional technology lacked the means to identify nutrient deficiencies based on a driver's biometric data and select appropriate beverages. Furthermore, there was no technology to optimize nutritional supplementation in response to a driver's emotional state, making efficient health management difficult. This resulted in fatigue and decreased concentration during long drives, and could not be expected to improve safety.

[0381] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0382] In this invention, the server includes a device for collecting the driver's biometric data, a communication terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients the driver is lacking, an emotion recognition engine for collecting the driver's emotional data and reflecting it in the identification of nutrients, means for selecting the optimal beverage from multiple beverages in nearby vending machines based on the identified nutrients and emotional data, and means for notifying the driver's terminal of the selected beverage information. This makes it possible to monitor the driver's health status in real time and provide beverages that correspond to the necessary nutrients and emotions at the optimal time.

[0383] "Driver biometric data" refers to physical information such as the driver's heart rate, calories burned, driving time, and sweat volume. This data is used to understand the driver's health status.

[0384] "Generative AI" refers to artificial intelligence technology that analyzes collected biometric and emotional data and outputs optimal results tailored to a specific purpose.

[0385] An "emotion recognition engine" refers to technology that analyzes a driver's facial expressions and voice to identify their emotional state.

[0386] A "communication terminal" refers to a device, such as a smartwatch or a vehicle's central control unit, that transmits biometric data and emotional data to a server.

[0387] "Beverage selection method" refers to a method for selecting the most suitable beverage for the driver by referring to beverage information from nearby vending machines, based on identified nutritional and emotional data.

[0388] "Notification means" refers to a means of transmitting selected beverage information to the driver's terminal and informing the driver of that information.

[0389] A "vending machine" refers to an automatic vending machine for beverages, food, etc., that is installed in a location where drivers can easily use it.

[0390] This invention relates to a system for installation in autonomous vehicles that monitors the driver's health and emotional state in real time and suggests beverages to replenish necessary nutrients at the appropriate time. This system includes the following main components:

[0391] System Configuration

[0392] 1. Driver data acquisition device

[0393] The driver wears a smartwatch or other device to collect biometric data such as heart rate, calories burned, driving time, and sweat volume. Additionally, a facial recognition camera and microphone are installed inside the vehicle to collect the driver's emotional data. This allows for simultaneous monitoring of the driver's physical information and emotional state.

[0394] 2. Data transmission means

[0395] The smartwatch transmits data collected via Bluetooth to the vehicle's central control unit (CCU). The CCU then transmits the data to an analysis server via the internet. As a result, biometric and emotional data are aggregated on the server.

[0396] 3. Data analysis using generative AI

[0397] The server is equipped with a generative AI model. This generative AI model analyzes collected biometric and emotional data to identify nutrients that the driver is lacking. For example, it determines deficiencies in water, vitamins, sodium, etc., from heart rate variability, calorie expenditure, driving intensity, sweat volume, and the driver's emotional state.

[0398] 4. Emotion Recognition Engine

[0399] The server uses an emotion recognition engine to analyze the driver's emotional data and incorporate it into identifying nutrients. For example, it can detect stress and fatigue during driving and suggest beverages with relaxing effects.

[0400] 5. Methods for selecting the optimal beverage

[0401] The AI ​​generates recommendations and selects the most suitable beverage for the driver based on the analysis results. In doing so, it references a database of nearby vending machines based on the driver's location information and compares the nutritional information of the beverages offered. This allows the AI ​​to select the optimal beverage for the driver.

[0402] 6. Means of notification

[0403] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the driver. The autonomous vehicle's infotainment system (IVI) displays to the driver, "We recommend beverage A, which has a relaxing effect and can be purchased at a nearby service area."

[0404] Specific example

[0405] 1. User: A typical driver will use a smartwatch and in-car camera to collect heart rate, calories burned, driving time, sweat volume, and emotional data while driving long distances.

[0406] 2. Terminal: The smartwatch transmits data to the CCU via Bluetooth, and the CCU transmits the data to the server via the internet.

[0407] 3. Server: The generation AI model analyzes the data and identifies deficient nutrients (e.g., water and vitamin B). Additionally, the emotion recognition engine recognizes the driver's stress level.

[0408] 4. Server: The generating AI references data from vending machines near the driver and selects beverages that are rich in vitamin B and have stress-reducing effects.

[0409] 5. Server: The server transmits the selected beverage information to the CCU, which then displays the "recommended beverage" via the IVI system.

[0410] Example of a prompt

[0411] "Please analyze the following biometric and emotional data to identify any nutrient deficiencies: Heart rate: [data], Calories burned: [data], Sweat volume: [data]. Facial recognition result: Stress level."

[0412] This allows drivers to efficiently replenish nutrients and select appropriate beverages according to their emotional state, thereby improving safety and comfort during long-distance driving.

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] The user wears a smartwatch that collects biometric data (heart rate, calories burned, driving time, sweat volume) during and after driving. A facial recognition camera and microphone collect the driver's emotional data (e.g., stress level, relaxation level).

[0416] Inputs: Driver's heart rate, calories burned, driving time, sweat volume, and emotion data from facial recognition camera and microphone.

[0417] Output: Collected biometric and emotional data

[0418] Step 2:

[0419] The device (smartwatch) transmits the collected data to the vehicle's central control unit (CCU) via Bluetooth.

[0420] Input: Biometric and emotional data from smartwatches

[0421] Output: Biometric and emotional data transmitted to the CCU

[0422] Step 3:

[0423] The CCU sends the data collected via the internet to an analysis server.

[0424] Input: Biometric and emotional data transmitted from the CCU.

[0425] Output: Biometric and emotional data sent to the analysis server.

[0426] Step 4:

[0427] The server (generating AI model) analyzes the received biometric and emotional data to identify nutrients the driver is lacking. This analysis is based on heart rate variability, calorie expenditure, driving intensity, sweat volume, and emotional state (e.g., stressful environment, relaxed state).

[0428] Input: Biometric data and emotional data

[0429] Output: Identified nutrient deficiencies (e.g., water, vitamin B, sodium)

[0430] Step 5:

[0431] The server (emotion recognition engine) analyzes the driver's emotional data and uses it to identify nutrients. For example, if stress is detected while driving, nutrients with relaxing effects will be prioritized.

[0432] Input: Sentiment data

[0433] Output: Corrected results for identified nutrients

[0434] Step 6:

[0435] Based on identified nutritional and emotional data, the server references the driver's location and selects the most suitable beverage from a database of nearby vending machines.

[0436] Input: Identified nutrients, emotional data, location information

[0437] Output: Information on the best beverage selected from nearby vending machines.

[0438] Step 7:

[0439] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the user of the beverage information through the infotainment system (IVI).

[0440] Input: Selected beverage information

[0441] Output: Beverage information displayed on the driver's IVI system (e.g., "We recommend beverage A, a relaxing drink available at the nearby service area.")

[0442] This will allow drivers to efficiently replenish necessary nutrients and select appropriate beverages according to their emotional state.

[0443] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0444] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0445] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0446] [Second Embodiment]

[0447] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0448] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0449] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0450] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0451] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0452] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0453] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0454] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0455] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0456] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0457] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0458] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0459] This invention is a system that collects biometric data, analyzes that data using generated AI, identifies deficient nutrients, and selects the most suitable beverage from nearby vending machines. This system is designed to allow users to efficiently replenish the necessary nutrients. The specific form is described below.

[0460] System Configuration

[0461] The system of the present invention consists of the following main components.

[0462] 1. Biometric data collection devices

[0463] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0464] 2. Data transmission terminal

[0465] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[0466] 3. Server for data analysis

[0467] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[0468] 4. Methods for selecting the optimal beverage

[0469] Server: Based on the analysis results, the generating AI uses user location information to access a database of nearby vending machines. This allows it to select the most suitable beverage from those offered by the vending machines.

[0470] 5. Means of notification

[0471] Server: Notifies the user's smartphone of the optimal beverage selection result.

[0472] Device: A smartphone notifies the user of the selected beverage information.

[0473] Program processing

[0474] The program for this system works as follows:

[0475] 1. User-driven data collection

[0476] User: Collects biometric data such as heart rate, calories burned, exercise time, and sweat volume using a smartwatch during and after exercise.

[0477] 2. Data transmission

[0478] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[0479] Terminal: A smartphone sends data to a server via the internet.

[0480] 3. Data analysis using generative AI

[0481] Server: The generating AI analyzes the received biometric data to identify nutrients that are deficient in the user's body. For example, the analysis might determine that there is a deficiency in water, B vitamins, sodium, etc.

[0482] 4. Selection of the optimal beverage

[0483] Server: The generating AI uses the user's location information to refer to a database of nearby vending machines and selects the most suitable beverage for nutritional replenishment from those offered.

[0484] 5. Notification to the user

[0485] Server: Sends selected beverage information to the user's smartphone.

[0486] Terminal: A smartphone notifies the user of beverage information, for example, displaying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[0487] Specific example

[0488] 1. User: Mr. Tanaka recorded his heart rate, calories burned, exercise time, and sweat volume using a smartwatch during his morning jog.

[0489] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[0490] 3. Server: The smartphone sent data to the server via the internet.

[0491] 4. Server: The generating AI analyzed Tanaka's data and determined that he was deficient in water and vitamin B.

[0492] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B.

[0493] 6. Server: The server sent information about "DrinkX" to Tanaka's smartphone.

[0494] 7. Device: The smartphone notified Ms. Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[0495] 8. User: Mr. Tanaka checked the notification and purchased "DrinkX" from a nearby vending machine.

[0496] In this way, users can effectively replenish nutrients that are lacking in their bodies, and it can also contribute to maintaining their physical condition after exercise.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] User: During and after exercise, use a smartwatch to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0500] Step 2:

[0501] Device: The smartwatch collects biometric data and transmits it to the user's smartphone via Bluetooth.

[0502] Step 3:

[0503] Device: The smartphone transmits biometric data to the server via the internet. If necessary, the user's location information is also transmitted at this time.

[0504] Step 4:

[0505] Server: The generating AI receives the transmitted biometric data and location information.

[0506] Step 5:

[0507] Server: The generating AI analyzes the received biometric data. This analysis identifies nutrients that are deficient in the user's body based on factors such as heart rate variability, calorie expenditure, exercise intensity, and sweat volume. For example, it may determine that the user is deficient in water, B vitamins, sodium, potassium, etc.

[0508] Step 6:

[0509] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[0510] Step 7:

[0511] Server: Compares the analysis results with the beverage information from the vending machine to select the optimal beverage for the user. For example, it might determine that "Sports Drink A" is optimal because it contains a lot of sodium and potassium.

[0512] Step 8:

[0513] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[0514] Step 9:

[0515] Terminal: The smartphone receives the selection information and notifies the user. For example, it displays a notification saying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[0516] Step 10:

[0517] User: Checks the notification and heads to a nearby vending machine.

[0518] Step 11:

[0519] User: Check the beverage displayed on the vending machine and purchase it.

[0520] (Example 1)

[0521] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0522] In today's busy lifestyle, it is difficult for users to efficiently and effectively replenish their body's nutrients. Especially after exercise, users need to quickly replenish deficient nutrients, but there are insufficient means to quickly identify which nutrients are lacking and to provide appropriate nutritional support. Furthermore, there is no system that selects and notifies users of the most suitable nutritional supplements from nearby vending machines, leaving users to gather information and make choices themselves.

[0523] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0524] In this invention, the server includes means for generating AI to analyze collected data and identify nutrients that the user is lacking, means for selecting the optimal nutritional supplement item from multiple nutritional supplement items in nearby vending machines based on the identified nutrients, and means for notifying the user's communication terminal of the selected nutritional supplement item information. As a result, the user can have the optimal nutritional supplement item automatically selected after exercise and quickly replenish any deficient nutrients.

[0525] A "wearable device" is a computer device that a user can wear on their body and use to collect biometric data such as heart rate and calories burned.

[0526] A "communication terminal" is a device used to transmit biometric data to a computer equipped with AI, and primarily refers to mobile devices such as smartphones and tablets.

[0527] "Generative AI" is an artificial intelligence technology that analyzes collected biometric data to identify nutrients that the user is lacking.

[0528] "Nutrients" refer to components such as vitamins, minerals, and water that are needed in the user's body.

[0529] "Nutritional supplement items" are products such as beverages and foods offered in vending machines that are designed to replenish nutrients that are lacking in the user's body.

[0530] A "vending machine" is a machine that allows users to directly operate and purchase nutritional supplements such as beverages and food.

[0531] "Analysis" is the process of evaluating collected data, extracting information, and interpreting the data according to a specific purpose.

[0532] "Notification" refers to the act of informing the user about selected nutritional supplement items, and is primarily done using communication devices.

[0533] This invention is a system that collects a user's biometric data, analyzes that data to identify deficient nutrients, and selects the most suitable nutritional supplement item from a vending machine. The following describes a specific implementation of this system.

[0534] System components

[0535] 1. Wearable devices

[0536] Users wear wearable devices to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. These devices include heart rate monitors and accelerometers (e.g., smartwatches).

[0537] 2. Communication terminals

[0538] Wearable devices use communication methods such as Bluetooth to collect biometric data and transmit it to the user's smartphone. The smartphone then transmits the data via the internet to a server equipped with AI that generates data.

[0539] 3. Server equipped with generation AI

[0540] The server analyzes biometric data received via the internet. This server is equipped with a generating AI (e.g., GPT-4, a proprietary nutritional analysis model) that analyzes the user's data to identify any nutritional deficiencies.

[0541] 4. Nutrient Identification and Optimal Beverage Selection Methods

[0542] Based on the analysis results, the generating AI uses the user's location information to access a database of nearby vending machines. It then selects the most suitable nutritional supplement from the items offered in the vending machines.

[0543] 5. Means of notification

[0544] The server notifies the user's smartphone of the selected nutritional supplement items. The smartphone receives this information and displays it to the user.

[0545] Specific example

[0546] 1. User

[0547] User Tanaka uses a smartwatch to record his heart rate, calories burned, exercise time, and sweat volume during his morning jog.

[0548] Specific example: "Mr. Tanaka recorded his heart rate and calories burned using a smartwatch while jogging in the morning."

[0549] 2. Terminal

[0550] The wearable device collects data and sends it to Mr. Tanaka's smartphone. The smartphone then sends the data to a server via the internet.

[0551] Specific example: "After exercise, the smartwatch collected data and sent it to Mr. Tanaka's smartphone via Bluetooth. The smartphone then sent the data to a server via the internet."

[0552] 3. Server

[0553] The server uses AI to analyze Tanaka's data and determines that he is deficient in water and vitamin B. Then, it refers to data from nearby vending machines and selects "DrinkX," a sports drink rich in vitamin B.

[0554] Specific example: "The server analyzed Mr. Tanaka's biometric data and identified that he was deficient in water and vitamin B. It then consulted information from nearby vending machines and selected 'DrinkX,' a sports drink rich in vitamin B."

[0555] 4. Notification

[0556] The server sends information about "DrinkX" to Tanaka's smartphone, and the smartphone notifies Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[0557] Specific example: "The server sent information about the selected DrinkX to Mr. Tanaka's smartphone, and the smartphone notified Mr. Tanaka, 'We recommend DrinkX that can be purchased from a nearby vending machine.'"

[0558] Example of a prompt

[0559] "We will design a system that collects data such as heart rate and calorie consumption using a smartwatch, analyzes this data with a generating AI to identify deficient nutrients, and then selects the most suitable beverage from a vending machine."

[0560] In this way, users can efficiently and effectively replenish deficient nutrients, which can also contribute to maintaining their physical condition after exercise.

[0561] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0562] Step 1:

[0563] Users wear wearable devices during and after exercise to collect biometric data (heart rate, calories burned, exercise time, and sweat volume).

[0564] Input: User biometric information during and after exercise

[0565] Output: Collected biometric data (heart rate, calories burned, exercise time, sweat volume)

[0566] Specific operation: The user starts jogging while wearing the wearable device, and the sensors record data such as heart rate and calories burned.

[0567] Step 2:

[0568] The device (wearable device) transmits collected biometric data to the user's smartphone via Bluetooth.

[0569] Input: Biometric data collected by wearable devices

[0570] Output: Biometric data sent to smartphone

[0571] Specific operation: After exercise, the wearable device automatically syncs with the smartphone and transfers data such as heart rate and calories burned.

[0572] Step 3:

[0573] The device (smartphone) transmits biometric data to the server via the internet.

[0574] Input: Biometric data stored on a smartphone

[0575] Output: Biometric data sent to the server

[0576] Specific operation: A dedicated smartphone application uploads biometric data to a server using Wi-Fi or mobile data communication.

[0577] Step 4:

[0578] The server uses generated AI to analyze biometric data and identify nutrients that are deficient in the user's body.

[0579] Input: Biometric data sent to the server

[0580] Output: Deficit nutrients identified through analysis

[0581] Specific operation: The generating AI cleans the data and analyzes data such as heart rate, calories burned, exercise time, and sweat volume to determine if there is a deficiency in water or vitamin B.

[0582] Step 5:

[0583] The server uses the user's location information to access a database of nearby vending machines and select the most suitable nutritional supplement item.

[0584] Input: Identified nutrient deficiencies and user location information

[0585] Output: Information on selected nutritional supplement items

[0586] Specific operation: The generating AI lists nearby vending machines based on the user's current location and selects an appropriate nutritional supplement item (e.g., the sports drink "DrinkX") from among them.

[0587] Step 6:

[0588] The server sends information about the selected nutritional supplement items to the user's smartphone.

[0589] Input: Information on selected nutritional supplement items

[0590] Output: Information about nutritional supplements sent to the smartphone

[0591] Specific operation: The server sends the selection information to the user's smartphone and displays it to the user via push notification.

[0592] Step 7:

[0593] The device (smartphone) notifies the user of information about the selected nutritional supplement item.

[0594] Input: Information about nutritional supplement items sent from the server.

[0595] Output: Information on nutritional supplement items notified to the user.

[0596] Specific action: The smartphone uses its push notification function to display a message to the user such as, "We recommend DrinkX, which is available for purchase at a nearby vending machine."

[0597] Step 8:

[0598] The user checks the notification and purchases the recommended nutritional supplement from a nearby vending machine.

[0599] Input: Notification information from smartphone

[0600] Output: Purchased nutritional supplements

[0601] Specific action: The user checks the notification, goes to a nearby vending machine, and purchases the selected nutritional supplement item (e.g., "DrinkX").

[0602] (Application Example 1)

[0603] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0604] Traditional nutritional supplementation methods required users to predict their own nutrient deficiencies and select appropriate foods and beverages, making effective and immediate nutritional supplementation difficult. As a result, users often spent considerable time and effort on proper nutrition. This was particularly problematic when rapid nutritional replenishment was needed, such as after exercise. Furthermore, there was a lack of systems that could suggest optimal foods based on biometric data, highlighting the need for highly accurate recommendations.

[0605] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0606] In this invention, the server includes a device for collecting biometric data, a terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients that the user is lacking, means for selecting the optimal meal from multiple meals from a partnered food delivery service based on the identified nutrients, and means for notifying the user's terminal of the selected meal information. This enables the user to quickly and efficiently replenish nutrients that are lacking after exercise or in daily life.

[0607] "During and after exercise" refers to the period during which a user is performing physical exercise and the subsequent recovery period.

[0608] "User biometric data" refers to information that indicates the physiological state of the human body, such as heart rate, calories burned, exercise time, and sweat volume.

[0609] A "device" is an instrument used to collect a user's biometric data, and includes smartwatches and exercise trackers.

[0610] A "terminal" is a device used to transmit collected data to a server, and includes the user's smartphone, for example.

[0611] "Generative AI" refers to artificial intelligence technology used to analyze a user's biometric data and identify deficient nutrients.

[0612] "Nutrient deficiencies" refer to nutrients such as vitamins, minerals, and water that are lacking in the user's body.

[0613] A "food delivery service" refers to a company or system that provides a service of delivering specified meals to a user's designated location.

[0614] "Meals" refers to dishes and food items containing nutrients necessary for the user's body, provided by partnered food delivery services.

[0615] "Selecting" refers to the process of determining the optimal foods and beverages based on the user's biometric data and nutritional deficiencies.

[0616] "Notify" refers to the action of informing the user about the selected food or beverages.

[0617] In this invention, the user first wears a device to collect biometric data during and after exercise. Specifically, devices such as smartwatches or exercise trackers are used. This records biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0618] The collected biometric data is transmitted to the user's smartphone using communication methods such as Bluetooth. The smartphone then transfers this data to a server via the internet. The server is equipped with a generative AI model that analyzes the received biometric data to identify nutrients that the user is lacking.

[0619] The generative AI model utilizes cutting-edge technologies such as OpenAI GPT-4 and includes advanced algorithms for analyzing biometric data. This model identifies specific nutrients that the user may be lacking, such as water, vitamins, and minerals.

[0620] Based on the nutritional information identified by the generating AI, the server consults the database of partner food delivery services to select the most suitable meal for the user. The user's location is also considered during the selection process to ensure the meal provides the quickest and most effective nutritional support.

[0621] The selected meal information is sent from the server to the smartphone. The smartphone uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the selected meal information. The notification includes "recommended meal sets and nutritionally balanced menus," which the user can then review.

[0622] Once a user checks the notification and selects a meal, it is delivered by a partnered food delivery service. This allows users to efficiently replenish any nutritional deficiencies they may have.

[0623] As a concrete example, a user records biometric data with a smartwatch while jogging in the morning, and this data is sent to a server via a smartphone. If the AI ​​analyzes the data and determines that the user is deficient in water and vitamin C, a food delivery service will suggest a meal rich in vitamin C (e.g., a spinach and orange salad) based on that information. Once the user selects the meal, it is delivered quickly.

[0624] The following prompts are used as input to the generative AI model:

[0625] "We have received the user's biometric data. Based on heart rate, calories burned, exercise time, and sweat volume, please analyze the nutrients that are lacking and suggest appropriate menu items from a food delivery service based on the user's current location."

[0626] This allows users to achieve a healthier lifestyle.

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The user wears a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. The input is the user's biometric data, and the output is the recording of this data on the smartwatch.

[0630] Step 2:

[0631] A smartwatch transmits biometric data collected via Bluetooth to the user's smartphone. The input is the biometric data stored in the smartwatch, and the output is the data transferred to the smartphone. Specifically, data compression and transfer are performed using the Bluetooth communication function.

[0632] Step 3:

[0633] A smartphone transmits biometric data to a server via the internet. The input is the biometric data received by the smartphone, which is then transmitted to the server via the internet. The output is the biometric data stored on the server. Specifically, the data transfer is performed using the HTTPS protocol.

[0634] Step 4:

[0635] A generative AI model installed on the server analyzes the received biometric data to identify deficient nutrients. The input is biometric data stored on the server, and the generative AI processes the data to identify deficient nutrients such as water, vitamin C, and iron based on factors like heart rate and calorie expenditure. The output is a list of deficient nutrients.

[0636] Step 5:

[0637] The server selects the optimal meal by referencing the database of partner food delivery services based on information about missing nutrients. The input is a list of missing nutrients, and the system selects the best meal menu through a database search. The output is a list of nutritionally balanced meals. Specifically, it uses SQL queries to retrieve the necessary information from the database.

[0638] Step 6:

[0639] The server sends the selected meal information to the user's smartphone. The input is the selected meal information, which is sent to the user's smartphone using a push notification system (e.g., Firebase Cloud Messaging). The output is the meal information displayed on the user's smartphone.

[0640] Step 7:

[0641] The user checks a notification displayed on their smartphone and selects a suggested meal. The input is the meal information displayed on the smartphone, and the output is the information of the meal selected by the user. Specifically, confirmation and selection are performed using the smartphone's app interface.

[0642] Step 8:

[0643] A food delivery service picks up the selected meal and delivers it to the user's specified location. The input is information about the meal selected by the user, and the output is the meal delivered to the user. Specifically, order confirmation, cooking, and delivery are all handled using a delivery application.

[0644] These steps allow users to efficiently replenish deficient nutrients and achieve a healthy lifestyle.

[0645] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0646] This invention combines an emotion engine with a system that selects the optimal beverage from nearby vending machines by collecting biometric data, analyzing that data using generated AI, and identifying deficient nutrients. The emotion engine recognizes the user's emotions and incorporates that data into the identification of nutrients, enabling more personalized nutritional supplementation. The specific form is described below.

[0647] System Configuration

[0648] The system of the present invention consists of the following main components.

[0649] 1. Biometric data collection devices

[0650] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0651] 2. Data transmission terminal

[0652] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[0653] 3. Server for data analysis

[0654] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[0655] 4. Emotional Engine

[0656] The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine collects user emotion data using facial recognition and speech recognition technologies.

[0657] 5. Methods for selecting the optimal beverage

[0658] Server: The generating AI uses user location information and sentiment data to refer to a database of nearby vending machines and select the most suitable beverage from those offered.

[0659] 6. Means of notification

[0660] Server: Notifies the user's smartphone of the optimal beverage selection result.

[0661] Device: A smartphone notifies the user of the selected beverage information.

[0662] Program processing

[0663] The program for this system works as follows:

[0664] 1. User-driven data collection

[0665] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume, as well as emotional data using facial recognition and voice recognition technology.

[0666] 2. Data transmission

[0667] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[0668] Device: A smartphone transmits biometric and emotional data to a server via the internet.

[0669] 3. Data analysis using generative AI

[0670] Server: The generating AI analyzes the received biometric and emotional data to identify nutrients that are deficient in the user's body. This analysis determines deficiencies in water, B vitamins, sodium, etc., based on heart rate variability, calorie expenditure, exercise intensity, sweat volume, and the user's emotional state (e.g., under stress or in a relaxed state).

[0671] 4. Selection of the optimal beverage

[0672] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[0673] Server: The server compares the analysis results with the beverage information from the vending machine to select the most suitable beverage for the user. For example, if the emotion engine recognizes the user's stress level, a beverage containing ingredients with relaxing effects will be selected.

[0674] 5. Notification to the user

[0675] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[0676] Device: A smartphone notifies the user of beverage information, for example, displaying, "We recommend beverage A, which has a relaxing effect and can be purchased from a nearby vending machine."

[0677] Specific example

[0678] 1. User: Ms. Tanaka recorded her heart rate, calories burned, exercise time, and sweat volume using a smartwatch during her morning jog. She also collected her emotional data using facial recognition technology.

[0679] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[0680] 3. Device: The smartphone sent data to the server via the internet.

[0681] 4. Server: The generating AI analyzed Tanaka's data and determined that she was deficient in water and vitamin B. Additionally, the emotion engine recognized that Tanaka was experiencing stress.

[0682] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[0683] 6. Server: The server sent information about "DrinkX" and "DrinkY" to Tanaka's smartphone.

[0684] 7. Device: The smartphone notified Ms. Tanaka, "We recommend DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect."

[0685] 8. User: Tanaka checked the notification and purchased "DrinkX" and "DrinkY" from a nearby vending machine.

[0686] In this way, users can effectively replenish nutrients that their bodies lack and maintain both physical and mental health by selecting the appropriate beverage according to their emotional state.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume. It also analyzes facial expressions using facial recognition technology and collects emotional data from voice using voice recognition technology.

[0690] Step 2:

[0691] Device: The smartwatch collects biometric and emotional data via Bluetooth and transmits it to the user's smartphone.

[0692] Step 3:

[0693] Device: A smartphone transmits biometric and emotional data to a server via the internet. Location information is also transmitted simultaneously.

[0694] Step 4:

[0695] Server: The generating AI receives the transmitted biometric data, emotional data, and location information.

[0696] Step 5:

[0697] Server: The generating AI analyzes the received biometric data. This analysis identifies nutrients (water, B vitamins, sodium, potassium, etc.) that are deficient in the user's body based on heart rate variability, calories burned, exercise intensity, sweat volume, etc.

[0698] Step 6:

[0699] Server: The server analyzes the emotional data received by the generating AI. For example, facial recognition technology determines the user's stress level and relaxation level from their facial expressions, while speech recognition technology determines their emotional state from the tone and speed of their voice.

[0700] Step 7:

[0701] Server: The generating AI comprehensively assesses the nutrients lacking in the user's body and their emotional state, and identifies the optimal nutrients and their combination.

[0702] Step 8:

[0703] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage and effects that depend on the user's emotional state.

[0704] Step 9:

[0705] Server: The server compares the analysis results with the beverage information from the vending machine to select the most suitable beverage for the user. For example, if the user's emotional state is stressed, a beverage with a relaxing effect will be selected.

[0706] Step 10:

[0707] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage, its effects, and the location of the vending machine where it can be purchased.

[0708] Step 11:

[0709] Device: The smartphone receives the selection information and notifies the user. For example, a notification might appear saying, "We recommend beverage A, which has a relaxing effect and can be purchased from a nearby vending machine."

[0710] Step 12:

[0711] User: Checks the notification and heads to a nearby vending machine.

[0712] Step 13:

[0713] User: Check the beverage displayed on the vending machine and purchase it.

[0714] (Example 2)

[0715] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0716] Conventional nutritional supplementation systems identify nutrients and select the optimal beverage based solely on biometric data, failing to take into account the user's emotional state or mental health. Therefore, there is a need for a system that provides personalized nutritional support to achieve comprehensive health management.

[0717] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0718] In this invention, the server includes a device for collecting biometric data, means for collecting emotional data, means for transmitting the collected data to a terminal equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients and emotional states that the user is lacking, means for selecting the optimal beverage from multiple beverages in nearby vending machines based on the identified nutrients and emotional data, and means for notifying the user's terminal of the selected beverage information. This enables personalized nutritional supplementation according to the user's emotional state.

[0719] "During and after exercise" refers to the time period while a user is performing physical activity and immediately after that activity has finished.

[0720] "Biometric data" refers to information about the user's physical condition, such as heart rate, calories burned, exercise time, and sweat volume.

[0721] A "device" refers to equipment used to collect a user's biometric data, such as smartwatches and exercise trackers.

[0722] A "terminal" refers to a communication device used to transmit biometric and emotional data to a server, such as a smartphone or tablet.

[0723] "Generative AI" refers to artificial intelligence algorithms that analyze collected data to identify a user's nutritional and emotional state.

[0724] "Emotional data" refers to information about a user's emotional state, obtained using facial recognition or speech recognition technologies.

[0725] "Nearby vending machines" refers to beverage vending machines located near the user, based on the user's location information.

[0726] An "optimal beverage" refers to a beverage that provides the most suitable nutritional support for the user, based on identified nutrient and emotional data.

[0727] "Notifying" refers to the act of displaying or transmitting selected beverage information to the user's device.

[0728] This invention is a system that collects biometric and emotional data and analyzes the user's nutritional status using generative AI. Furthermore, it also has a function to select the most suitable beverage from nearby vending machines and notify the user. A specific embodiment of this system is described below.

[0729] Hardware configuration

[0730] This system consists of the following main components:

[0731] 1. Biometric data collection devices

[0732] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0733] 2. Data transmission terminal

[0734] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[0735] 3. Server for data analysis

[0736] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[0737] 4. Emotional Engine

[0738] The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine collects user emotion data using facial recognition and speech recognition technologies.

[0739] 5. Methods for selecting the optimal beverage

[0740] Server: The generating AI uses user location information and sentiment data to refer to a database of nearby vending machines and select the most suitable beverage from those offered.

[0741] 6. Means of notification

[0742] Server: Notifies the user's smartphone of the optimal beverage selection result.

[0743] Device: A smartphone notifies the user of the selected beverage information.

[0744] Software Configuration

[0745] 1. The biometric data collection device has an application installed that records heart rate, calories burned, exercise time, and sweat volume in real time.

[0746] 2. The data transmission terminal includes a communication application for sending biometric and emotional data to the server.

[0747] 3. The data analysis server is equipped with a generative AI model that analyzes the collected data, utilizing machine learning frameworks such as Python and TensorFlow.

[0748] 4. The emotion engine is built using face recognition libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[0749] Specific example

[0750] 1. User: Use a smartwatch during exercise to record heart rate, calories burned, exercise time, and sweat volume. After exercise, use your smartphone's camera to scan your face or add emotional data via voice input.

[0751] 2. Terminal: The smartwatch sends the data it collects via Bluetooth to the smartphone, and the smartphone sends the data to the server via the internet.

[0752] 3. Server: Analyzes the received data and identifies that the user is deficient in vitamin B. The emotion engine also determines that the user's emotional state indicates a high stress level.

[0753] 4. Server: Referencing a database of nearby vending machines, the generating AI selects "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[0754] 5. Server: Sends the selected beverage information to the user's smartphone.

[0755] 6. Device: The smartphone notifies the user that "DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect, are recommended."

[0756] This system allows users to effectively replenish nutrients that their bodies lack and select appropriate beverages according to their emotional state, thereby maintaining both physical and mental health.

[0757] Example of a prompt

[0758] "I'm looking for a relaxing drink. Could you tell me what I can buy from a nearby vending machine?"

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] User: Wear a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0762] Input: User's physical condition during and after exercise.

[0763] Output: Biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0764] Specific operation: The smartwatch's sensors record this data in real time.

[0765] Step 2:

[0766] User: Uses facial recognition and speech recognition technologies to collect their own emotional data.

[0767] Input: User's facial expressions and voice.

[0768] Output: Emotional data (e.g., stress, relaxation, joy, etc.).

[0769] Specific operation: Uses the smartphone's camera and microphone to analyze facial expressions and voice and generate emotion data.

[0770] Step 3:

[0771] Device: The smartwatch collects biometric data via Bluetooth and transmits it to the user's smartphone.

[0772] Input: Biometric data stored on a smartwatch.

[0773] Output: Biometric data transferred to a smartphone.

[0774] Specific operation: Data is transferred from the smartwatch to the smartphone using a Bluetooth communication module.

[0775] Step 4:

[0776] Device: A smartphone transmits collected biometric and emotional data to a server via the internet.

[0777] Input: Biometric data and emotional data stored on a smartphone.

[0778] Output: Biometric and emotional data transferred to the server.

[0779] Specific operation: Data is sent to the server using an API. The data is typically sent in JSON format.

[0780] Step 5:

[0781] Server: Analyzes received biometric and emotional data to identify nutrients lacking in the user's body and their emotional state.

[0782] Input: Biometric and emotional data stored on the server.

[0783] Output: Identification of the user's nutritional deficiencies and emotional state.

[0784] Specific operation: The generating AI analyzes fluctuating data such as heart rate and calorie expenditure to identify deficiencies in vitamins B, sodium, water, etc. Simultaneously, the emotion engine analyzes emotional data to determine stress levels and relaxation levels.

[0785] Step 6:

[0786] Server: Based on the user's location information, it refers to a database of nearby vending machines and selects the most suitable beverage.

[0787] Input: Location information, biometric data analysis results, emotional data analysis results.

[0788] Output: Selection results for the optimal beverage.

[0789] Specific operation: The generating AI uses location information to search a database and identify beverages that are rich in vitamin B or have a relaxing effect.

[0790] Step 7:

[0791] Server: Notifies the user's smartphone of the selected optimal beverage information.

[0792] Input: Selection results for the optimal beverage.

[0793] Output: Beverage information sent to the smartphone.

[0794] Specific operation: The server sends the selection results to the smartphone in JSON format.

[0795] Step 8:

[0796] Device: A smartphone notifies the user of the selected beverage information.

[0797] Input: Beverage information sent from the server.

[0798] Output: Beverage information displayed to the user.

[0799] Specific action: Using the smartphone's notification function, display a message saying, "We recommend DrinkX, available from a nearby vending machine, and DrinkY, which has a relaxing effect."

[0800] This series of processing steps allows users to effectively replenish nutrients that their bodies are lacking and select the appropriate beverage according to their emotional state.

[0801] (Application Example 2)

[0802] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0803] Conventional technology lacked the means to identify nutrient deficiencies based on a driver's biometric data and select appropriate beverages. Furthermore, there was no technology to optimize nutritional supplementation in response to a driver's emotional state, making efficient health management difficult. This resulted in fatigue and decreased concentration during long drives, and could not be expected to improve safety.

[0804] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0805] In this invention, the server includes a device for collecting the driver's biometric data, a communication terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients the driver is lacking, an emotion recognition engine for collecting the driver's emotional data and reflecting it in the identification of nutrients, means for selecting the optimal beverage from multiple beverages in nearby vending machines based on the identified nutrients and emotional data, and means for notifying the driver's terminal of the selected beverage information. This makes it possible to monitor the driver's health status in real time and provide beverages that correspond to the necessary nutrients and emotions at the optimal time.

[0806] "Driver biometric data" refers to physical information such as the driver's heart rate, calories burned, driving time, and sweat volume. This data is used to understand the driver's health status.

[0807] "Generative AI" refers to artificial intelligence technology that analyzes collected biometric and emotional data and outputs optimal results tailored to a specific purpose.

[0808] An "emotion recognition engine" refers to technology that analyzes a driver's facial expressions and voice to identify their emotional state.

[0809] A "communication terminal" refers to a device, such as a smartwatch or a vehicle's central control unit, that transmits biometric data and emotional data to a server.

[0810] "Beverage selection method" refers to a method for selecting the most suitable beverage for the driver by referring to beverage information from nearby vending machines, based on identified nutritional and emotional data.

[0811] "Notification means" refers to a means of transmitting selected beverage information to the driver's terminal and informing the driver of that information.

[0812] A "vending machine" refers to an automatic vending machine for beverages, food, etc., that is installed in a location where drivers can easily use it.

[0813] This invention relates to a system for installation in autonomous vehicles that monitors the driver's health and emotional state in real time and suggests beverages to replenish necessary nutrients at the appropriate time. This system includes the following main components:

[0814] System Configuration

[0815] 1. Driver data acquisition device

[0816] The driver wears a smartwatch or other device to collect biometric data such as heart rate, calories burned, driving time, and sweat volume. Additionally, a facial recognition camera and microphone are installed inside the vehicle to collect the driver's emotional data. This allows for simultaneous monitoring of the driver's physical information and emotional state.

[0817] 2. Data transmission means

[0818] The smartwatch transmits data collected via Bluetooth to the vehicle's central control unit (CCU). The CCU then transmits the data to an analysis server via the internet. As a result, biometric and emotional data are aggregated on the server.

[0819] 3. Data analysis using generative AI

[0820] The server is equipped with a generative AI model. This generative AI model analyzes collected biometric and emotional data to identify nutrients that the driver is lacking. For example, it determines deficiencies in water, vitamins, sodium, etc., from heart rate variability, calorie expenditure, driving intensity, sweat volume, and the driver's emotional state.

[0821] 4. Emotion Recognition Engine

[0822] The server uses an emotion recognition engine to analyze the driver's emotional data and incorporate it into identifying nutrients. For example, it can detect stress and fatigue during driving and suggest beverages with relaxing effects.

[0823] 5. Methods for selecting the optimal beverage

[0824] The AI ​​generates recommendations and selects the most suitable beverage for the driver based on the analysis results. In doing so, it references a database of nearby vending machines based on the driver's location information and compares the nutritional information of the beverages offered. This allows the AI ​​to select the optimal beverage for the driver.

[0825] 6. Means of notification

[0826] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the driver. The autonomous vehicle's infotainment system (IVI) displays to the driver, "We recommend beverage A, which has a relaxing effect and can be purchased at a nearby service area."

[0827] Specific example

[0828] 1. User: A typical driver will use a smartwatch and in-car camera to collect heart rate, calories burned, driving time, sweat volume, and emotional data while driving long distances.

[0829] 2. Terminal: The smartwatch transmits data to the CCU via Bluetooth, and the CCU transmits the data to the server via the internet.

[0830] 3. Server: The generation AI model analyzes the data and identifies deficient nutrients (e.g., water and vitamin B). Additionally, the emotion recognition engine recognizes the driver's stress level.

[0831] 4. Server: The generating AI references data from vending machines near the driver and selects beverages that are rich in vitamin B and have stress-reducing effects.

[0832] 5. Server: The server transmits the selected beverage information to the CCU, which then displays the "recommended beverage" via the IVI system.

[0833] Example of a prompt

[0834] "Please analyze the following biometric and emotional data to identify any nutrient deficiencies: Heart rate: [data], Calories burned: [data], Sweat volume: [data]. Facial recognition result: Stress level."

[0835] This allows drivers to efficiently replenish nutrients and select appropriate beverages according to their emotional state, thereby improving safety and comfort during long-distance driving.

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] The user wears a smartwatch that collects biometric data (heart rate, calories burned, driving time, sweat volume) during and after driving. A facial recognition camera and microphone collect the driver's emotional data (e.g., stress level, relaxation level).

[0839] Inputs: Driver's heart rate, calories burned, driving time, sweat volume, and emotion data from facial recognition camera and microphone.

[0840] Output: Collected biometric and emotional data

[0841] Step 2:

[0842] The device (smartwatch) transmits the collected data to the vehicle's central control unit (CCU) via Bluetooth.

[0843] Input: Biometric and emotional data from smartwatches

[0844] Output: Biometric and emotional data transmitted to the CCU

[0845] Step 3:

[0846] The CCU sends the data collected via the internet to an analysis server.

[0847] Input: Biometric and emotional data transmitted from the CCU.

[0848] Output: Biometric and emotional data sent to the analysis server.

[0849] Step 4:

[0850] The server (generating AI model) analyzes the received biometric and emotional data to identify nutrients the driver is lacking. This analysis is based on heart rate variability, calorie expenditure, driving intensity, sweat volume, and emotional state (e.g., stressful environment, relaxed state).

[0851] Input: Biometric data and emotional data

[0852] Output: Identified nutrient deficiencies (e.g., water, vitamin B, sodium)

[0853] Step 5:

[0854] The server (emotion recognition engine) analyzes the driver's emotional data and uses it to identify nutrients. For example, if stress is detected while driving, nutrients with relaxing effects will be prioritized.

[0855] Input: Sentiment data

[0856] Output: Corrected results for identified nutrients

[0857] Step 6:

[0858] Based on identified nutritional and emotional data, the server references the driver's location and selects the most suitable beverage from a database of nearby vending machines.

[0859] Input: Identified nutrients, emotional data, location information

[0860] Output: Information on the best beverage selected from nearby vending machines.

[0861] Step 7:

[0862] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the user of the beverage information through the infotainment system (IVI).

[0863] Input: Selected beverage information

[0864] Output: Beverage information displayed on the driver's IVI system (e.g., "We recommend beverage A, a relaxing drink available at the nearby service area.")

[0865] This will allow drivers to efficiently replenish necessary nutrients and select appropriate beverages according to their emotional state.

[0866] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0867] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0868] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0869] [Third Embodiment]

[0870] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0871] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0872] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0873] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0874] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0875] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0876] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0877] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0878] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0879] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0880] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0881] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0882] This invention is a system that collects biometric data, analyzes that data using generated AI, identifies deficient nutrients, and selects the most suitable beverage from nearby vending machines. This system is designed to allow users to efficiently replenish the necessary nutrients. The specific form is described below.

[0883] System Configuration

[0884] The system of the present invention consists of the following main components.

[0885] 1. Biometric data collection devices

[0886] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0887] 2. Data transmission terminal

[0888] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[0889] 3. Server for data analysis

[0890] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[0891] 4. Methods for selecting the optimal beverage

[0892] Server: Based on the analysis results, the generating AI uses user location information to access a database of nearby vending machines. This allows it to select the most suitable beverage from those offered by the vending machines.

[0893] 5. Means of notification

[0894] Server: Notifies the user's smartphone of the optimal beverage selection result.

[0895] Device: A smartphone notifies the user of the selected beverage information.

[0896] Program processing

[0897] The program for this system works as follows:

[0898] 1. User-driven data collection

[0899] User: Collects biometric data such as heart rate, calories burned, exercise time, and sweat volume using a smartwatch during and after exercise.

[0900] 2. Data transmission

[0901] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[0902] Terminal: A smartphone sends data to a server via the internet.

[0903] 3. Data analysis using generative AI

[0904] Server: The generating AI analyzes the received biometric data to identify nutrients that are deficient in the user's body. For example, the analysis might determine that there is a deficiency in water, B vitamins, sodium, etc.

[0905] 4. Selection of the optimal beverage

[0906] Server: The generating AI uses the user's location information to refer to a database of nearby vending machines and selects the most suitable beverage for nutritional replenishment from those offered.

[0907] 5. Notification to the user

[0908] Server: Sends selected beverage information to the user's smartphone.

[0909] Terminal: A smartphone notifies the user of beverage information, for example, displaying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[0910] Specific example

[0911] 1. User: Mr. Tanaka recorded his heart rate, calories burned, exercise time, and sweat volume using a smartwatch during his morning jog.

[0912] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[0913] 3. Server: The smartphone sent data to the server via the internet.

[0914] 4. Server: The generating AI analyzed Tanaka's data and determined that he was deficient in water and vitamin B.

[0915] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B.

[0916] 6. Server: The server sent information about "DrinkX" to Tanaka's smartphone.

[0917] 7. Device: The smartphone notified Ms. Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[0918] 8. User: Mr. Tanaka checked the notification and purchased "DrinkX" from a nearby vending machine.

[0919] In this way, users can effectively replenish nutrients that are lacking in their bodies, and it can also contribute to maintaining their physical condition after exercise.

[0920] The following describes the processing flow.

[0921] Step 1:

[0922] User: During and after exercise, use a smartwatch to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[0923] Step 2:

[0924] Device: The smartwatch collects biometric data and transmits it to the user's smartphone via Bluetooth.

[0925] Step 3:

[0926] Device: The smartphone transmits biometric data to the server via the internet. If necessary, the user's location information is also transmitted at this time.

[0927] Step 4:

[0928] Server: The generating AI receives the transmitted biometric data and location information.

[0929] Step 5:

[0930] Server: The generating AI analyzes the received biometric data. This analysis identifies nutrients that are deficient in the user's body based on factors such as heart rate variability, calorie expenditure, exercise intensity, and sweat volume. For example, it may determine that the user is deficient in water, B vitamins, sodium, potassium, etc.

[0931] Step 6:

[0932] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[0933] Step 7:

[0934] Server: Compares the analysis results with the beverage information from the vending machine to select the optimal beverage for the user. For example, it might determine that "Sports Drink A" is optimal because it contains a lot of sodium and potassium.

[0935] Step 8:

[0936] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[0937] Step 9:

[0938] Terminal: The smartphone receives the selection information and notifies the user. For example, it displays a notification saying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[0939] Step 10:

[0940] User: Checks the notification and heads to a nearby vending machine.

[0941] Step 11:

[0942] User: Check the beverage displayed on the vending machine and purchase it.

[0943] (Example 1)

[0944] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0945] In today's busy lifestyle, it is difficult for users to efficiently and effectively replenish their body's nutrients. Especially after exercise, users need to quickly replenish deficient nutrients, but there are insufficient means to quickly identify which nutrients are lacking and to provide appropriate nutritional support. Furthermore, there is no system that selects and notifies users of the most suitable nutritional supplements from nearby vending machines, leaving users to gather information and make choices themselves.

[0946] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0947] In this invention, the server includes means for generating AI to analyze collected data and identify nutrients that the user is lacking, means for selecting the optimal nutritional supplement item from multiple nutritional supplement items in nearby vending machines based on the identified nutrients, and means for notifying the user's communication terminal of the selected nutritional supplement item information. As a result, the user can have the optimal nutritional supplement item automatically selected after exercise and quickly replenish any deficient nutrients.

[0948] A "wearable device" is a computer device that a user can wear on their body and use to collect biometric data such as heart rate and calories burned.

[0949] A "communication terminal" is a device used to transmit biometric data to a computer equipped with AI, and primarily refers to mobile devices such as smartphones and tablets.

[0950] "Generative AI" is an artificial intelligence technology that analyzes collected biometric data to identify nutrients that the user is lacking.

[0951] "Nutrients" refer to components such as vitamins, minerals, and water that are needed in the user's body.

[0952] "Nutritional supplement items" are products such as beverages and foods offered in vending machines that are designed to replenish nutrients that are lacking in the user's body.

[0953] A "vending machine" is a machine that allows users to directly operate and purchase nutritional supplements such as beverages and food.

[0954] "Analysis" is the process of evaluating collected data, extracting information, and interpreting the data according to a specific purpose.

[0955] "Notification" refers to the act of informing the user about selected nutritional supplement items, and is primarily done using communication devices.

[0956] This invention is a system that collects a user's biometric data, analyzes that data to identify deficient nutrients, and selects the most suitable nutritional supplement item from a vending machine. The following describes a specific implementation of this system.

[0957] System components

[0958] 1. Wearable devices

[0959] Users wear wearable devices to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. These devices include heart rate monitors and accelerometers (e.g., smartwatches).

[0960] 2. Communication terminals

[0961] Wearable devices use communication methods such as Bluetooth to collect biometric data and transmit it to the user's smartphone. The smartphone then transmits the data via the internet to a server equipped with AI that generates data.

[0962] 3. Server equipped with generation AI

[0963] The server analyzes biometric data received via the internet. This server is equipped with a generating AI (e.g., GPT-4, a proprietary nutritional analysis model) that analyzes the user's data to identify any nutritional deficiencies.

[0964] 4. Nutrient Identification and Optimal Beverage Selection Methods

[0965] Based on the analysis results, the generating AI uses the user's location information to access a database of nearby vending machines. It then selects the most suitable nutritional supplement from the items offered in the vending machines.

[0966] 5. Means of notification

[0967] The server notifies the user's smartphone of the selected nutritional supplement items. The smartphone receives this information and displays it to the user.

[0968] Specific example

[0969] 1. User

[0970] User Tanaka uses a smartwatch to record his heart rate, calories burned, exercise time, and sweat volume during his morning jog.

[0971] Specific example: "Mr. Tanaka recorded his heart rate and calories burned using a smartwatch while jogging in the morning."

[0972] 2. Terminal

[0973] The wearable device collects data and sends it to Mr. Tanaka's smartphone. The smartphone then sends the data to a server via the internet.

[0974] Specific example: "After exercise, the smartwatch collected data and sent it to Mr. Tanaka's smartphone via Bluetooth. The smartphone then sent the data to a server via the internet."

[0975] 3. Server

[0976] The server uses AI to analyze Tanaka's data and determines that he is deficient in water and vitamin B. Then, it refers to data from nearby vending machines and selects "DrinkX," a sports drink rich in vitamin B.

[0977] Specific example: "The server analyzed Mr. Tanaka's biometric data and identified that he was deficient in water and vitamin B. It then consulted information from nearby vending machines and selected 'DrinkX,' a sports drink rich in vitamin B."

[0978] 4. Notification

[0979] The server sends information about "DrinkX" to Tanaka's smartphone, and the smartphone notifies Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[0980] Specific example: "The server sent information about the selected DrinkX to Mr. Tanaka's smartphone, and the smartphone notified Mr. Tanaka, 'We recommend DrinkX that can be purchased from a nearby vending machine.'"

[0981] Example of a prompt

[0982] "We will design a system that collects data such as heart rate and calorie consumption using a smartwatch, analyzes this data with a generating AI to identify deficient nutrients, and then selects the most suitable beverage from a vending machine."

[0983] In this way, users can efficiently and effectively replenish deficient nutrients, which can also contribute to maintaining their physical condition after exercise.

[0984] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0985] Step 1:

[0986] Users wear wearable devices during and after exercise to collect biometric data (heart rate, calories burned, exercise time, and sweat volume).

[0987] Input: User biometric information during and after exercise

[0988] Output: Collected biometric data (heart rate, calories burned, exercise time, sweat volume)

[0989] Specific operation: The user starts jogging while wearing the wearable device, and the sensors record data such as heart rate and calories burned.

[0990] Step 2:

[0991] The device (wearable device) transmits collected biometric data to the user's smartphone via Bluetooth.

[0992] Input: Biometric data collected by wearable devices

[0993] Output: Biometric data sent to smartphone

[0994] Specific operation: After exercise, the wearable device automatically syncs with the smartphone and transfers data such as heart rate and calories burned.

[0995] Step 3:

[0996] The device (smartphone) transmits biometric data to the server via the internet.

[0997] Input: Biometric data stored on a smartphone

[0998] Output: Biometric data sent to the server

[0999] Specific operation: A dedicated smartphone application uploads biometric data to a server using Wi-Fi or mobile data communication.

[1000] Step 4:

[1001] The server uses generated AI to analyze biometric data and identify nutrients that are deficient in the user's body.

[1002] Input: Biometric data sent to the server

[1003] Output: Deficit nutrients identified through analysis

[1004] Specific operation: The generating AI cleans the data and analyzes data such as heart rate, calories burned, exercise time, and sweat volume to determine if there is a deficiency in water or vitamin B.

[1005] Step 5:

[1006] The server uses the user's location information to access a database of nearby vending machines and select the most suitable nutritional supplement item.

[1007] Input: Identified nutrient deficiencies and user location information

[1008] Output: Information on selected nutritional supplement items

[1009] Specific operation: The generating AI lists nearby vending machines based on the user's current location and selects an appropriate nutritional supplement item (e.g., the sports drink "DrinkX") from among them.

[1010] Step 6:

[1011] The server sends information about the selected nutritional supplement items to the user's smartphone.

[1012] Input: Information on selected nutritional supplement items

[1013] Output: Information about nutritional supplements sent to the smartphone

[1014] Specific operation: The server sends the selection information to the user's smartphone and displays it to the user via push notification.

[1015] Step 7:

[1016] The device (smartphone) notifies the user of information about the selected nutritional supplement item.

[1017] Input: Information about nutritional supplement items sent from the server.

[1018] Output: Information on nutritional supplement items notified to the user.

[1019] Specific action: The smartphone uses its push notification function to display a message to the user such as, "We recommend DrinkX, which is available for purchase at a nearby vending machine."

[1020] Step 8:

[1021] The user checks the notification and purchases the recommended nutritional supplement from a nearby vending machine.

[1022] Input: Notification information from smartphone

[1023] Output: Purchased nutritional supplements

[1024] Specific action: The user checks the notification, goes to a nearby vending machine, and purchases the selected nutritional supplement item (e.g., "DrinkX").

[1025] (Application Example 1)

[1026] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1027] Traditional nutritional supplementation methods required users to predict their own nutrient deficiencies and select appropriate foods and beverages, making effective and immediate nutritional supplementation difficult. As a result, users often spent considerable time and effort on proper nutrition. This was particularly problematic when rapid nutritional replenishment was needed, such as after exercise. Furthermore, there was a lack of systems that could suggest optimal foods based on biometric data, highlighting the need for highly accurate recommendations.

[1028] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1029] In this invention, the server includes a device for collecting biometric data, a terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients that the user is lacking, means for selecting the optimal meal from multiple meals from a partnered food delivery service based on the identified nutrients, and means for notifying the user's terminal of the selected meal information. This enables the user to quickly and efficiently replenish nutrients that are lacking after exercise or in daily life.

[1030] "During and after exercise" refers to the period during which a user is performing physical exercise and the subsequent recovery period.

[1031] "User biometric data" refers to information that indicates the physiological state of the human body, such as heart rate, calories burned, exercise time, and sweat volume.

[1032] A "device" is an instrument used to collect a user's biometric data, and includes smartwatches and exercise trackers.

[1033] A "terminal" is a device used to transmit collected data to a server, and includes the user's smartphone, for example.

[1034] "Generative AI" refers to artificial intelligence technology used to analyze a user's biometric data and identify deficient nutrients.

[1035] "Nutrient deficiencies" refer to nutrients such as vitamins, minerals, and water that are lacking in the user's body.

[1036] A "food delivery service" refers to a company or system that provides a service of delivering specified meals to a user's designated location.

[1037] "Meals" refers to dishes and food items containing nutrients necessary for the user's body, provided by partnered food delivery services.

[1038] "Selecting" refers to the process of determining the optimal foods and beverages based on the user's biometric data and nutritional deficiencies.

[1039] "Notify" refers to the action of informing the user about the selected food or beverages.

[1040] In this invention, the user first wears a device to collect biometric data during and after exercise. Specifically, devices such as smartwatches or exercise trackers are used. This records biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1041] The collected biometric data is transmitted to the user's smartphone using communication methods such as Bluetooth. The smartphone then transfers this data to a server via the internet. The server is equipped with a generative AI model that analyzes the received biometric data to identify nutrients that the user is lacking.

[1042] The generative AI model utilizes cutting-edge technologies such as OpenAI GPT-4 and includes advanced algorithms for analyzing biometric data. This model identifies specific nutrients that the user may be lacking, such as water, vitamins, and minerals.

[1043] Based on the nutritional information identified by the generating AI, the server consults the database of partner food delivery services to select the most suitable meal for the user. The user's location is also considered during the selection process to ensure the meal provides the quickest and most effective nutritional support.

[1044] The selected meal information is sent from the server to the smartphone. The smartphone uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the selected meal information. The notification includes "recommended meal sets and nutritionally balanced menus," which the user can then review.

[1045] Once a user checks the notification and selects a meal, it is delivered by a partnered food delivery service. This allows users to efficiently replenish any nutritional deficiencies they may have.

[1046] As a concrete example, a user records biometric data with a smartwatch while jogging in the morning, and this data is sent to a server via a smartphone. If the AI ​​analyzes the data and determines that the user is deficient in water and vitamin C, a food delivery service will suggest a meal rich in vitamin C (e.g., a spinach and orange salad) based on that information. Once the user selects the meal, it is delivered quickly.

[1047] The following prompts are used as input to the generative AI model:

[1048] "We have received the user's biometric data. Based on heart rate, calories burned, exercise time, and sweat volume, please analyze the nutrients that are lacking and suggest appropriate menu items from a food delivery service based on the user's current location."

[1049] This allows users to achieve a healthier lifestyle.

[1050] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1051] Step 1:

[1052] The user wears a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. The input is the user's biometric data, and the output is the recording of this data on the smartwatch.

[1053] Step 2:

[1054] A smartwatch transmits biometric data collected via Bluetooth to the user's smartphone. The input is the biometric data stored in the smartwatch, and the output is the data transferred to the smartphone. Specifically, data compression and transfer are performed using the Bluetooth communication function.

[1055] Step 3:

[1056] A smartphone transmits biometric data to a server via the internet. The input is the biometric data received by the smartphone, which is then transmitted to the server via the internet. The output is the biometric data stored on the server. Specifically, the data transfer is performed using the HTTPS protocol.

[1057] Step 4:

[1058] A generative AI model installed on the server analyzes the received biometric data to identify deficient nutrients. The input is biometric data stored on the server, and the generative AI processes the data to identify deficient nutrients such as water, vitamin C, and iron based on factors like heart rate and calorie expenditure. The output is a list of deficient nutrients.

[1059] Step 5:

[1060] The server selects the optimal meal by referencing the database of partner food delivery services based on information about missing nutrients. The input is a list of missing nutrients, and the system selects the best meal menu through a database search. The output is a list of nutritionally balanced meals. Specifically, it uses SQL queries to retrieve the necessary information from the database.

[1061] Step 6:

[1062] The server sends the selected meal information to the user's smartphone. The input is the selected meal information, which is sent to the user's smartphone using a push notification system (e.g., Firebase Cloud Messaging). The output is the meal information displayed on the user's smartphone.

[1063] Step 7:

[1064] The user checks a notification displayed on their smartphone and selects a suggested meal. The input is the meal information displayed on the smartphone, and the output is the information of the meal selected by the user. Specifically, confirmation and selection are performed using the smartphone's app interface.

[1065] Step 8:

[1066] A food delivery service picks up the selected meal and delivers it to the user's specified location. The input is information about the meal selected by the user, and the output is the meal delivered to the user. Specifically, order confirmation, cooking, and delivery are all handled using a delivery application.

[1067] These steps allow users to efficiently replenish deficient nutrients and achieve a healthy lifestyle.

[1068] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1069] This invention combines an emotion engine with a system that selects the optimal beverage from nearby vending machines by collecting biometric data, analyzing that data using generated AI, and identifying deficient nutrients. The emotion engine recognizes the user's emotions and incorporates that data into the identification of nutrients, enabling more personalized nutritional supplementation. The specific form is described below.

[1070] System Configuration

[1071] The system of the present invention consists of the following main components.

[1072] 1. Biometric data collection devices

[1073] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1074] 2. Data transmission terminal

[1075] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[1076] 3. Server for data analysis

[1077] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[1078] 4. Emotional Engine

[1079] The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine collects user emotion data using facial recognition and speech recognition technologies.

[1080] 5. Methods for selecting the optimal beverage

[1081] Server: The generating AI uses user location information and sentiment data to refer to a database of nearby vending machines and select the most suitable beverage from those offered.

[1082] 6. Means of notification

[1083] Server: Notifies the user's smartphone of the optimal beverage selection result.

[1084] Device: A smartphone notifies the user of the selected beverage information.

[1085] Program processing

[1086] The program for this system works as follows:

[1087] 1. User-driven data collection

[1088] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume, as well as emotional data using facial recognition and voice recognition technology.

[1089] 2. Data transmission

[1090] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[1091] Device: A smartphone transmits biometric and emotional data to a server via the internet.

[1092] 3. Data analysis using generative AI

[1093] Server: The generating AI analyzes the received biometric and emotional data to identify nutrients that are deficient in the user's body. This analysis determines deficiencies in water, B vitamins, sodium, etc., based on heart rate variability, calorie expenditure, exercise intensity, sweat volume, and the user's emotional state (e.g., under stress or in a relaxed state).

[1094] 4. Selection of the optimal beverage

[1095] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[1096] Server: The server compares the analysis results with the beverage information from the vending machine to select the most suitable beverage for the user. For example, if the emotion engine recognizes the user's stress level, a beverage containing ingredients with relaxing effects will be selected.

[1097] 5. Notification to the user

[1098] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[1099] Device: A smartphone notifies the user of beverage information, for example, displaying, "We recommend beverage A, which has a relaxing effect and can be purchased from a nearby vending machine."

[1100] Specific example

[1101] 1. User: Ms. Tanaka recorded her heart rate, calories burned, exercise time, and sweat volume using a smartwatch during her morning jog. She also collected her emotional data using facial recognition technology.

[1102] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[1103] 3. Device: The smartphone sent data to the server via the internet.

[1104] 4. Server: The generating AI analyzed Tanaka's data and determined that she was deficient in water and vitamin B. Additionally, the emotion engine recognized that Tanaka was experiencing stress.

[1105] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[1106] 6. Server: The server sent information about "DrinkX" and "DrinkY" to Tanaka's smartphone.

[1107] 7. Device: The smartphone notified Ms. Tanaka, "We recommend DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect."

[1108] 8. User: Tanaka checked the notification and purchased "DrinkX" and "DrinkY" from a nearby vending machine.

[1109] In this way, users can effectively replenish nutrients that their bodies lack and maintain both physical and mental health by selecting the appropriate beverage according to their emotional state.

[1110] The following describes the processing flow.

[1111] Step 1:

[1112] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume. It also analyzes facial expressions using facial recognition technology and collects emotional data from voice using voice recognition technology.

[1113] Step 2:

[1114] Device: The smartwatch collects biometric and emotional data via Bluetooth and transmits it to the user's smartphone.

[1115] Step 3:

[1116] Device: A smartphone transmits biometric and emotional data to a server via the internet. Location information is also transmitted simultaneously.

[1117] Step 4:

[1118] Server: The generating AI receives the transmitted biometric data, emotional data, and location information.

[1119] Step 5:

[1120] Server: The generating AI analyzes the received biometric data. This analysis identifies nutrients (water, B vitamins, sodium, potassium, etc.) that are deficient in the user's body based on heart rate variability, calories burned, exercise intensity, sweat volume, etc.

[1121] Step 6:

[1122] Server: The server analyzes the emotional data received by the generating AI. For example, facial recognition technology determines the user's stress level and relaxation level from their facial expressions, while speech recognition technology determines their emotional state from the tone and speed of their voice.

[1123] Step 7:

[1124] Server: The generating AI comprehensively assesses the nutrients lacking in the user's body and their emotional state, and identifies the optimal nutrients and their combination.

[1125] Step 8:

[1126] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage and effects that depend on the user's emotional state.

[1127] Step 9:

[1128] Server: The server compares the analysis results with the beverage information from the vending machine to select the most suitable beverage for the user. For example, if the user's emotional state is stressed, a beverage with a relaxing effect will be selected.

[1129] Step 10:

[1130] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage, its effects, and the location of the vending machine where it can be purchased.

[1131] Step 11:

[1132] Device: The smartphone receives the selection information and notifies the user. For example, a notification might appear saying, "We recommend beverage A, which has a relaxing effect and can be purchased from a nearby vending machine."

[1133] Step 12:

[1134] User: Checks the notification and heads to a nearby vending machine.

[1135] Step 13:

[1136] User: Check the beverage displayed on the vending machine and purchase it.

[1137] (Example 2)

[1138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1139] Conventional nutritional supplementation systems identify nutrients and select the optimal beverage based solely on biometric data, failing to take into account the user's emotional state or mental health. Therefore, there is a need for a system that provides personalized nutritional support to achieve comprehensive health management.

[1140] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1141] In this invention, the server includes a device for collecting biometric data, means for collecting emotional data, means for transmitting the collected data to a terminal equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients and emotional states that the user is lacking, means for selecting the optimal beverage from multiple beverages in nearby vending machines based on the identified nutrients and emotional data, and means for notifying the user's terminal of the selected beverage information. This enables personalized nutritional supplementation according to the user's emotional state.

[1142] "During and after exercise" refers to the time period while a user is performing physical activity and immediately after that activity has finished.

[1143] "Biometric data" refers to information about the user's physical condition, such as heart rate, calories burned, exercise time, and sweat volume.

[1144] A "device" refers to equipment used to collect a user's biometric data, such as smartwatches and exercise trackers.

[1145] A "terminal" refers to a communication device used to transmit biometric and emotional data to a server, such as a smartphone or tablet.

[1146] "Generative AI" refers to artificial intelligence algorithms that analyze collected data to identify a user's nutritional and emotional state.

[1147] "Emotional data" refers to information about a user's emotional state, obtained using facial recognition or speech recognition technologies.

[1148] "Nearby vending machines" refers to beverage vending machines located near the user, based on the user's location information.

[1149] An "optimal beverage" refers to a beverage that provides the most suitable nutritional support for the user, based on identified nutrient and emotional data.

[1150] "Notifying" refers to the act of displaying or transmitting selected beverage information to the user's device.

[1151] This invention is a system that collects biometric and emotional data and analyzes the user's nutritional status using generative AI. Furthermore, it also has a function to select the most suitable beverage from nearby vending machines and notify the user. A specific embodiment of this system is described below.

[1152] Hardware configuration

[1153] This system consists of the following main components:

[1154] 1. Biometric data collection devices

[1155] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1156] 2. Data transmission terminal

[1157] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[1158] 3. Server for data analysis

[1159] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[1160] 4. Emotional Engine

[1161] The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine collects user emotion data using facial recognition and speech recognition technologies.

[1162] 5. Methods for selecting the optimal beverage

[1163] Server: The generating AI uses user location information and sentiment data to refer to a database of nearby vending machines and select the most suitable beverage from those offered.

[1164] 6. Means of notification

[1165] Server: Notifies the user's smartphone of the optimal beverage selection result.

[1166] Device: A smartphone notifies the user of the selected beverage information.

[1167] Software Configuration

[1168] 1. The biometric data collection device has an application installed that records heart rate, calories burned, exercise time, and sweat volume in real time.

[1169] 2. The data transmission terminal includes a communication application for sending biometric and emotional data to the server.

[1170] 3. The data analysis server is equipped with a generative AI model that analyzes the collected data, utilizing machine learning frameworks such as Python and TensorFlow.

[1171] 4. The emotion engine is built using face recognition libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[1172] Specific example

[1173] 1. User: Use a smartwatch during exercise to record heart rate, calories burned, exercise time, and sweat volume. After exercise, use your smartphone's camera to scan your face or add emotional data via voice input.

[1174] 2. Terminal: The smartwatch sends the data it collects via Bluetooth to the smartphone, and the smartphone sends the data to the server via the internet.

[1175] 3. Server: Analyzes the received data and identifies that the user is deficient in vitamin B. The emotion engine also determines that the user's emotional state indicates a high stress level.

[1176] 4. Server: Referencing a database of nearby vending machines, the generating AI selects "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[1177] 5. Server: Sends the selected beverage information to the user's smartphone.

[1178] 6. Device: The smartphone notifies the user that "DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect, are recommended."

[1179] This system allows users to effectively replenish nutrients that their bodies lack and select appropriate beverages according to their emotional state, thereby maintaining both physical and mental health.

[1180] Example of a prompt

[1181] "I'm looking for a relaxing drink. Could you tell me what I can buy from a nearby vending machine?"

[1182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1183] Step 1:

[1184] User: Wear a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1185] Input: User's physical condition during and after exercise.

[1186] Output: Biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1187] Specific operation: The smartwatch's sensors record this data in real time.

[1188] Step 2:

[1189] User: Uses facial recognition and speech recognition technologies to collect their own emotional data.

[1190] Input: User's facial expressions and voice.

[1191] Output: Emotional data (e.g., stress, relaxation, joy, etc.).

[1192] Specific operation: Uses the smartphone's camera and microphone to analyze facial expressions and voice and generate emotion data.

[1193] Step 3:

[1194] Device: The smartwatch collects biometric data via Bluetooth and transmits it to the user's smartphone.

[1195] Input: Biometric data stored on a smartwatch.

[1196] Output: Biometric data transferred to a smartphone.

[1197] Specific operation: Data is transferred from the smartwatch to the smartphone using a Bluetooth communication module.

[1198] Step 4:

[1199] Device: A smartphone transmits collected biometric and emotional data to a server via the internet.

[1200] Input: Biometric data and emotional data stored on a smartphone.

[1201] Output: Biometric and emotional data transferred to the server.

[1202] Specific operation: Data is sent to the server using an API. The data is typically sent in JSON format.

[1203] Step 5:

[1204] Server: Analyzes received biometric and emotional data to identify nutrients lacking in the user's body and their emotional state.

[1205] Input: Biometric and emotional data stored on the server.

[1206] Output: Identification of the user's nutritional deficiencies and emotional state.

[1207] Specific operation: The generating AI analyzes fluctuating data such as heart rate and calorie expenditure to identify deficiencies in vitamins B, sodium, water, etc. Simultaneously, the emotion engine analyzes emotional data to determine stress levels and relaxation levels.

[1208] Step 6:

[1209] Server: Based on the user's location information, it refers to a database of nearby vending machines and selects the most suitable beverage.

[1210] Input: Location information, biometric data analysis results, emotional data analysis results.

[1211] Output: Selection results for the optimal beverage.

[1212] Specific operation: The generating AI uses location information to search a database and identify beverages that are rich in vitamin B or have a relaxing effect.

[1213] Step 7:

[1214] Server: Notifies the user's smartphone of the selected optimal beverage information.

[1215] Input: Selection results for the optimal beverage.

[1216] Output: Beverage information sent to the smartphone.

[1217] Specific operation: The server sends the selection results to the smartphone in JSON format.

[1218] Step 8:

[1219] Device: A smartphone notifies the user of the selected beverage information.

[1220] Input: Beverage information sent from the server.

[1221] Output: Beverage information displayed to the user.

[1222] Specific action: Using the smartphone's notification function, display a message saying, "We recommend DrinkX, available from a nearby vending machine, and DrinkY, which has a relaxing effect."

[1223] This series of processing steps allows users to effectively replenish nutrients that their bodies are lacking and select the appropriate beverage according to their emotional state.

[1224] (Application Example 2)

[1225] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1226] Conventional technology lacked the means to identify nutrient deficiencies based on a driver's biometric data and select appropriate beverages. Furthermore, there was no technology to optimize nutritional supplementation in response to a driver's emotional state, making efficient health management difficult. This resulted in fatigue and decreased concentration during long drives, and could not be expected to improve safety.

[1227] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1228] In this invention, the server includes a device for collecting the driver's biometric data, a communication terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients the driver is lacking, an emotion recognition engine for collecting the driver's emotional data and reflecting it in the identification of nutrients, means for selecting the optimal beverage from multiple beverages in nearby vending machines based on the identified nutrients and emotional data, and means for notifying the driver's terminal of the selected beverage information. This makes it possible to monitor the driver's health status in real time and provide beverages that correspond to the necessary nutrients and emotions at the optimal time.

[1229] "Driver biometric data" refers to physical information such as the driver's heart rate, calories burned, driving time, and sweat volume. This data is used to understand the driver's health status.

[1230] "Generative AI" refers to artificial intelligence technology that analyzes collected biometric and emotional data and outputs optimal results tailored to a specific purpose.

[1231] An "emotion recognition engine" refers to technology that analyzes a driver's facial expressions and voice to identify their emotional state.

[1232] A "communication terminal" refers to a device, such as a smartwatch or a vehicle's central control unit, that transmits biometric data and emotional data to a server.

[1233] "Beverage selection method" refers to a method for selecting the most suitable beverage for the driver by referring to beverage information from nearby vending machines, based on identified nutritional and emotional data.

[1234] "Notification means" refers to a means of transmitting selected beverage information to the driver's terminal and informing the driver of that information.

[1235] A "vending machine" refers to an automatic vending machine for beverages, food, etc., that is installed in a location where drivers can easily use it.

[1236] This invention relates to a system for installation in autonomous vehicles that monitors the driver's health and emotional state in real time and suggests beverages to replenish necessary nutrients at the appropriate time. This system includes the following main components:

[1237] System Configuration

[1238] 1. Driver data acquisition device

[1239] The driver wears a smartwatch or other device to collect biometric data such as heart rate, calories burned, driving time, and sweat volume. Additionally, a facial recognition camera and microphone are installed inside the vehicle to collect the driver's emotional data. This allows for simultaneous monitoring of the driver's physical information and emotional state.

[1240] 2. Data transmission means

[1241] The smartwatch transmits data collected via Bluetooth to the vehicle's central control unit (CCU). The CCU then transmits the data to an analysis server via the internet. As a result, biometric and emotional data are aggregated on the server.

[1242] 3. Data analysis using generative AI

[1243] The server is equipped with a generative AI model. This generative AI model analyzes collected biometric and emotional data to identify nutrients that the driver is lacking. For example, it determines deficiencies in water, vitamins, sodium, etc., from heart rate variability, calorie expenditure, driving intensity, sweat volume, and the driver's emotional state.

[1244] 4. Emotion Recognition Engine

[1245] The server uses an emotion recognition engine to analyze the driver's emotional data and incorporate it into identifying nutrients. For example, it can detect stress and fatigue during driving and suggest beverages with relaxing effects.

[1246] 5. Methods for selecting the optimal beverage

[1247] The AI ​​generates recommendations and selects the most suitable beverage for the driver based on the analysis results. In doing so, it references a database of nearby vending machines based on the driver's location information and compares the nutritional information of the beverages offered. This allows the AI ​​to select the optimal beverage for the driver.

[1248] 6. Means of notification

[1249] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the driver. The autonomous vehicle's infotainment system (IVI) displays to the driver, "We recommend beverage A, which has a relaxing effect and can be purchased at a nearby service area."

[1250] Specific example

[1251] 1. User: A typical driver will use a smartwatch and in-car camera to collect heart rate, calories burned, driving time, sweat volume, and emotional data while driving long distances.

[1252] 2. Terminal: The smartwatch transmits data to the CCU via Bluetooth, and the CCU transmits the data to the server via the internet.

[1253] 3. Server: The generation AI model analyzes the data and identifies deficient nutrients (e.g., water and vitamin B). Additionally, the emotion recognition engine recognizes the driver's stress level.

[1254] 4. Server: The generating AI references data from vending machines near the driver and selects beverages that are rich in vitamin B and have stress-reducing effects.

[1255] 5. Server: The server transmits the selected beverage information to the CCU, which then displays the "recommended beverage" via the IVI system.

[1256] Example of a prompt

[1257] "Please analyze the following biometric and emotional data to identify any nutrient deficiencies: Heart rate: [data], Calories burned: [data], Sweat volume: [data]. Facial recognition result: Stress level."

[1258] This allows drivers to efficiently replenish nutrients and select appropriate beverages according to their emotional state, thereby improving safety and comfort during long-distance driving.

[1259] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1260] Step 1:

[1261] The user wears a smartwatch that collects biometric data (heart rate, calories burned, driving time, sweat volume) during and after driving. A facial recognition camera and microphone collect the driver's emotional data (e.g., stress level, relaxation level).

[1262] Inputs: Driver's heart rate, calories burned, driving time, sweat volume, and emotion data from facial recognition camera and microphone.

[1263] Output: Collected biometric and emotional data

[1264] Step 2:

[1265] The device (smartwatch) transmits the collected data to the vehicle's central control unit (CCU) via Bluetooth.

[1266] Input: Biometric and emotional data from smartwatches

[1267] Output: Biometric and emotional data transmitted to the CCU

[1268] Step 3:

[1269] The CCU sends the data collected via the internet to an analysis server.

[1270] Input: Biometric and emotional data transmitted from the CCU.

[1271] Output: Biometric and emotional data sent to the analysis server.

[1272] Step 4:

[1273] The server (generating AI model) analyzes the received biometric and emotional data to identify nutrients the driver is lacking. This analysis is based on heart rate variability, calorie expenditure, driving intensity, sweat volume, and emotional state (e.g., stressful environment, relaxed state).

[1274] Input: Biometric data and emotional data

[1275] Output: Identified nutrient deficiencies (e.g., water, vitamin B, sodium)

[1276] Step 5:

[1277] The server (emotion recognition engine) analyzes the driver's emotional data and uses it to identify nutrients. For example, if stress is detected while driving, nutrients with relaxing effects will be prioritized.

[1278] Input: Sentiment data

[1279] Output: Corrected results for identified nutrients

[1280] Step 6:

[1281] Based on identified nutritional and emotional data, the server references the driver's location and selects the most suitable beverage from a database of nearby vending machines.

[1282] Input: Identified nutrients, emotional data, location information

[1283] Output: Information on the best beverage selected from nearby vending machines.

[1284] Step 7:

[1285] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the user of the beverage information through the infotainment system (IVI).

[1286] Input: Selected beverage information

[1287] Output: Beverage information displayed on the driver's IVI system (e.g., "We recommend beverage A, a relaxing drink available at the nearby service area.")

[1288] This will allow drivers to efficiently replenish necessary nutrients and select appropriate beverages according to their emotional state.

[1289] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1290] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1291] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1292] [Fourth Embodiment]

[1293] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1294] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1295] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1296] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1297] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1298] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1299] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1300] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1301] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1302] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1303] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1304] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1305] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1306] This invention is a system that collects biometric data, analyzes that data using generated AI, identifies deficient nutrients, and selects the most suitable beverage from nearby vending machines. This system is designed to allow users to efficiently replenish the necessary nutrients. The specific form is described below.

[1307] System Configuration

[1308] The system of the present invention consists of the following main components.

[1309] 1. Biometric data collection devices

[1310] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1311] 2. Data transmission terminal

[1312] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[1313] 3. Server for data analysis

[1314] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[1315] 4. Methods for selecting the optimal beverage

[1316] Server: Based on the analysis results, the generating AI uses user location information to access a database of nearby vending machines. This allows it to select the most suitable beverage from those offered by the vending machines.

[1317] 5. Means of notification

[1318] Server: Notifies the user's smartphone of the optimal beverage selection result.

[1319] Device: A smartphone notifies the user of the selected beverage information.

[1320] Program processing

[1321] The program for this system works as follows:

[1322] 1. User-driven data collection

[1323] User: Collects biometric data such as heart rate, calories burned, exercise time, and sweat volume using a smartwatch during and after exercise.

[1324] 2. Data transmission

[1325] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[1326] Terminal: A smartphone sends data to a server via the internet.

[1327] 3. Data analysis using generative AI

[1328] Server: The generating AI analyzes the received biometric data to identify nutrients that are deficient in the user's body. For example, the analysis might determine that there is a deficiency in water, B vitamins, sodium, etc.

[1329] 4. Selection of the optimal beverage

[1330] Server: The generating AI uses the user's location information to refer to a database of nearby vending machines and selects the most suitable beverage for nutritional replenishment from those offered.

[1331] 5. Notification to the user

[1332] Server: Sends selected beverage information to the user's smartphone.

[1333] Terminal: A smartphone notifies the user of beverage information, for example, displaying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[1334] Specific example

[1335] 1. User: Mr. Tanaka recorded his heart rate, calories burned, exercise time, and sweat volume using a smartwatch during his morning jog.

[1336] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[1337] 3. Server: The smartphone sent data to the server via the internet.

[1338] 4. Server: The generating AI analyzed Tanaka's data and determined that he was deficient in water and vitamin B.

[1339] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B.

[1340] 6. Server: The server sent information about "DrinkX" to Tanaka's smartphone.

[1341] 7. Device: The smartphone notified Ms. Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[1342] 8. User: Mr. Tanaka checked the notification and purchased "DrinkX" from a nearby vending machine.

[1343] In this way, users can effectively replenish nutrients that are lacking in their bodies, and it can also contribute to maintaining their physical condition after exercise.

[1344] The following describes the processing flow.

[1345] Step 1:

[1346] User: During and after exercise, use a smartwatch to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1347] Step 2:

[1348] Device: The smartwatch collects biometric data and transmits it to the user's smartphone via Bluetooth.

[1349] Step 3:

[1350] Device: The smartphone transmits biometric data to the server via the internet. If necessary, the user's location information is also transmitted at this time.

[1351] Step 4:

[1352] Server: The generating AI receives the transmitted biometric data and location information.

[1353] Step 5:

[1354] Server: The generating AI analyzes the received biometric data. This analysis identifies nutrients that are deficient in the user's body based on factors such as heart rate variability, calorie expenditure, exercise intensity, and sweat volume. For example, it may determine that the user is deficient in water, B vitamins, sodium, potassium, etc.

[1355] Step 6:

[1356] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[1357] Step 7:

[1358] Server: Compares the analysis results with the beverage information from the vending machine to select the optimal beverage for the user. For example, it might determine that "Sports Drink A" is optimal because it contains a lot of sodium and potassium.

[1359] Step 8:

[1360] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[1361] Step 9:

[1362] Terminal: The smartphone receives the selection information and notifies the user. For example, it displays a notification saying, "We recommend sports drink A, which can be purchased from a nearby vending machine."

[1363] Step 10:

[1364] User: Checks the notification and heads to a nearby vending machine.

[1365] Step 11:

[1366] User: Check the beverage displayed on the vending machine and purchase it.

[1367] (Example 1)

[1368] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1369] In today's busy lifestyle, it is difficult for users to efficiently and effectively replenish their body's nutrients. Especially after exercise, users need to quickly replenish deficient nutrients, but there are insufficient means to quickly identify which nutrients are lacking and to provide appropriate nutritional support. Furthermore, there is no system that selects and notifies users of the most suitable nutritional supplements from nearby vending machines, leaving users to gather information and make choices themselves.

[1370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1371] In this invention, the server includes means for generating AI to analyze collected data and identify nutrients that the user is lacking, means for selecting the optimal nutritional supplement item from multiple nutritional supplement items in nearby vending machines based on the identified nutrients, and means for notifying the user's communication terminal of the selected nutritional supplement item information. As a result, the user can have the optimal nutritional supplement item automatically selected after exercise and quickly replenish any deficient nutrients.

[1372] A "wearable device" is a computer device that a user can wear on their body and use to collect biometric data such as heart rate and calories burned.

[1373] A "communication terminal" is a device used to transmit biometric data to a computer equipped with AI, and primarily refers to mobile devices such as smartphones and tablets.

[1374] "Generative AI" is an artificial intelligence technology that analyzes collected biometric data to identify nutrients that the user is lacking.

[1375] "Nutrients" refer to components such as vitamins, minerals, and water that are needed in the user's body.

[1376] "Nutritional supplement items" are products such as beverages and foods offered in vending machines that are designed to replenish nutrients that are lacking in the user's body.

[1377] A "vending machine" is a machine that allows users to directly operate and purchase nutritional supplements such as beverages and food.

[1378] "Analysis" is the process of evaluating collected data, extracting information, and interpreting the data according to a specific purpose.

[1379] "Notification" refers to the act of informing the user about selected nutritional supplement items, and is primarily done using communication devices.

[1380] This invention is a system that collects a user's biometric data, analyzes that data to identify deficient nutrients, and selects the most suitable nutritional supplement item from a vending machine. The following describes a specific implementation of this system.

[1381] System components

[1382] 1. Wearable devices

[1383] Users wear wearable devices to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. These devices include heart rate monitors and accelerometers (e.g., smartwatches).

[1384] 2. Communication terminals

[1385] Wearable devices use communication methods such as Bluetooth to collect biometric data and transmit it to the user's smartphone. The smartphone then transmits the data via the internet to a server equipped with AI that generates data.

[1386] 3. Server equipped with generation AI

[1387] The server analyzes biometric data received via the internet. This server is equipped with a generating AI (e.g., GPT-4, a proprietary nutritional analysis model) that analyzes the user's data to identify any nutritional deficiencies.

[1388] 4. Nutrient Identification and Optimal Beverage Selection Methods

[1389] Based on the analysis results, the generating AI uses the user's location information to access a database of nearby vending machines. It then selects the most suitable nutritional supplement from the items offered in the vending machines.

[1390] 5. Means of notification

[1391] The server notifies the user's smartphone of the selected nutritional supplement items. The smartphone receives this information and displays it to the user.

[1392] Specific example

[1393] 1. User

[1394] User Tanaka uses a smartwatch to record his heart rate, calories burned, exercise time, and sweat volume during his morning jog.

[1395] Specific example: "Mr. Tanaka recorded his heart rate and calories burned using a smartwatch while jogging in the morning."

[1396] 2. Terminal

[1397] The wearable device collects data and sends it to Mr. Tanaka's smartphone. The smartphone then sends the data to a server via the internet.

[1398] Specific example: "After exercise, the smartwatch collected data and sent it to Mr. Tanaka's smartphone via Bluetooth. The smartphone then sent the data to a server via the internet."

[1399] 3. Server

[1400] The server uses AI to analyze Tanaka's data and determines that he is deficient in water and vitamin B. Then, it refers to data from nearby vending machines and selects "DrinkX," a sports drink rich in vitamin B.

[1401] Specific example: "The server analyzed Mr. Tanaka's biometric data and identified that he was deficient in water and vitamin B. It then consulted information from nearby vending machines and selected 'DrinkX,' a sports drink rich in vitamin B."

[1402] 4. Notification

[1403] The server sends information about "DrinkX" to Tanaka's smartphone, and the smartphone notifies Tanaka that "DrinkX is available for purchase from a nearby vending machine."

[1404] Specific example: "The server sent information about the selected DrinkX to Mr. Tanaka's smartphone, and the smartphone notified Mr. Tanaka, 'We recommend DrinkX that can be purchased from a nearby vending machine.'"

[1405] Example of a prompt

[1406] "We will design a system that collects data such as heart rate and calorie consumption using a smartwatch, analyzes this data with a generating AI to identify deficient nutrients, and then selects the most suitable beverage from a vending machine."

[1407] In this way, users can efficiently and effectively replenish deficient nutrients, which can also contribute to maintaining their physical condition after exercise.

[1408] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1409] Step 1:

[1410] Users wear wearable devices during and after exercise to collect biometric data (heart rate, calories burned, exercise time, and sweat volume).

[1411] Input: User biometric information during and after exercise

[1412] Output: Collected biometric data (heart rate, calories burned, exercise time, sweat volume)

[1413] Specific operation: The user starts jogging while wearing the wearable device, and the sensors record data such as heart rate and calories burned.

[1414] Step 2:

[1415] The device (wearable device) transmits collected biometric data to the user's smartphone via Bluetooth.

[1416] Input: Biometric data collected by wearable devices

[1417] Output: Biometric data sent to smartphone

[1418] Specific operation: After exercise, the wearable device automatically syncs with the smartphone and transfers data such as heart rate and calories burned.

[1419] Step 3:

[1420] The device (smartphone) transmits biometric data to the server via the internet.

[1421] Input: Biometric data stored on a smartphone

[1422] Output: Biometric data sent to the server

[1423] Specific operation: A dedicated smartphone application uploads biometric data to a server using Wi-Fi or mobile data communication.

[1424] Step 4:

[1425] The server uses generated AI to analyze biometric data and identify nutrients that are deficient in the user's body.

[1426] Input: Biometric data sent to the server

[1427] Output: Deficit nutrients identified through analysis

[1428] Specific operation: The generating AI cleans the data and analyzes data such as heart rate, calories burned, exercise time, and sweat volume to determine if there is a deficiency in water or vitamin B.

[1429] Step 5:

[1430] The server uses the user's location information to access a database of nearby vending machines and select the most suitable nutritional supplement item.

[1431] Input: Identified nutrient deficiencies and user location information

[1432] Output: Information on selected nutritional supplement items

[1433] Specific operation: The generating AI lists nearby vending machines based on the user's current location and selects an appropriate nutritional supplement item (e.g., the sports drink "DrinkX") from among them.

[1434] Step 6:

[1435] The server sends information about the selected nutritional supplement items to the user's smartphone.

[1436] Input: Information on selected nutritional supplement items

[1437] Output: Information about nutritional supplements sent to the smartphone

[1438] Specific operation: The server sends the selection information to the user's smartphone and displays it to the user via push notification.

[1439] Step 7:

[1440] The device (smartphone) notifies the user of information about the selected nutritional supplement item.

[1441] Input: Information about nutritional supplement items sent from the server.

[1442] Output: Information on nutritional supplement items notified to the user.

[1443] Specific action: The smartphone uses its push notification function to display a message to the user such as, "We recommend DrinkX, which is available for purchase at a nearby vending machine."

[1444] Step 8:

[1445] The user checks the notification and purchases the recommended nutritional supplement from a nearby vending machine.

[1446] Input: Notification information from smartphone

[1447] Output: Purchased nutritional supplements

[1448] Specific action: The user checks the notification, goes to a nearby vending machine, and purchases the selected nutritional supplement item (e.g., "DrinkX").

[1449] (Application Example 1)

[1450] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1451] Traditional nutritional supplementation methods required users to predict their own nutrient deficiencies and select appropriate foods and beverages, making effective and immediate nutritional supplementation difficult. As a result, users often spent considerable time and effort on proper nutrition. This was particularly problematic when rapid nutritional replenishment was needed, such as after exercise. Furthermore, there was a lack of systems that could suggest optimal foods based on biometric data, highlighting the need for highly accurate recommendations.

[1452] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1453] In this invention, the server includes a device for collecting biometric data, a terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients that the user is lacking, means for selecting the optimal meal from multiple meals from a partnered food delivery service based on the identified nutrients, and means for notifying the user's terminal of the selected meal information. This enables the user to quickly and efficiently replenish nutrients that are lacking after exercise or in daily life.

[1454] "During and after exercise" refers to the period during which a user is performing physical exercise and the subsequent recovery period.

[1455] "User biometric data" refers to information that indicates the physiological state of the human body, such as heart rate, calories burned, exercise time, and sweat volume.

[1456] A "device" is an instrument used to collect a user's biometric data, and includes smartwatches and exercise trackers.

[1457] A "terminal" is a device used to transmit collected data to a server, and includes the user's smartphone, for example.

[1458] "Generative AI" refers to artificial intelligence technology used to analyze a user's biometric data and identify deficient nutrients.

[1459] "Nutrient deficiencies" refer to nutrients such as vitamins, minerals, and water that are lacking in the user's body.

[1460] A "food delivery service" refers to a company or system that provides a service of delivering specified meals to a user's designated location.

[1461] "Meals" refers to dishes and food items containing nutrients necessary for the user's body, provided by partnered food delivery services.

[1462] "Selecting" refers to the process of determining the optimal foods and beverages based on the user's biometric data and nutritional deficiencies.

[1463] "Notify" refers to the action of informing the user about the selected food or beverages.

[1464] In this invention, the user first wears a device to collect biometric data during and after exercise. Specifically, devices such as smartwatches or exercise trackers are used. This records biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1465] The collected biometric data is transmitted to the user's smartphone using communication methods such as Bluetooth. The smartphone then transfers this data to a server via the internet. The server is equipped with a generative AI model that analyzes the received biometric data to identify nutrients that the user is lacking.

[1466] The generative AI model utilizes cutting-edge technologies such as OpenAI GPT-4 and includes advanced algorithms for analyzing biometric data. This model identifies specific nutrients that the user may be lacking, such as water, vitamins, and minerals.

[1467] Based on the nutritional information identified by the generating AI, the server consults the database of partner food delivery services to select the most suitable meal for the user. The user's location is also considered during the selection process to ensure the meal provides the quickest and most effective nutritional support.

[1468] The selected meal information is sent from the server to the smartphone. The smartphone uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the selected meal information. The notification includes "recommended meal sets and nutritionally balanced menus," which the user can then review.

[1469] Once a user checks the notification and selects a meal, it is delivered by a partnered food delivery service. This allows users to efficiently replenish any nutritional deficiencies they may have.

[1470] As a concrete example, a user records biometric data with a smartwatch while jogging in the morning, and this data is sent to a server via a smartphone. If the AI ​​analyzes the data and determines that the user is deficient in water and vitamin C, a food delivery service will suggest a meal rich in vitamin C (e.g., a spinach and orange salad) based on that information. Once the user selects the meal, it is delivered quickly.

[1471] The following prompts are used as input to the generative AI model:

[1472] "We have received the user's biometric data. Based on heart rate, calories burned, exercise time, and sweat volume, please analyze the nutrients that are lacking and suggest appropriate menu items from a food delivery service based on the user's current location."

[1473] This allows users to achieve a healthier lifestyle.

[1474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1475] Step 1:

[1476] The user wears a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume. The input is the user's biometric data, and the output is the recording of this data on the smartwatch.

[1477] Step 2:

[1478] A smartwatch transmits biometric data collected via Bluetooth to the user's smartphone. The input is the biometric data stored in the smartwatch, and the output is the data transferred to the smartphone. Specifically, data compression and transfer are performed using the Bluetooth communication function.

[1479] Step 3:

[1480] A smartphone transmits biometric data to a server via the internet. The input is the biometric data received by the smartphone, which is then transmitted to the server via the internet. The output is the biometric data stored on the server. Specifically, the data transfer is performed using the HTTPS protocol.

[1481] Step 4:

[1482] A generative AI model installed on the server analyzes the received biometric data to identify deficient nutrients. The input is biometric data stored on the server, and the generative AI processes the data to identify deficient nutrients such as water, vitamin C, and iron based on factors like heart rate and calorie expenditure. The output is a list of deficient nutrients.

[1483] Step 5:

[1484] The server selects the optimal meal by referencing the database of partner food delivery services based on information about missing nutrients. The input is a list of missing nutrients, and the system selects the best meal menu through a database search. The output is a list of nutritionally balanced meals. Specifically, it uses SQL queries to retrieve the necessary information from the database.

[1485] Step 6:

[1486] The server sends the selected meal information to the user's smartphone. The input is the selected meal information, which is sent to the user's smartphone using a push notification system (e.g., Firebase Cloud Messaging). The output is the meal information displayed on the user's smartphone.

[1487] Step 7:

[1488] The user checks a notification displayed on their smartphone and selects a suggested meal. The input is the meal information displayed on the smartphone, and the output is the information of the meal selected by the user. Specifically, confirmation and selection are performed using the smartphone's app interface.

[1489] Step 8:

[1490] A food delivery service picks up the selected meal and delivers it to the user's specified location. The input is information about the meal selected by the user, and the output is the meal delivered to the user. Specifically, order confirmation, cooking, and delivery are all handled using a delivery application.

[1491] These steps allow users to efficiently replenish deficient nutrients and achieve a healthy lifestyle.

[1492] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1493] This invention combines an emotion engine with a system that selects the optimal beverage from nearby vending machines by collecting biometric data, analyzing that data using generated AI, and identifying deficient nutrients. The emotion engine recognizes the user's emotions and incorporates that data into the identification of nutrients, enabling more personalized nutritional supplementation. The specific form is described below.

[1494] System Configuration

[1495] The system of the present invention consists of the following main components.

[1496] 1. Biometric data collection devices

[1497] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1498] 2. Data transmission terminal

[1499] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[1500] 3. Server for data analysis

[1501] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[1502] 4. Emotional Engine

[1503] The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine collects user emotion data using facial recognition and speech recognition technologies.

[1504] 5. Methods for selecting the optimal beverage

[1505] Server: The generating AI uses user location information and sentiment data to refer to a database of nearby vending machines and select the most suitable beverage from those offered.

[1506] 6. Means of notification

[1507] Server: Notifies the user's smartphone of the optimal beverage selection result.

[1508] Device: A smartphone notifies the user of the selected beverage information.

[1509] Program processing

[1510] The program for this system works as follows:

[1511] 1. User-driven data collection

[1512] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume, as well as emotional data using facial recognition and voice recognition technology.

[1513] 2. Data transmission

[1514] Device: The smartwatch collects data via Bluetooth and sends it to the user's smartphone.

[1515] Device: A smartphone transmits biometric and emotional data to a server via the internet.

[1516] 3. Data analysis using generative AI

[1517] Server: The generating AI analyzes the received biometric and emotional data to identify nutrients that are deficient in the user's body. This analysis determines deficiencies in water, B vitamins, sodium, etc., based on heart rate variability, calorie expenditure, exercise intensity, sweat volume, and the user's emotional state (e.g., under stress or in a relaxed state).

[1518] 4. Selection of the optimal beverage

[1519] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage.

[1520] Server: The server compares the analysis results with the beverage information from the vending machine to select the most suitable beverage for the user. For example, if the emotion engine recognizes the user's stress level, a beverage containing ingredients with relaxing effects will be selected.

[1521] 5. Notification to the user

[1522] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage and the location of the vending machine where it can be purchased.

[1523] Device: A smartphone notifies the user of beverage information, for example, displaying, "We recommend beverage A, which has a relaxing effect and can be purchased from a nearby vending machine."

[1524] Specific example

[1525] 1. User: Ms. Tanaka recorded her heart rate, calories burned, exercise time, and sweat volume using a smartwatch during her morning jog. She also collected her emotional data using facial recognition technology.

[1526] 2. Device: The smartwatch collected data and sent it to Mr. Tanaka's smartphone.

[1527] 3. Device: The smartphone sent data to the server via the internet.

[1528] 4. Server: The generating AI analyzed Tanaka's data and determined that she was deficient in water and vitamin B. Additionally, the emotion engine recognized that Tanaka was experiencing stress.

[1529] 5. Server: The generating AI referenced data from vending machines near Mr. Tanaka and selected "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[1530] 6. Server: The server sent information about "DrinkX" and "DrinkY" to Tanaka's smartphone.

[1531] 7. Device: The smartphone notified Ms. Tanaka, "We recommend DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect."

[1532] 8. User: Tanaka checked the notification and purchased "DrinkX" and "DrinkY" from a nearby vending machine.

[1533] In this way, users can effectively replenish nutrients that their bodies lack and maintain both physical and mental health by selecting the appropriate beverage according to their emotional state.

[1534] The following describes the processing flow.

[1535] Step 1:

[1536] User: During and after exercise, the smartwatch collects biometric data such as heart rate, calories burned, exercise time, and sweat volume. It also analyzes facial expressions using facial recognition technology and collects emotional data from voice using voice recognition technology.

[1537] Step 2:

[1538] Device: The smartwatch collects biometric and emotional data via Bluetooth and transmits it to the user's smartphone.

[1539] Step 3:

[1540] Device: A smartphone transmits biometric and emotional data to a server via the internet. Location information is also transmitted simultaneously.

[1541] Step 4:

[1542] Server: The generating AI receives the transmitted biometric data, emotional data, and location information.

[1543] Step 5:

[1544] Server: The generating AI analyzes the received biometric data. This analysis identifies nutrients (water, B vitamins, sodium, potassium, etc.) that are deficient in the user's body based on heart rate variability, calories burned, exercise intensity, sweat volume, etc.

[1545] Step 6:

[1546] Server: The server analyzes the emotional data received by the generating AI. For example, facial recognition technology determines the user's stress level and relaxation level from their facial expressions, while speech recognition technology determines their emotional state from the tone and speed of their voice.

[1547] Step 7:

[1548] Server: The generating AI comprehensively assesses the nutrients lacking in the user's body and their emotional state, and identifies the optimal nutrients and their combination.

[1549] Step 8:

[1550] Server: The generating AI uses the user's location information to access a database of nearby vending machines. The database contains nutritional information for each beverage and effects that depend on the user's emotional state.

[1551] Step 9:

[1552] Server: The server compares the analysis results with the beverage information from the vending machine to select the most suitable beverage for the user. For example, if the user's emotional state is stressed, a beverage with a relaxing effect will be selected.

[1553] Step 10:

[1554] Server: Sends selected beverage information to the user's smartphone. This information includes the name of the recommended beverage, its effects, and the location of the vending machine where it can be purchased.

[1555] Step 11:

[1556] Device: The smartphone receives the selection information and notifies the user. For example, a notification might appear saying, "We recommend beverage A, which has a relaxing effect and can be purchased from a nearby vending machine."

[1557] Step 12:

[1558] User: Checks the notification and heads to a nearby vending machine.

[1559] Step 13:

[1560] User: Check the beverage displayed on the vending machine and purchase it.

[1561] (Example 2)

[1562] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1563] Conventional nutritional supplementation systems identify nutrients and select the optimal beverage based solely on biometric data, failing to take into account the user's emotional state or mental health. Therefore, there is a need for a system that provides personalized nutritional support to achieve comprehensive health management.

[1564] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1565] In this invention, the server includes a device for collecting biometric data, means for collecting emotional data, means for transmitting the collected data to a terminal equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients and emotional states that the user is lacking, means for selecting the optimal beverage from multiple beverages in nearby vending machines based on the identified nutrients and emotional data, and means for notifying the user's terminal of the selected beverage information. This enables personalized nutritional supplementation according to the user's emotional state.

[1566] "During and after exercise" refers to the time period while a user is performing physical activity and immediately after that activity has finished.

[1567] "Biometric data" refers to information about the user's physical condition, such as heart rate, calories burned, exercise time, and sweat volume.

[1568] A "device" refers to equipment used to collect a user's biometric data, such as smartwatches and exercise trackers.

[1569] A "terminal" refers to a communication device used to transmit biometric and emotional data to a server, such as a smartphone or tablet.

[1570] "Generative AI" refers to artificial intelligence algorithms that analyze collected data to identify a user's nutritional and emotional state.

[1571] "Emotional data" refers to information about a user's emotional state, obtained using facial recognition or speech recognition technologies.

[1572] "Nearby vending machines" refers to beverage vending machines located near the user, based on the user's location information.

[1573] An "optimal beverage" refers to a beverage that provides the most suitable nutritional support for the user, based on identified nutrient and emotional data.

[1574] "Notifying" refers to the act of displaying or transmitting selected beverage information to the user's device.

[1575] This invention is a system that collects biometric and emotional data and analyzes the user's nutritional status using generative AI. Furthermore, it also has a function to select the most suitable beverage from nearby vending machines and notify the user. A specific embodiment of this system is described below.

[1576] Hardware configuration

[1577] This system consists of the following main components:

[1578] 1. Biometric data collection devices

[1579] User: During and after exercise, wear a smartwatch (e.g., a general exercise tracker) to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1580] 2. Data transmission terminal

[1581] Device: The smartwatch collects biometric data and transmits it to the user's smartphone using communication methods such as Bluetooth.

[1582] 3. Server for data analysis

[1583] Server: Receives biometric data transmitted from smartphones via the internet. This server is equipped with a generating AI that analyzes the received data to identify nutrients the user is lacking.

[1584] 4. Emotional Engine

[1585] The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine collects user emotion data using facial recognition and speech recognition technologies.

[1586] 5. Methods for selecting the optimal beverage

[1587] Server: The generating AI uses user location information and sentiment data to refer to a database of nearby vending machines and select the most suitable beverage from those offered.

[1588] 6. Means of notification

[1589] Server: Notifies the user's smartphone of the optimal beverage selection result.

[1590] Device: A smartphone notifies the user of the selected beverage information.

[1591] Software Configuration

[1592] 1. The biometric data collection device has an application installed that records heart rate, calories burned, exercise time, and sweat volume in real time.

[1593] 2. The data transmission terminal includes a communication application for sending biometric and emotional data to the server.

[1594] 3. The data analysis server is equipped with a generative AI model that analyzes the collected data, utilizing machine learning frameworks such as Python and TensorFlow.

[1595] 4. The emotion engine is built using face recognition libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[1596] Specific example

[1597] 1. User: Use a smartwatch during exercise to record heart rate, calories burned, exercise time, and sweat volume. After exercise, use your smartphone's camera to scan your face or add emotional data via voice input.

[1598] 2. Terminal: The smartwatch sends the data it collects via Bluetooth to the smartphone, and the smartphone sends the data to the server via the internet.

[1599] 3. Server: Analyzes the received data and identifies that the user is deficient in vitamin B. The emotion engine also determines that the user's emotional state indicates a high stress level.

[1600] 4. Server: Referencing a database of nearby vending machines, the generating AI selects "DrinkX," a sports drink rich in vitamin B, and "DrinkY," a relaxing herbal tea.

[1601] 5. Server: Sends the selected beverage information to the user's smartphone.

[1602] 6. Device: The smartphone notifies the user that "DrinkX, which can be purchased from a nearby vending machine, and DrinkY, which has a relaxing effect, are recommended."

[1603] This system allows users to effectively replenish nutrients that their bodies lack and select appropriate beverages according to their emotional state, thereby maintaining both physical and mental health.

[1604] Example of a prompt

[1605] "I'm looking for a relaxing drink. Could you tell me what I can buy from a nearby vending machine?"

[1606] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1607] Step 1:

[1608] User: Wear a smartwatch during and after exercise to collect biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1609] Input: User's physical condition during and after exercise.

[1610] Output: Biometric data such as heart rate, calories burned, exercise time, and sweat volume.

[1611] Specific operation: The smartwatch's sensors record this data in real time.

[1612] Step 2:

[1613] User: Uses facial recognition and speech recognition technologies to collect their own emotional data.

[1614] Input: User's facial expressions and voice.

[1615] Output: Emotional data (e.g., stress, relaxation, joy, etc.).

[1616] Specific operation: Uses the smartphone's camera and microphone to analyze facial expressions and voice and generate emotion data.

[1617] Step 3:

[1618] Device: The smartwatch collects biometric data via Bluetooth and transmits it to the user's smartphone.

[1619] Input: Biometric data stored on a smartwatch.

[1620] Output: Biometric data transferred to a smartphone.

[1621] Specific operation: Data is transferred from the smartwatch to the smartphone using a Bluetooth communication module.

[1622] Step 4:

[1623] Device: A smartphone transmits collected biometric and emotional data to a server via the internet.

[1624] Input: Biometric data and emotional data stored on a smartphone.

[1625] Output: Biometric and emotional data transferred to the server.

[1626] Specific operation: Data is sent to the server using an API. The data is typically sent in JSON format.

[1627] Step 5:

[1628] Server: Analyzes received biometric and emotional data to identify nutrients lacking in the user's body and their emotional state.

[1629] Input: Biometric and emotional data stored on the server.

[1630] Output: Identification of the user's nutritional deficiencies and emotional state.

[1631] Specific operation: The generating AI analyzes fluctuating data such as heart rate and calorie expenditure to identify deficiencies in vitamins B, sodium, water, etc. Simultaneously, the emotion engine analyzes emotional data to determine stress levels and relaxation levels.

[1632] Step 6:

[1633] Server: Based on the user's location information, it refers to a database of nearby vending machines and selects the most suitable beverage.

[1634] Input: Location information, biometric data analysis results, emotional data analysis results.

[1635] Output: Selection results for the optimal beverage.

[1636] Specific operation: The generating AI uses location information to search a database and identify beverages that are rich in vitamin B or have a relaxing effect.

[1637] Step 7:

[1638] Server: Notifies the user's smartphone of the selected optimal beverage information.

[1639] Input: Selection results for the optimal beverage.

[1640] Output: Beverage information sent to the smartphone.

[1641] Specific operation: The server sends the selection results to the smartphone in JSON format.

[1642] Step 8:

[1643] Device: A smartphone notifies the user of the selected beverage information.

[1644] Input: Beverage information sent from the server.

[1645] Output: Beverage information displayed to the user.

[1646] Specific action: Using the smartphone's notification function, display a message saying, "We recommend DrinkX, available from a nearby vending machine, and DrinkY, which has a relaxing effect."

[1647] This series of processing steps allows users to effectively replenish nutrients that their bodies are lacking and select the appropriate beverage according to their emotional state.

[1648] (Application Example 2)

[1649] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1650] Conventional technology lacked the means to identify nutrient deficiencies based on a driver's biometric data and select appropriate beverages. Furthermore, there was no technology to optimize nutritional supplementation in response to a driver's emotional state, making efficient health management difficult. This resulted in fatigue and decreased concentration during long drives, and could not be expected to improve safety.

[1651] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1652] In this invention, the server includes a device for collecting the driver's biometric data, a communication terminal for transmitting the collected data to a server equipped with a generating AI, means for the generating AI to analyze the collected data and identify nutrients the driver is lacking, an emotion recognition engine for collecting the driver's emotional data and reflecting it in the identification of nutrients, means for selecting the optimal beverage from multiple beverages in nearby vending machines based on the identified nutrients and emotional data, and means for notifying the driver's terminal of the selected beverage information. This makes it possible to monitor the driver's health status in real time and provide beverages that correspond to the necessary nutrients and emotions at the optimal time.

[1653] "Driver biometric data" refers to physical information such as the driver's heart rate, calories burned, driving time, and sweat volume. This data is used to understand the driver's health status.

[1654] "Generative AI" refers to artificial intelligence technology that analyzes collected biometric and emotional data and outputs optimal results tailored to a specific purpose.

[1655] An "emotion recognition engine" refers to technology that analyzes a driver's facial expressions and voice to identify their emotional state.

[1656] A "communication terminal" refers to a device, such as a smartwatch or a vehicle's central control unit, that transmits biometric data and emotional data to a server.

[1657] "Beverage selection method" refers to a method for selecting the most suitable beverage for the driver by referring to beverage information from nearby vending machines, based on identified nutritional and emotional data.

[1658] "Notification means" refers to a means of transmitting selected beverage information to the driver's terminal and informing the driver of that information.

[1659] A "vending machine" refers to an automatic vending machine for beverages, food, etc., that is installed in a location where drivers can easily use it.

[1660] This invention relates to a system for installation in autonomous vehicles that monitors the driver's health and emotional state in real time and suggests beverages to replenish necessary nutrients at the appropriate time. This system includes the following main components:

[1661] System Configuration

[1662] 1. Driver data acquisition device

[1663] The driver wears a smartwatch or other device to collect biometric data such as heart rate, calories burned, driving time, and sweat volume. Additionally, a facial recognition camera and microphone are installed inside the vehicle to collect the driver's emotional data. This allows for simultaneous monitoring of the driver's physical information and emotional state.

[1664] 2. Data transmission means

[1665] The smartwatch transmits data collected via Bluetooth to the vehicle's central control unit (CCU). The CCU then transmits the data to an analysis server via the internet. As a result, biometric and emotional data are aggregated on the server.

[1666] 3. Data analysis using generative AI

[1667] The server is equipped with a generative AI model. This generative AI model analyzes collected biometric and emotional data to identify nutrients that the driver is lacking. For example, it determines deficiencies in water, vitamins, sodium, etc., from heart rate variability, calorie expenditure, driving intensity, sweat volume, and the driver's emotional state.

[1668] 4. Emotion Recognition Engine

[1669] The server uses an emotion recognition engine to analyze the driver's emotional data and incorporate it into identifying nutrients. For example, it can detect stress and fatigue during driving and suggest beverages with relaxing effects.

[1670] 5. Methods for selecting the optimal beverage

[1671] The AI ​​generates recommendations and selects the most suitable beverage for the driver based on the analysis results. In doing so, it references a database of nearby vending machines based on the driver's location information and compares the nutritional information of the beverages offered. This allows the AI ​​to select the optimal beverage for the driver.

[1672] 6. Means of notification

[1673] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the driver. The autonomous vehicle's infotainment system (IVI) displays to the driver, "We recommend beverage A, which has a relaxing effect and can be purchased at a nearby service area."

[1674] Specific example

[1675] 1. User: A typical driver will use a smartwatch and in-car camera to collect heart rate, calories burned, driving time, sweat volume, and emotional data while driving long distances.

[1676] 2. Terminal: The smartwatch transmits data to the CCU via Bluetooth, and the CCU transmits the data to the server via the internet.

[1677] 3. Server: The generation AI model analyzes the data and identifies deficient nutrients (e.g., water and vitamin B). Additionally, the emotion recognition engine recognizes the driver's stress level.

[1678] 4. Server: The generating AI references data from vending machines near the driver and selects beverages that are rich in vitamin B and have stress-reducing effects.

[1679] 5. Server: The server transmits the selected beverage information to the CCU, which then displays the "recommended beverage" via the IVI system.

[1680] Example of a prompt

[1681] "Please analyze the following biometric and emotional data to identify any nutrient deficiencies: Heart rate: [data], Calories burned: [data], Sweat volume: [data]. Facial recognition result: Stress level."

[1682] This allows drivers to efficiently replenish nutrients and select appropriate beverages according to their emotional state, thereby improving safety and comfort during long-distance driving.

[1683] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1684] Step 1:

[1685] The user wears a smartwatch that collects biometric data (heart rate, calories burned, driving time, sweat volume) during and after driving. A facial recognition camera and microphone collect the driver's emotional data (e.g., stress level, relaxation level).

[1686] Inputs: Driver's heart rate, calories burned, driving time, sweat volume, and emotion data from facial recognition camera and microphone.

[1687] Output: Collected biometric and emotional data

[1688] Step 2:

[1689] The device (smartwatch) transmits the collected data to the vehicle's central control unit (CCU) via Bluetooth.

[1690] Input: Biometric and emotional data from smartwatches

[1691] Output: Biometric and emotional data transmitted to the CCU

[1692] Step 3:

[1693] The CCU sends the data collected via the internet to an analysis server.

[1694] Input: Biometric and emotional data transmitted from the CCU.

[1695] Output: Biometric and emotional data sent to the analysis server.

[1696] Step 4:

[1697] The server (generating AI model) analyzes the received biometric and emotional data to identify nutrients the driver is lacking. This analysis is based on heart rate variability, calorie expenditure, driving intensity, sweat volume, and emotional state (e.g., stressful environment, relaxed state).

[1698] Input: Biometric data and emotional data

[1699] Output: Identified nutrient deficiencies (e.g., water, vitamin B, sodium)

[1700] Step 5:

[1701] The server (emotion recognition engine) analyzes the driver's emotional data and uses it to identify nutrients. For example, if stress is detected while driving, nutrients with relaxing effects will be prioritized.

[1702] Input: Sentiment data

[1703] Output: Corrected results for identified nutrients

[1704] Step 6:

[1705] Based on identified nutritional and emotional data, the server references the driver's location and selects the most suitable beverage from a database of nearby vending machines.

[1706] Input: Identified nutrients, emotional data, location information

[1707] Output: Information on the best beverage selected from nearby vending machines.

[1708] Step 7:

[1709] The server transmits the selected beverage information to the vehicle's CCU, which then notifies the user of the beverage information through the infotainment system (IVI).

[1710] Input: Selected beverage information

[1711] Output: Beverage information displayed on the driver's IVI system (e.g., "We recommend beverage A, a relaxing drink available at the nearby service area.")

[1712] This will allow drivers to efficiently replenish necessary nutrients and select appropriate beverages according to their emotional state.

[1713] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1714] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1715] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1716] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1717] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1718] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1719] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1720] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1721] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1722] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1723] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1724] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1725] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1726] 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.

[1727] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1728] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1729] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1730] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1731] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1732] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1733] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1734] The following is further disclosed regarding the embodiments described above.

[1735] (Claim 1)

[1736] A device for collecting the user's biometric data during and after exercise,

[1737] A terminal for sending collected data to a server equipped with AI for generating data,

[1738] The generating AI analyzes the collected data and identifies nutrients that the user is lacking,

[1739] A means for selecting the optimal beverage from multiple beverages in nearby vending machines based on identified nutrients,

[1740] A means of notifying the user's terminal of the selected beverage information,

[1741] A system that includes this.

[1742] (Claim 2)

[1743] The system according to claim 1, further comprising means for collecting heart rate, calories burned, exercise time, and sweat volume as biometric data.

[1744] (Claim 3)

[1745] The system according to claim 1, further comprising means for the generating AI to acquire the user's location information and refer to information on nearby vending machines based on the location information.

[1746] "Example 1"

[1747] (Claim 1)

[1748] A wearable device for collecting the user's biometric data during and after exercise,

[1749] A communication terminal for transmitting collected data to a computer equipped with AI for generating data,

[1750] The generating AI analyzes the collected data and identifies nutrients that the user is lacking,

[1751] A method for selecting the optimal nutritional supplement item from multiple nutritional supplement items in nearby vending machines based on identified nutrients,

[1752] A means of notifying the user's communication terminal of the selected nutritional supplement item information,

[1753] A system that includes this.

[1754] (Claim 2)

[1755] The system according to claim 1, further comprising means for collecting heart rate, calories burned, exercise time, and sweat volume as biometric data.

[1756] (Claim 3)

[1757] The system according to claim 1, further comprising means for the generating AI to acquire the user's location information and refer to information on nearby vending machines based on the location information.

[1758] "Application Example 1"

[1759] (Claim 1)

[1760] A device for collecting the user's biometric data during and after exercise,

[1761] A terminal for sending collected data to a server equipped with AI for generating data,

[1762] The generating AI analyzes the collected data and identifies nutrients that the user is lacking,

[1763] A method for selecting the optimal meal from multiple meals offered by partner food delivery services based on identified nutrients,

[1764] A means of notifying the user's terminal of the selected meal information,

[1765] A system that includes this.

[1766] (Claim 2)

[1767] The system according to claim 1, further comprising means for collecting heart rate, calories burned, exercise time, and sweat volume as biometric data.

[1768] (Claim 3)

[1769] The system according to claim 1, further comprising means for generating AI to acquire the user's location information and referencing information on nearby food delivery services based on the location information.

[1770] "Example 2 of combining an emotion engine"

[1771] (Claim 1)

[1772] A device for collecting the user's biometric data during and after exercise,

[1773] A terminal for sending collected data to a server equipped with AI for generating data,

[1774] The generating AI analyzes the collected data and identifies nutrients that the user is lacking,

[1775] A means of collecting user sentiment data and reflecting that data in the analysis results,

[1776] A means for selecting the optimal beverage from multiple beverages in nearby vending machines based on identified nutrient and emotional data,

[1777] A means of notifying the user's terminal of the selected beverage information,

[1778] A system that includes this.

[1779] (Claim 2)

[1780] The system according to claim 1, further comprising means for collecting heart rate, calories burned, exercise time, and sweat volume as biometric data.

[1781] (Claim 3)

[1782] The system according to claim 1, further comprising means for the generating AI to acquire the user's location information and refer to information on nearby vending machines based on the location information.

[1783] "Application example 2 when combining with an emotional engine"

[1784] (Claim 1)

[1785] A device for collecting the user's biometric data during and after exercise,

[1786] A communication terminal for sending collected data to a server equipped with AI for generating data,

[1787] The generating AI analyzes the collected data and identifies nutrients that the user is lacking,

[1788] An emotion recognition engine that collects user emotion data and uses it to identify nutrients,

[1789] A means for selecting the optimal beverage from multiple beverages in nearby vending machines based on identified nutrient and emotional data,

[1790] A means of notifying the user's terminal of the selected beverage information,

[1791] A system that includes this.

[1792] (Claim 2)

[1793] The system according to claim 1, further comprising means for collecting heart rate, calories burned, exercise time, and sweat volume as biometric data.

[1794] (Claim 3)

[1795] The system according to claim 1, further comprising means for the generating AI to acquire the user's location information and refer to information on nearby vending machines based on the location information. [Explanation of Symbols]

[1796] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for collecting the user's biometric data during and after exercise, A terminal for sending collected data to a server equipped with AI for generating data, The generating AI analyzes the collected data and identifies nutrients that the user is lacking, A means for selecting the optimal beverage from multiple beverages in nearby vending machines based on identified nutrients, A means of notifying the user's terminal of the selected beverage information, A system that includes this.

2. The system according to claim 1, further comprising means for collecting heart rate, calories burned, exercise time, and sweat volume as biometric data.

3. The system according to claim 1, further comprising means for the generating AI to acquire the user's location information and refer to information on nearby vending machines based on the location information.

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