system

Abstract

Provide a system. [Solution] an input means for a user to input information about a type of drink or a meal; a transmitting means for transmitting the input information to the processing device; suggestion means for receiving information input by the processing device and suggesting beverage and meal combinations; a display means for displaying the proposal result generated by the proposal means to the user; A system including:

Description

[Technical Field]

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

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

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

[0004] Traditionally, selecting the optimal drink and food pairing (marriage) required specialized knowledge and experience, making it difficult for the average consumer. Furthermore, selecting the optimal pairing from the wide variety of drink and food options required time and effort, making it inconvenient. Furthermore, it was difficult to obtain suggestions tailored to individual tastes, and consumers often relied on general guidelines. These issues limited opportunities to enjoy drink and food pairings. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including an input means for a user to input information about a beverage type or a meal, a transmission means for transmitting the input information to a processing device, a suggestion means for receiving the input information at the processing device and suggesting beverage and meal pairings, and a display means for displaying the suggestion results generated by the suggestion means to the user. Furthermore, the suggestion means uses an AI model for calculating optimal meal pairings based on the input beverage type, and also uses an AI model for calculating optimal beverage types based on the input meal information, thereby achieving highly convenient suggestions for the user. This allows even average users to easily find appropriate beverage and meal pairings, thereby increasing opportunities to enjoy beverage and meal pairings.

[0006] "Input means" refers to a device or interface for a user to input drink type or meal information.

[0007] "Transmitting means" refers to a communication circuit or software configuration for transmitting input information to a processing device.

[0008] The "processing device" refers to a computer or server that receives information sent by a user and generates optimal proposals based on that information.

[0009] "Recommendation means" refers to logic circuitry or software for calculating the optimal combination of beverages and food based on the received information and generating the results.

[0010] The "display means" refers to a display or interface that displays the proposal results generated by the proposal means to the user.

[0011] "AI model" refers to an algorithm or machine learning model that uses artificial intelligence technology to suggest optimal drink and food pairings. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] The present invention provides a system that allows a user to input information about a type of beverage or a meal and, based on that information, suggests optimal pairings (marriage). The system includes an input unit, a transmission unit, a suggestion unit, and a display unit.

[0034] User Operation

[0035] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[0036] Sending data

[0037] Next, the terminal transmits the input data to the server, and the transmission means converts the input information into JSON format and transmits it to the server via the network.

[0038] Data Receipt and Processing

[0039] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine," the AI ​​model will suggest that "steak" is the best pairing. This calculation utilizes past data and trained algorithms.

[0040] Displaying the results

[0041] After the server generates the calculation result, it sends it back to the device. The device analyzes the result and displays it to the user. For example, it may display the result in the form of "Suggested combination: Steak." This display allows the user to easily find the best drink and food combination.

[0042] Specific examples

[0043] 1. The user enters "red wine" on the terminal and submits the form.

[0044] 2. The device sends the input data to the server.

[0045] 3. The server accepts the data and processes it using the AI ​​model.

[0046] 4. The server generates the result "The best food to go with red wine is steak."

[0047] 5. The server sends the result back to the terminal, which displays "Suggested combination: steak" to the user.

[0048] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user opens a form on the terminal and enters information about a type of drink or meal, for example, "red wine" or "steak."

[0052] Step 2:

[0053] The terminal obtains the information entered by the user and stores it as a data structure (e.g., a JavaScript object).

[0054] Step 3:

[0055] The device serializes the stored data into JSON format and prepares it for transmission, specifically creating a JSON object containing the input drink or meal name.

[0056] Step 4:

[0057] The JSON data generated by the device is sent to the server via the network using an HTTP request.

[0058] Step 5:

[0059] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[0060] Step 6:

[0061] The server analyzes the acquired JSON data and extracts information about the type of drink and meal.

[0062] Step 7:

[0063] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal pairing. Specifically, the AI ​​model calculates the optimal food pairing for each type of beverage.

[0064] Step 8:

[0065] The server composes the proposal results obtained from the AI ​​model in JSON format and prepares them as a response.

[0066] Step 9:

[0067] The server sends the constructed JSON response to the device.

[0068] Step 10:

[0069] The device parses the JSON response received from the server and extracts the proposed combination information.

[0070] Step 11:

[0071] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" on the screen.

[0072] Step 12:

[0073] The user checks the suggested results and enjoys the optimal drink and meal combination.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] Systems that suggest optimal drink and food pairings (marriage) have traditionally required specialized knowledge and have been difficult for average users to use. Furthermore, there has been a lack of technological solutions that can effectively process input data and make prompt and accurate recommendations. For this reason, there has been a demand for a system that allows users to easily enjoy optimal drink and food pairings.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes an input device for a user to input information about the type of drink or meal, a transmission device for transmitting the input information to the server, a processing device that receives the input information at the server and calculates the optimal drink and meal combination using an AI model, and a display device that displays the proposal results generated by the processing device to the user. This makes it possible to calculate the optimal drink and meal combination using the AI ​​model and provide the result to the user quickly and accurately.

[0079] An "input device" is a device that allows a user to input information about a type of drink or a meal.

[0080] The "transmitting device" is a device for transmitting information input by a terminal to a server.

[0081] A "server" is a computer system that performs processing based on received information, generates results, and returns them.

[0082] "Processing device" means a device that receives data entered into the server, processes the data using an AI model, and calculates the optimal combination of beverages and meals.

[0083] An "AI model" is an algorithm or program that uses past data and trained algorithms to calculate the optimal combination based on input beverage and meal information.

[0084] A "display device" is a device or interface for visually displaying to a user the suggestion results generated by the processing device.

[0085] The present invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input device, a transmission device, a processing device, and a display device. Specific embodiments of this system are described below.

[0086] Hardware and Software Configuration

[0087] Users access the form through a web browser on a device (e.g., a PC, smartphone, or tablet) and the form is created using HTML, CSS, and JavaScript and is displayed on the device.

[0088] Input Devices

[0089] The user inputs information about the type of drink or meal, such as "red wine" or "steak," into the form. The input device is a keyboard or a touch screen.

[0090] Transmitting device

[0091] The terminal uses JavaScript to convert the input data into JSON format, which allows the data to be structured and efficiently transmitted over the network, and then sends the converted JSON data to the server as an HTTP POST request using AJAX technology.

[0092] Processing equipment

[0093] The server uses a backend framework (e.g., PHP, Node.js) to receive data sent from the device. The received data is parsed using Python libraries. The parsed data is then fed into a generative AI model to calculate the optimal drink and food pairing. The AI ​​model performs its calculations using historical data and trained algorithms.

[0094] display device

[0095] After the server generates the calculation results, it sends them back to the device as a JSON-formatted response. The device uses an AJAX response handler to parse the result data received from the server. The parsed data is visually displayed to the user using HTML and CSS. Specifically, it is displayed on the screen in the form of "Suggested combination: steak."

[0096] Specific examples

[0097] 1. User action: The user enters "red wine" on the terminal and submits the form.

[0098] 2. Sending data: The device converts the input data into JSON format and sends it to the server as an HTTP POST request.

[0099] 3. Data reception and processing: The server receives the JSON data and passes it to the AI ​​model using a Python library. The AI ​​model uses past data and algorithms to calculate the best food for red wine: steak.

[0100] 4. Generate and send results: The server sends the results obtained from the AI ​​model back to the device as a JSON response.

[0101] 5. Display the result: The device uses the AJAX response handler to parse the JSON data and displays "Suggested combination: steak" on the screen.

[0102] Example prompts for generative AI models

[0103] User entered "red wine". Please suggest the best meal.

[0104] In this way, the present invention can realize a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[0105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0106] Step 1:

[0107] User action: The user opens a browser on their device and accesses the specified URL to display the form.

[0108] What happens: The user enters drink or meal information into the form fields and clicks the submit button.

[0109] Input: The data a user enters into a form (e.g., "red wine" or "steak")

[0110] Output: The data entered by the user is ready to be sent.

[0111] Step 2:

[0112] Terminal operation: The terminal takes the user's input data and converts it into JSON format for transmission.

[0113] Specific behavior: Uses JavaScript to get input data and generate it as a JSON object.

[0114] Input: Data entered by the user (e.g., "red wine")

[0115] Output: Data converted to JSON format (e.g. {'drink': 'red wine'})

[0116] Step 3:

[0117] Terminal operation: Data converted to JSON format is sent to the server.

[0118] What it does: It uses AJAX techniques to send data to the server as an HTTP POST request.

[0119] Input: JSON format data (e.g. {'drink': 'red wine'})

[0120] Output: Input data sent to the server

[0121] Step 4:

[0122] Server operation: The server receives the JSON data sent from the device.

[0123] What it does: Receives requests using a backend framework (e.g., PHP, Node.js).

[0124] Input: JSON data sent from the terminal (e.g., {'drink': 'red wine'})

[0125] Output: The state in which the received data is prepared for analysis

[0126] Step 5:

[0127] Server operation: The server analyzes the received data and passes it to the AI ​​model.

[0128] What it does: Uses Python libraries to parse data and feed it into an AI model.

[0129] Input: Received JSON data (e.g. {'drink': 'red wine'})

[0130] Output: Data input to the AI ​​model (e.g., data after format conversion)

[0131] Step 6:

[0132] Server operation: The AI ​​model calculates the optimal combination based on the input drink and meal information.

[0133] What it does: A generative AI model uses past data and trained algorithms to make calculations.

[0134] Input: Data fed into the AI ​​model (e.g., "red wine")

[0135] Output: Calculation result (e.g. "steak")

[0136] Step 7:

[0137] Server operation: The server generates a response based on the calculation results obtained from the AI ​​model and formats it in JSON format.

[0138] Specific behavior: Converts the calculation result into a JSON object.

[0139] Input: The calculation result from the AI ​​model (e.g., "steak")

[0140] Output: Response data in JSON format (e.g., {'suggestion': 'steak'})

[0141] Step 8:

[0142] Server operation: Returns the formatted data to the terminal.

[0143] Specific behavior: Sends JSON data as an HTTP response.

[0144] Input: JSON format response data (e.g., {'suggestion': 'steak'})

[0145] Output: Data sent to the terminal

[0146] Step 9:

[0147] Terminal operation: The terminal analyzes the result data received from the server.

[0148] Specific behavior: Receives and parses JSON data using the AJAX response handler.

[0149] Input: JSON data returned from the server (e.g., {'suggestion': 'steak'})

[0150] Output: Parsed data (e.g. "steak")

[0151] Step 10:

[0152] Terminal operation: The analyzed data is visually displayed to the user.

[0153] What it does: Displays the results on the screen using HTML and CSS.

[0154] Input: Parsed data (e.g. "steak")

[0155] Output: The suggested result displayed to the user (e.g., "Suggested combination: steak")

[0156] This process allows the user to easily find the best drink and meal combination.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] In modern society, optimal drink and food pairings (marriage) are an important factor in enhancing dining experiences. However, it is difficult for individual users to determine the optimal drink and food pairing, requiring a great deal of time and knowledge. Furthermore, restaurants and food delivery services require a high level of specialized knowledge to provide satisfactory suggestions to users. This invention aims to solve these problems and provide a system that allows users to easily find the optimal pairing.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes means for converting user input into JSON format and sending it to the server via a network, means for the server to perform calculations based on the received data and return the optimal combination to the user's smartphone, and means for displaying the generated recommendation results on the user's smartphone and enabling the user to order the recommended drink and meal all at once. This enables users to quickly find the optimal drink and meal combination and easily order without specialized knowledge.

[0162] The "input means" is a means for the user to input information about the type of drink or meal.

[0163] The "transmitting means" is a means for transmitting input information to the processing device.

[0164] The "suggestion means" is a means for receiving information inputted into the processing device and proposing combinations of drinks and meals.

[0165] The "display means" is a means for displaying the proposal results generated by the proposal means to the user.

[0166] An "artificial intelligence model" is a model for calculating optimal beverage and food pairings, using data and algorithms to make predictions and recommendations.

[0167] The "JSON format" is a lightweight data exchange format that expresses data in text format and exchanges structured data.

[0168] A "network" is a collection of communication means for exchanging information, including the Internet and local area networks (LANs).

[0169] A "smartphone" is a portable communication terminal that has not only telephone functions but also the ability to connect to the Internet and run various applications.

[0170] MODE FOR CARRYING OUT THE INVENTION

[0171] This invention is a system that, when a user inputs information about the type of drink and the food, suggests the optimal combination (marriage) based on that information. The specific configuration and processing of this system are described below.

[0172] System configuration

[0173] The system consists of the following main components:

[0174] 1. Input means: A means for the user to input drink type or meal information. This may include a smartphone app or web interface.

[0175] 2. Transmission means: A means for transmitting the input information to the server. Here, the data is converted into JSON format and sent to the server via the Internet.

[0176] 3. Recommendation means: A means for suggesting optimal drink and meal combinations based on the data received by the server. Here, an artificial intelligence model is used.

[0177] 4. Display means: A means for displaying the proposed results to the user. A smartphone or other display device is used.

[0178] Processing flow

[0179] 1. User Input: The user opens the smartphone app and inputs information about their preferred beverage or food, for example, "red wine."

[0180] 2. Data transmission: The entered information is converted into JSON format and sent to the server via the network.

[0181] 3. Processing on the server: The server analyzes the received data and uses an artificial intelligence model to calculate the best food or drink pairing. For example, it might suggest "steak" as the best food to pair with red wine.

[0182] 4. Returning the results: The server converts the calculation results into JSON format and returns them to the user's smartphone.

[0183] 5. Displaying the results: The user's smartphone analyzes the received data and displays the results on the screen, for example, "Suggested combination: Steak."

[0184] Hardware and software used

[0185] Smartphone: A device for user input and display of results.

[0186] Server: Receives and processes data, often using a web framework such as Flask.

[0187] Artificial intelligence model: A model for calculating optimal combinations. It is built using libraries such as TENSORFLOW (registered trademark) and PyTorch.

[0188] JSON format: A data interchange format.

[0189] Specific examples

[0190] 1. The user enters "red wine" into the smartphone app and presses the send button.

[0191] 2. The data is sent to the server, which calculates the best meal to match the "red wine."

[0192] 3. The server returns the calculation result, "steak."

[0193] 4. The user's smartphone will display "Suggested combination: Steak."

[0194] Prompt Sentence Examples

[0195] Please suggest the best food pairings for the following beverages:

[0196] Drink: Red wine

[0197] This invention allows users to easily enjoy optimal drink and food pairings without any specialized knowledge.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] The user inputs information about the type of drink or meal through the input means of the terminal. For example, the user inputs the type of drink, "red wine." At this time, the user's input is entered into the application form, and the application is ready for the next process.

[0201] Input: User enters "red wine"

[0202] Output: User input data (e.g. red wine)

[0203] Step 2:

[0204] The device converts the input information into JSON format and sends it to the server via the network. Specifically, the sending means sends an HTTP POST request to the URL endpoint, passing the user's input data to the server.

[0205] Input: User-entered data (e.g., red wine)

[0206] Output: JSON format data (e.g. {"drink": "red wine"})

[0207] Step 3:

[0208] The server parses the JSON data received over the network and converts it into an internal data structure. Based on this data, an artificial intelligence model calculates the optimal meal combination. Here, data calculations are performed using a generative AI model.

[0209] Input: JSON format data (e.g., {"drink": "red wine"})

[0210] Output: Optimal food combination (e.g. steak)

[0211] Step 4:

[0212] The server converts the calculated optimal combination into JSON format and returns it to the device as an HTTP response. The server generates data containing the proposal results and stores it in the response body.

[0213] Input: Optimal food pairing (e.g. steak)

[0214] Output: Response data in JSON format (e.g. {"meal": "steak"})

[0215] Step 5:

[0216] The device parses the JSON format data received from the server and converts it into a format that can be visually presented to the user. Specifically, the data is passed to a UI component that displays the results, and the results are displayed on the user's screen.

[0217] Input: JSON format response data (e.g. {"meal": "steak"})

[0218] Output: User-visible suggestion results (e.g., "Suggested combination: steak")

[0219] Specific examples of operation

[0220] Example prompt (text format):

[0221] Please suggest the best food pairings for the following beverages:

[0222] Drink: Red wine

[0223] In this way, having clear inputs and outputs for each step makes it easier to understand the processing flow of the entire system, and enables the construction of a system that responds quickly and accurately to user requests.

[0224] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0225] The present invention is a system that allows a user to input information about a type of beverage or a meal and then suggests optimal pairings (marriage) based on that information. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[0226] User Operation

[0227] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[0228] Emotion analysis using an emotion engine

[0229] Before the input data is sent, the device uses an emotion engine to analyze the user's input to determine whether they are in a "positive" or "negative" mood, for example, by analyzing the entered text and the user's facial expressions.

[0230] Sending data

[0231] Next, the terminal transmits the user's input data and the result of the emotion analysis to the server. The transmission means converts the input information and the result of the emotion analysis into JSON format and transmits them to the server via the network.

[0232] Data Receipt and Processing

[0233] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine" and the user's emotion is "positive," the AI ​​model will suggest that "steak" is the optimal pairing. This calculation utilizes past data and a trained algorithm.

[0234] Adjusting and displaying results

[0235] After the server generates the calculation results, it adjusts the recommendations accordingly based on the results of the sentiment analysis. For example, if the user is in a negative mood, it might recommend a combination of red wine and chocolate. The adjusted results are then sent back to the device.

[0236] Specific examples

[0237] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[0238] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's mood is "positive."

[0239] 3. The device sends the input data and the results of emotion analysis to the server.

[0240] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[0241] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[0242] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[0243] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a way that is easy for even general users to use. By combining it with an emotion engine, more personalized suggestions that match the user's mood become possible, improving the user experience.

[0244] The processing flow will be explained below.

[0245] Step 1:

[0246] The user opens a form on the terminal and enters the type of drink or meal information, for example, "red wine" or "steak."

[0247] Step 2:

[0248] The user clicks the form submit button, which causes the device to temporarily store the entered data.

[0249] Step 3:

[0250] The emotion engine analyzes the user's emotions based on the data stored on the device. The emotion engine uses text input and facial recognition data to determine the user's emotional state (positive, negative, etc.).

[0251] Step 4:

[0252] The device combines the user's sentiment analysis results and input data into a single JSON object, which includes the drink type, meal information, and emotional state.

[0253] Step 5:

[0254] The JSON object generated by the terminal is sent to the server using an HTTP request.

[0255] Step 6:

[0256] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[0257] Step 7:

[0258] The server analyzes the JSON data it receives and extracts the type of drink, meal information, and the user's emotional state.

[0259] Step 8:

[0260] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal food pairing. Specifically, the AI ​​model calculates the optimal food pairing taking into account the type of beverage and the user's emotional state.

[0261] Step 9:

[0262] The server composes the recommendation results obtained from the AI ​​model in JSON format and prepares them as a response. For example, if the emotional state is "positive," the recommendation result will be "steak," and if the emotional state is "negative," the recommendation result will be "chocolate."

[0263] Step 10:

[0264] The server sends the constructed JSON response to the device.

[0265] Step 11:

[0266] The device parses the JSON response received from the server and extracts the proposed combination information.

[0267] Step 12:

[0268] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" or "Suggested combination: chocolate" on the screen.

[0269] Step 13:

[0270] The user checks the suggested results and enjoys the optimal drink and meal combination.

[0271] Example 2

[0272] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0273] Conventional systems that suggest drink and food pairings have the problem of not taking into account the user's emotional state, and therefore not providing suggestions that match the user's mood. This can lead to low user satisfaction and convenience. Furthermore, the level of personalization of suggestions is low, and necessary information may not be provided appropriately.

[0274] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input information about the type of drink or meal, an emotion analysis means for analyzing the input information and the user's facial expression data using an emotion engine to identify the user's emotional state, and a transmission means for transmitting the analyzed emotional state and the input information to the processing device. This makes it possible to propose optimal combinations of drinks and meals that take the user's emotional state into consideration.

[0275] The "input means" is a means for the user to input information about the type of drink or meal.

[0276] The "emotion analysis means" is a means for analyzing input information and the user's facial expression data to identify the user's emotional state.

[0277] The "transmitting means" is a means for transmitting the analyzed emotional state and input information to the processing device.

[0278] The "suggestion means" is a means for receiving the emotional state analyzed by the processing device and input information, and proposing a combination of drink and food.

[0279] The "result adjustment means" is a means for adjusting the result of the suggestion means based on the emotional state.

[0280] The "display means" is a means for displaying the adjusted proposal results to the user.

[0281] An "AI model" is a model that uses machine learning algorithms to analyze data and calculate optimal drink and meal combinations.

[0282] An "emotion engine" is software or algorithms that analyze a user's facial expressions and text data to identify their emotional state.

[0283] "Processing device" means a device that receives user input data and sentiment analysis results and calculates and adjusts beverage and food pairings using an AI model.

[0284] A "network protocol" is a communication protocol for sending and receiving data, and is used to establish communication between a terminal and a server.

[0285] The present invention provides a system for suggesting optimal pairings (marriage) based on information about drink types or meals input by a user. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[0286] First, the user enters information about the type of drink or meal using a form displayed on the device. For example, they can enter information such as "red wine" or "steak." HTML and JavaScript are used to input information into the form.

[0287] Next, the device analyzes the input information and the user's facial expression using an emotion engine. The emotion engine uses facial recognition APIs such as OpenFace and Affectiva to determine the user's emotional state. For example, if the user is expressing positive emotions, that information will be included in the analysis results.

[0288] The analyzed emotional state and input information are converted to JSON format and sent to the server using a network protocol (e.g., HTTP POST), typically using the JavaScript fetch API or XMLHttpRequest.

[0289] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. The AI ​​model uses TensorFlow and PyTorch, and performs calculations using past data and trained algorithms. The calculation results may be a recommendation such as "The best food to pair red wine with is steak."

[0290] Furthermore, the server adjusts the recommendation results based on the user's emotional state. For example, if the user is in a negative emotional state, the server will adjust the pairings based on the user's emotional state, such as red wine and chocolate. This adjustment is achieved using an emotion-based content filtering algorithm.

[0291] The adjusted results are converted back into JSON format and sent over the network to the device. The device parses the received results and displays them in an appropriate format for the user. For example, it displays "Suggested combination: Steak" on the screen using HTML and JavaScript.

[0292] As a specific example, the following operations can be considered.

[0293] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[0294] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's emotion is "positive."

[0295] 3. The device sends the input data and the results of emotion analysis to the server.

[0296] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[0297] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[0298] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[0299] Examples of prompts include:

[0300] 1. "Please suggest the best food to pair with red wine. I'm feeling positive."

[0301] 2. "For a user who is in a negative mood, suggest the best drink to pair with steak."

[0302] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0303] Step 1: User Input

[0304] The user uses a form displayed on the device to input information about the type of drink or meal. For example, they enter information such as "red wine" or "steak" into the text box and press the "Submit" button. The input information is temporarily stored in the device's memory. An HTML form and JavaScript are used for the actual input. The input data is used in the next step.

[0305] Step 2: Sentiment Analysis

[0306] The device receives information entered by the user and the user's facial expression data and analyzes it using an emotion engine. The emotion engine uses an expression recognition API (e.g., OpenFace or Affectiva) to identify the user's emotional state. For example, it analyzes entered text and captured facial expression data to determine whether the user is in a "positive" or "negative" emotional state. The analysis results of the emotional state are stored on the device.

[0307] Step 3: Send data

[0308] The device converts the emotion analysis results and input information into JSON format. This JSON data is sent to the server via a network protocol (e.g., HTTP POST). The data is sent to the server using JavaScript's fetch API or XMLHttpRequest. The specific input data is the type of drink (e.g., "red wine") and the emotional state (e.g., "positive"), and is sent as a JSON object:

[0309] json

[0310] {

[0311] "drink": "red wine",

[0312] "mood": "positive"

[0313] }

[0314] Step 4: Marriage calculation

[0315] The server receives the JSON data sent from the device and begins processing. The server uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal drink and meal pairing. For example, if the drink is "red wine" and the user's sentiment is "positive," the server generates a calculation result suggesting "steak" as the optimal combination. This calculation utilizes past data and a trained algorithm. The output is the proposed combination (e.g., "steak").

[0316] Step 5: Adjust the results

[0317] The server receives the calculated recommendation results and makes adjustments based on the results of sentiment analysis. For example, if the user is in a "negative" emotional state, the server may determine that "red wine" and "chocolate" are a good combination. Such adjustments are made using an emotion-based content filtering algorithm. The adjusted recommendation results are converted back to JSON format and sent back to the device as output.

[0318] Step 6: View the results

[0319] The device analyzes the adjusted proposal results received from the server and displays them appropriately to the user. HTML and JavaScript are used for display. Specifically, a message such as "Suggested combination: Steak" is displayed on the screen. The display results are then made available to the user, allowing them to refer to the proposed drink and food combinations.

[0320] (Application example 2)

[0321] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0322] Conventional drink and food pairing recommendation systems have difficulty making personalized recommendations that take into account the user's emotional state. Furthermore, the lack of real-time recommendations using virtual stores or smart glasses has led to a decline in the quality of the user experience. Furthermore, there has been a lack of a means to make more appropriate recommendations based on the user's emotions.

[0323] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0324] In this invention, the server includes an input means for a user to input information about a type of drink or a meal, a transmission means for transmitting the input information to a processing device, a suggestion means for receiving the input information at the processing device and suggesting drink and meal combinations, the suggestion means including an adjustment means for adjusting the combinations based on an emotion analysis means for analyzing the user's emotional state, and a display means for displaying the suggestion results generated by the suggestion means to the user. This enables personalized drink and meal combination suggestions that take the user's emotional state into consideration. Furthermore, real-time suggestions can be made using a virtual store or smart glasses, improving the quality of the user experience.

[0325] "Input means" refers to a device or function that allows the user to input information about the type of drink or meal.

[0326] The "transmission means" refers to a function or method for transmitting input information to a processing device.

[0327] The "suggestion means" refers to a function or algorithm that receives information input into the processing device and suggests drink and meal combinations.

[0328] "Emotion analysis means" refers to software or a device for analyzing the user's emotional state, and is capable of, for example, facial expression recognition and voice analysis.

[0329] "Adjustment means" refers to a function or method for adjusting the proposal results based on sentiment analysis.

[0330] The "display means" refers to a device or method for displaying the proposal results generated by the proposal means to the user.

[0331] A "generative AI model" is an artificial intelligence model trained to calculate optimal drink and meal pairings.

[0332] This invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input means, a transmission means, a suggestion means, a display means, and an emotion analysis means. It also includes an adjustment means that adjusts the suggestion results based on the emotion analysis means. Specific embodiments are described below.

[0333] Hardware and software used

[0334] Smart glasses: facial recognition camera, microphone, display

[0335] Emotion analysis engine: OpenFace, Microsoft® Azure® Emotion API

[0336] Data processing: Sending and receiving data in JSON format

[0337] Server: AI model (scikit-learn, TensorFlow, etc.)

[0338] System Operation

[0339] The user wears the smart glasses and inputs information about the type of drink and meal they want using voice or touch controls. The smart glasses' built-in facial recognition camera and microphone capture the user's facial expressions and voice tone, and an emotion analysis engine is used to analyze their emotional state.

[0340] The analysis results and input data are sent to the server in JSON format. The server uses an AI model to calculate the optimal drink and meal pairing. The results of the sentiment analysis are also taken into account, and the pairing results are adjusted accordingly. For example, if "red wine" and a "positive" emotional state are input, the server will suggest "steak."

[0341] The final, adjusted recommendations are displayed on the smart glasses' display, allowing users to see the recommendations in real time, enhancing the shopping experience in the virtual store.

[0342] Specific examples

[0343] Suppose a user verbally commands "red wine" into the smart glasses. The facial recognition camera captures the user's smile, and the emotion analysis engine determines this as "positive." This information is sent in JSON format to the server, where an AI model is used to calculate the optimal combination of "red wine" and "steak." The result is displayed on the smart glasses' display and suggested to the user.

[0344] Prompt Sentence Examples

[0345] "Suggest the perfect food pairing for red wine. Users feel positive."

[0346] This system enables personalized drink and food pairing suggestions that take into account the user's emotional state, and also enables real-time suggestions using virtual stores and smart glasses, improving the quality of the user experience.

[0347] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0348] Step 1:

[0349] The user puts on the smart glasses and inputs information about the type of drink or meal, for example, "red wine," using voice input or touch operation. At this time, the microphone in the smart glasses detects the user's voice and captures the drink information as input data.

[0350] Step 2:

[0351] A facial recognition camera built into the smart glasses captures the user's facial expressions. The facial recognition camera captures the user's face as video data and sends it to an emotion analysis engine. The emotion analysis engine (e.g., OpenFace or Microsoft Azure Emotion API) analyzes the video data and identifies the user's emotional state (e.g., "positive" or "negative").

[0352] Step 3:

[0353] The acquired beverage information and the analysis results of the emotional state are sent to the server in JSON format. The transmission means of the smart glasses processes the input data and sends it to the server via the network. The transmitted data includes beverage information (e.g., "red wine") and the emotional state (e.g., "positive").

[0354] Step 4:

[0355] The server analyzes the received data and uses a generative AI model to calculate the optimal food and drink pairing based on the beverage information and emotional state. The server-side AI model (e.g., scikit-learn or TensorFlow) performs calculations based on the received data to estimate the optimal beverage and food pairing, utilizing past data and pre-trained models.

[0356] Step 5:

[0357] The server adjusts the pairing suggestions generated by the AI ​​model based on the emotional state. For example, if the emotional state is negative, the server might suggest "steak" instead of "red wine." This adjustment is performed by a server-side adjustment mechanism.

[0358] Step 6:

[0359] The server then returns the adjusted recommendation results in JSON format to the smart glasses. The server then compiles the recommendation results as output data and sends it to the smart glasses via the network. The transmitted data includes the recommendation result (e.g., "The best food to go with red wine is steak").

[0360] Step 7:

[0361] The display means of the smart glasses displays the received recommendation results to the user. The display of the smart glasses interprets the received data and displays the recommendation results in the user's field of view. The user can view the recommendation results in real time and improve their shopping experience in the virtual store.

[0362] Through the above steps, a system is realized that takes into account the user's emotional state and suggests optimal drink and meal combinations.

[0363] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0364] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0365] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0366] [Second embodiment]

[0367] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0368] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0369] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0370] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0371] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0373] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0374] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0375] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0376] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0377] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0378] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0379] The present invention provides a system that allows a user to input information about a type of beverage or a meal and, based on that information, suggests optimal pairings (marriage). The system includes an input unit, a transmission unit, a suggestion unit, and a display unit.

[0380] User Operation

[0381] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[0382] Sending data

[0383] Next, the terminal transmits the input data to the server, and the transmission means converts the input information into JSON format and transmits it to the server via the network.

[0384] Data Receipt and Processing

[0385] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine," the AI ​​model will suggest that "steak" is the best pairing. This calculation utilizes past data and trained algorithms.

[0386] Displaying the results

[0387] After the server generates the calculation result, it sends it back to the device. The device analyzes the result and displays it to the user. For example, it may display the result in the form of "Suggested combination: Steak." This display allows the user to easily find the best drink and food combination.

[0388] Specific examples

[0389] 1. The user enters "red wine" on the terminal and submits the form.

[0390] 2. The device sends the input data to the server.

[0391] 3. The server accepts the data and processes it using the AI ​​model.

[0392] 4. The server generates the result "The best food to go with red wine is steak."

[0393] 5. The server sends the result back to the terminal, which displays "Suggested combination: steak" to the user.

[0394] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[0395] The processing flow will be explained below.

[0396] Step 1:

[0397] The user opens a form on the terminal and enters information about a type of drink or meal, for example, "red wine" or "steak."

[0398] Step 2:

[0399] The device captures the information entered by the user and stores it as a data structure (e.g., a JavaScript object).

[0400] Step 3:

[0401] The device serializes the stored data into JSON format and prepares it for transmission, specifically creating a JSON object containing the input drink or meal name.

[0402] Step 4:

[0403] The JSON data generated by the device is sent to the server via the network using an HTTP request.

[0404] Step 5:

[0405] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[0406] Step 6:

[0407] The server analyzes the acquired JSON data and extracts information about the type of drink and meal.

[0408] Step 7:

[0409] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal pairing. Specifically, the AI ​​model calculates the optimal food pairing for each type of beverage.

[0410] Step 8:

[0411] The server composes the proposal results obtained from the AI ​​model in JSON format and prepares them as a response.

[0412] Step 9:

[0413] The server sends the constructed JSON response to the device.

[0414] Step 10:

[0415] The device parses the JSON response received from the server and extracts the proposed combination information.

[0416] Step 11:

[0417] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" on the screen.

[0418] Step 12:

[0419] The user checks the suggested results and enjoys the optimal drink and meal combination.

[0420] Example 1

[0421] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0422] Systems that suggest optimal drink and food pairings (marriage) have traditionally required specialized knowledge and have been difficult for average users to use. Furthermore, there has been a lack of technological solutions that can effectively process input data and make prompt and accurate recommendations. For this reason, there has been a demand for a system that allows users to easily enjoy optimal drink and food pairings.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0424] In this invention, the server includes an input device for a user to input information about the type of drink or meal, a transmission device for transmitting the input information to the server, a processing device that receives the input information at the server and calculates the optimal drink and meal combination using an AI model, and a display device that displays the proposal results generated by the processing device to the user. This makes it possible to calculate the optimal drink and meal combination using the AI ​​model and provide the result to the user quickly and accurately.

[0425] An "input device" is a device that allows a user to input information about a type of drink or a meal.

[0426] The "transmitting device" is a device for transmitting information input by a terminal to a server.

[0427] A "server" is a computer system that performs processing based on received information, generates results, and returns them.

[0428] "Processing device" means a device that receives data entered into the server, processes the data using an AI model, and calculates the optimal combination of beverages and meals.

[0429] An "AI model" is an algorithm or program that uses past data and trained algorithms to calculate the optimal combination based on input beverage and meal information.

[0430] A "display device" is a device or interface for visually displaying to a user the suggestion results generated by the processing device.

[0431] The present invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input device, a transmission device, a processing device, and a display device. Specific embodiments of this system are described below.

[0432] Hardware and Software Configuration

[0433] Users access the form through a web browser on a device (e.g., a PC, smartphone, or tablet) and the form is created using HTML, CSS, and JavaScript and is displayed on the device.

[0434] Input Devices

[0435] The user inputs information about the type of drink or meal, such as "red wine" or "steak," into the form. The input device is a keyboard or a touch screen.

[0436] Transmitting device

[0437] The terminal uses JavaScript to convert the input data into JSON format, which allows the data to be structured and efficiently transmitted over the network, and then sends the converted JSON data to the server as an HTTP POST request using AJAX technology.

[0438] Processing equipment

[0439] The server uses a backend framework (e.g., PHP, Node.js) to receive data sent from the device. The received data is parsed using Python libraries. The parsed data is then fed into a generative AI model to calculate the optimal drink and food pairing. The AI ​​model performs its calculations using historical data and trained algorithms.

[0440] display device

[0441] After the server generates the calculation results, it sends them back to the device as a JSON-formatted response. The device uses an AJAX response handler to parse the result data received from the server. The parsed data is visually displayed to the user using HTML and CSS. Specifically, it is displayed on the screen in the form of "Suggested combination: steak."

[0442] Specific examples

[0443] 1. User action: The user enters "red wine" on the terminal and submits the form.

[0444] 2. Sending data: The device converts the input data into JSON format and sends it to the server as an HTTP POST request.

[0445] 3. Data reception and processing: The server receives the JSON data and passes it to the AI ​​model using a Python library. The AI ​​model uses past data and algorithms to calculate the best food for red wine: steak.

[0446] 4. Generate and send results: The server sends the results obtained from the AI ​​model back to the device as a JSON response.

[0447] 5. Display the result: The device uses the AJAX response handler to parse the JSON data and displays "Suggested combination: steak" on the screen.

[0448] Example prompts for generative AI models

[0449] User entered "red wine". Please suggest the best meal.

[0450] In this way, the present invention can realize a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[0451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0452] Step 1:

[0453] User action: The user opens a browser on their device and accesses the specified URL to display the form.

[0454] What happens: The user enters drink or meal information into the form fields and clicks the submit button.

[0455] Input: The data a user enters into a form (e.g., "red wine" or "steak")

[0456] Output: The data entered by the user is ready to be sent.

[0457] Step 2:

[0458] Terminal operation: The terminal takes the user's input data and converts it into JSON format for transmission.

[0459] Specific behavior: Uses JavaScript to get input data and generate it as a JSON object.

[0460] Input: Data entered by the user (e.g., "red wine")

[0461] Output: Data converted to JSON format (e.g. {'drink': 'red wine'})

[0462] Step 3:

[0463] Terminal operation: Data converted to JSON format is sent to the server.

[0464] What it does: It uses AJAX techniques to send data to the server as an HTTP POST request.

[0465] Input: JSON format data (e.g. {'drink': 'red wine'})

[0466] Output: Input data sent to the server

[0467] Step 4:

[0468] Server operation: The server receives the JSON data sent from the device.

[0469] What it does: Receives requests using a backend framework (e.g., PHP, Node.js).

[0470] Input: JSON data sent from the terminal (e.g., {'drink': 'red wine'})

[0471] Output: The state in which the received data is prepared for analysis

[0472] Step 5:

[0473] Server operation: The server analyzes the received data and passes it to the AI ​​model.

[0474] What it does: Uses Python libraries to parse data and feed it into an AI model.

[0475] Input: Received JSON data (e.g. {'drink': 'red wine'})

[0476] Output: Data input to the AI ​​model (e.g., data after format conversion)

[0477] Step 6:

[0478] Server operation: The AI ​​model calculates the optimal combination based on the input drink and meal information.

[0479] What it does: A generative AI model uses past data and trained algorithms to make calculations.

[0480] Input: Data fed into the AI ​​model (e.g., "red wine")

[0481] Output: Calculation result (e.g. "steak")

[0482] Step 7:

[0483] Server operation: The server generates a response based on the calculation results obtained from the AI ​​model and formats it in JSON format.

[0484] Specific behavior: Converts the calculation result into a JSON object.

[0485] Input: The calculation result from the AI ​​model (e.g., "steak")

[0486] Output: Response data in JSON format (e.g., {'suggestion': 'steak'})

[0487] Step 8:

[0488] Server operation: Returns the formatted data to the terminal.

[0489] Specific behavior: Sends JSON data as an HTTP response.

[0490] Input: JSON format response data (e.g., {'suggestion': 'steak'})

[0491] Output: Data sent to the terminal

[0492] Step 9:

[0493] Terminal operation: The terminal analyzes the result data received from the server.

[0494] Specific behavior: Receives and parses JSON data using the AJAX response handler.

[0495] Input: JSON data returned from the server (e.g., {'suggestion': 'steak'})

[0496] Output: Parsed data (e.g. "steak")

[0497] Step 10:

[0498] Terminal operation: The analyzed data is visually displayed to the user.

[0499] What it does: Displays the results on the screen using HTML and CSS.

[0500] Input: Parsed data (e.g. "steak")

[0501] Output: The suggested result displayed to the user (e.g., "Suggested combination: steak")

[0502] This process allows the user to easily find the best drink and meal combination.

[0503] (Application example 1)

[0504] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0505] In modern society, optimal drink and food pairings (marriage) are an important factor in enhancing dining experiences. However, it is difficult for individual users to determine the optimal drink and food pairing, requiring a great deal of time and knowledge. Furthermore, restaurants and food delivery services require a high level of specialized knowledge to provide satisfactory suggestions to users. This invention aims to solve these problems and provide a system that allows users to easily find the optimal pairing.

[0506] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0507] In this invention, the server includes means for converting user input into JSON format and sending it to the server via a network, means for the server to perform calculations based on the received data and return the optimal combination to the user's smartphone, and means for displaying the generated recommendation results on the user's smartphone and enabling the user to order the recommended drink and meal all at once. This enables users to quickly find the optimal drink and meal combination and easily order without specialized knowledge.

[0508] The "input means" is a means for the user to input information about the type of drink or meal.

[0509] The "transmitting means" is a means for transmitting input information to the processing device.

[0510] The "suggestion means" is a means for receiving information inputted into the processing device and proposing combinations of drinks and meals.

[0511] The "display means" is a means for displaying the proposal results generated by the proposal means to the user.

[0512] An "artificial intelligence model" is a model for calculating optimal beverage and food pairings, using data and algorithms to make predictions and recommendations.

[0513] The "JSON format" is a lightweight data exchange format that expresses data in text format and exchanges structured data.

[0514] A "network" is a collection of communication means for exchanging information, including the Internet and local area networks (LANs).

[0515] A "smartphone" is a portable communication terminal that has not only telephone functions but also the ability to connect to the Internet and run various applications.

[0516] MODE FOR CARRYING OUT THE INVENTION

[0517] This invention is a system that, when a user inputs information about the type of drink and the food, suggests the optimal combination (marriage) based on that information. The specific configuration and processing of this system are described below.

[0518] System configuration

[0519] The system consists of the following main components:

[0520] 1. Input means: A means for the user to input drink type or meal information. This may include a smartphone app or web interface.

[0521] 2. Transmission means: A means for transmitting the input information to the server. Here, the data is converted into JSON format and sent to the server via the Internet.

[0522] 3. Recommendation means: A means for suggesting optimal drink and meal combinations based on the data received by the server. Here, an artificial intelligence model is used.

[0523] 4. Display means: A means for displaying the proposed results to the user. A smartphone or other display device is used.

[0524] Processing flow

[0525] 1. User Input: The user opens the smartphone app and inputs information about their preferred beverage or food, for example, "red wine."

[0526] 2. Data transmission: The entered information is converted into JSON format and sent to the server via the network.

[0527] 3. Processing on the server: The server analyzes the received data and uses an artificial intelligence model to calculate the best food or drink pairing. For example, it might suggest "steak" as the best food to pair with red wine.

[0528] 4. Returning the results: The server converts the calculation results into JSON format and returns them to the user's smartphone.

[0529] 5. Displaying the results: The user's smartphone analyzes the received data and displays the results on the screen, for example, "Suggested combination: Steak."

[0530] Hardware and software used

[0531] Smartphone: A device for user input and display of results.

[0532] Server: Receives and processes data, often using a web framework such as Flask.

[0533] Artificial intelligence model: A model for calculating optimal combinations. It is built using libraries such as TensorFlow and PyTorch.

[0534] JSON format: A data interchange format.

[0535] Specific examples

[0536] 1. The user enters "red wine" into the smartphone app and presses the send button.

[0537] 2. The data is sent to the server, which calculates the best meal to match the "red wine."

[0538] 3. The server returns the calculation result, "steak."

[0539] 4. The user's smartphone will display "Suggested combination: Steak."

[0540] Prompt Sentence Examples

[0541] Please suggest the best food pairings for the following beverages:

[0542] Drink: Red wine

[0543] This invention allows users to easily enjoy optimal drink and food pairings without any specialized knowledge.

[0544] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0545] Step 1:

[0546] The user inputs information about the type of drink or meal through the input means of the terminal. For example, the user inputs the type of drink, "red wine." At this time, the user's input is entered into the application form, and the application is ready for the next process.

[0547] Input: User enters "red wine"

[0548] Output: User input data (e.g. red wine)

[0549] Step 2:

[0550] The device converts the input information into JSON format and sends it to the server via the network. Specifically, the sending means sends an HTTP POST request to the URL endpoint, passing the user's input data to the server.

[0551] Input: User-entered data (e.g., red wine)

[0552] Output: JSON format data (e.g. {"drink": "red wine"})

[0553] Step 3:

[0554] The server parses the JSON data received over the network and converts it into an internal data structure. Based on this data, an artificial intelligence model calculates the optimal meal combination. Here, data calculations are performed using a generative AI model.

[0555] Input: JSON format data (e.g., {"drink": "red wine"})

[0556] Output: Optimal food combination (e.g. steak)

[0557] Step 4:

[0558] The server converts the calculated optimal combination into JSON format and returns it to the device as an HTTP response. The server generates data containing the proposal results and stores it in the response body.

[0559] Input: Optimal food pairing (e.g. steak)

[0560] Output: Response data in JSON format (e.g. {"meal": "steak"})

[0561] Step 5:

[0562] The device parses the JSON format data received from the server and converts it into a format that can be visually presented to the user. Specifically, the data is passed to a UI component that displays the results, and the results are displayed on the user's screen.

[0563] Input: JSON format response data (e.g. {"meal": "steak"})

[0564] Output: User-visible suggestion results (e.g., "Suggested combination: steak")

[0565] Specific examples of operation

[0566] Example prompt (text format):

[0567] Please suggest the best food pairings for the following beverages:

[0568] Drink: Red wine

[0569] In this way, having clear inputs and outputs for each step makes it easier to understand the processing flow of the entire system, and enables the construction of a system that responds quickly and accurately to user requests.

[0570] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0571] The present invention is a system that allows a user to input information about a type of beverage or a meal and then suggests optimal pairings (marriage) based on that information. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[0572] User Operation

[0573] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[0574] Emotion analysis using an emotion engine

[0575] Before the input data is sent, the device uses an emotion engine to analyze the user's input to determine whether they are in a "positive" or "negative" mood, for example, by analyzing the entered text and the user's facial expressions.

[0576] Sending data

[0577] Next, the terminal transmits the user's input data and the result of the emotion analysis to the server. The transmission means converts the input information and the result of the emotion analysis into JSON format and transmits them to the server via the network.

[0578] Data Receipt and Processing

[0579] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine" and the user's emotion is "positive," the AI ​​model will suggest that "steak" is the optimal pairing. This calculation utilizes past data and a trained algorithm.

[0580] Adjusting and displaying results

[0581] After the server generates the calculation results, it adjusts the recommendations accordingly based on the results of the sentiment analysis. For example, if the user is in a negative mood, it might recommend a combination of red wine and chocolate. The adjusted results are then sent back to the device.

[0582] Specific examples

[0583] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[0584] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's mood is "positive."

[0585] 3. The device sends the input data and the results of emotion analysis to the server.

[0586] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[0587] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[0588] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[0589] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a way that is easy for even general users to use. By combining it with an emotion engine, more personalized suggestions that match the user's mood become possible, improving the user experience.

[0590] The processing flow will be explained below.

[0591] Step 1:

[0592] The user opens a form on the terminal and enters the type of drink or meal information, for example, "red wine" or "steak."

[0593] Step 2:

[0594] The user clicks the form submit button, which causes the device to temporarily store the entered data.

[0595] Step 3:

[0596] The emotion engine analyzes the user's emotions based on the data stored on the device. The emotion engine uses text input and facial recognition data to determine the user's emotional state (positive, negative, etc.).

[0597] Step 4:

[0598] The device combines the user's sentiment analysis results and input data into a single JSON object, which includes the drink type, meal information, and emotional state.

[0599] Step 5:

[0600] The JSON object generated by the terminal is sent to the server using an HTTP request.

[0601] Step 6:

[0602] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[0603] Step 7:

[0604] The server analyzes the JSON data it receives and extracts the type of drink, meal information, and the user's emotional state.

[0605] Step 8:

[0606] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal food pairing. Specifically, the AI ​​model calculates the optimal food pairing taking into account the type of beverage and the user's emotional state.

[0607] Step 9:

[0608] The server composes the recommendation results obtained from the AI ​​model in JSON format and prepares them as a response. For example, if the emotional state is "positive," the recommendation result will be "steak," and if the emotional state is "negative," the recommendation result will be "chocolate."

[0609] Step 10:

[0610] The server sends the constructed JSON response to the device.

[0611] Step 11:

[0612] The device parses the JSON response received from the server and extracts the proposed combination information.

[0613] Step 12:

[0614] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" or "Suggested combination: chocolate" on the screen.

[0615] Step 13:

[0616] The user checks the suggested results and enjoys the optimal drink and meal combination.

[0617] Example 2

[0618] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0619] Conventional systems that suggest drink and food pairings have the problem of not taking into account the user's emotional state, and therefore not providing suggestions that match the user's mood. This can lead to low user satisfaction and convenience. Furthermore, the level of personalization of suggestions is low, and necessary information may not be provided appropriately.

[0620] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input information about the type of drink or meal, an emotion analysis means for analyzing the input information and the user's facial expression data using an emotion engine to identify the user's emotional state, and a transmission means for transmitting the analyzed emotional state and the input information to the processing device. This makes it possible to propose optimal combinations of drinks and meals that take the user's emotional state into consideration.

[0621] The "input means" is a means for the user to input information about the type of drink or meal.

[0622] The "emotion analysis means" is a means for analyzing input information and the user's facial expression data to identify the user's emotional state.

[0623] The "transmitting means" is a means for transmitting the analyzed emotional state and input information to the processing device.

[0624] The "suggestion means" is a means for receiving the emotional state analyzed by the processing device and input information, and proposing a combination of drink and food.

[0625] The "result adjustment means" is a means for adjusting the result of the suggestion means based on the emotional state.

[0626] The "display means" is a means for displaying the adjusted proposal results to the user.

[0627] An "AI model" is a model that uses machine learning algorithms to analyze data and calculate optimal drink and meal combinations.

[0628] An "emotion engine" is software or algorithms that analyze a user's facial expressions and text data to identify their emotional state.

[0629] "Processing device" means a device that receives user input data and sentiment analysis results and calculates and adjusts beverage and food pairings using an AI model.

[0630] A "network protocol" is a communication protocol for sending and receiving data, and is used to establish communication between a terminal and a server.

[0631] The present invention provides a system for suggesting optimal pairings (marriage) based on information about drink types or meals input by a user. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[0632] First, the user enters information about the type of drink or meal using a form displayed on the device. For example, they can enter information such as "red wine" or "steak." HTML and JavaScript are used to input information into the form.

[0633] Next, the device analyzes the input information and the user's facial expression using an emotion engine. The emotion engine uses facial recognition APIs such as OpenFace and Affectiva to determine the user's emotional state. For example, if the user is expressing positive emotions, that information will be included in the analysis results.

[0634] The analyzed emotional state and input information are converted to JSON format and sent to the server using a network protocol (e.g., HTTP POST), typically using the JavaScript fetch API or XMLHttpRequest.

[0635] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. The AI ​​model uses TensorFlow and PyTorch, and performs calculations using past data and trained algorithms. The calculation results may be a recommendation such as "The best food to pair red wine with is steak."

[0636] Furthermore, the server adjusts the recommendation results based on the user's emotional state. For example, if the user is in a negative emotional state, the server will adjust the pairings based on the user's emotional state, such as red wine and chocolate. This adjustment is achieved using an emotion-based content filtering algorithm.

[0637] The adjusted results are converted back into JSON format and sent over the network to the device. The device parses the received results and displays them in an appropriate format for the user. For example, it displays "Suggested combination: Steak" on the screen using HTML and JavaScript.

[0638] As a specific example, the following operations can be considered.

[0639] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[0640] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's emotion is "positive."

[0641] 3. The device sends the input data and the results of emotion analysis to the server.

[0642] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[0643] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[0644] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[0645] Examples of prompts include:

[0646] 1. "Please suggest the best food to pair with red wine. I'm feeling positive."

[0647] 2. "For a user who is in a negative mood, suggest the best drink to pair with steak."

[0648] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0649] Step 1: User Input

[0650] The user uses a form displayed on the device to input information about the type of drink or meal. For example, they enter information such as "red wine" or "steak" into the text box and press the "Submit" button. The input information is temporarily stored in the device's memory. An HTML form and JavaScript are used for the actual input. The input data is used in the next step.

[0651] Step 2: Sentiment Analysis

[0652] The device receives information entered by the user and the user's facial expression data and analyzes it using an emotion engine. The emotion engine uses an expression recognition API (e.g., OpenFace or Affectiva) to identify the user's emotional state. For example, it analyzes entered text and captured facial expression data to determine whether the user is in a "positive" or "negative" emotional state. The analysis results of the emotional state are stored on the device.

[0653] Step 3: Send data

[0654] The device converts the emotion analysis results and input information into JSON format. This JSON data is sent to the server via a network protocol (e.g., HTTP POST). The data is sent to the server using JavaScript's fetch API or XMLHttpRequest. The specific input data is the type of drink (e.g., "red wine") and the emotional state (e.g., "positive"), and is sent as a JSON object:

[0655] json

[0656] {

[0657] "drink": "red wine",

[0658] "mood": "positive"

[0659] }

[0660] Step 4: Marriage calculation

[0661] The server receives the JSON data sent from the device and begins processing. The server uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal drink and meal pairing. For example, if the drink is "red wine" and the user's sentiment is "positive," the server generates a calculation result suggesting "steak" as the optimal combination. This calculation utilizes past data and a trained algorithm. The output is the proposed combination (e.g., "steak").

[0662] Step 5: Adjust the results

[0663] The server receives the calculated recommendation results and makes adjustments based on the results of sentiment analysis. For example, if the user is in a "negative" emotional state, the server may determine that "red wine" and "chocolate" are a good combination. Such adjustments are made using an emotion-based content filtering algorithm. The adjusted recommendation results are converted back to JSON format and sent back to the device as output.

[0664] Step 6: View the results

[0665] The device analyzes the adjusted proposal results received from the server and displays them appropriately to the user. HTML and JavaScript are used for display. Specifically, a message such as "Suggested combination: Steak" is displayed on the screen. The display results are then made available to the user, allowing them to refer to the proposed drink and food combinations.

[0666] (Application example 2)

[0667] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] Conventional drink and food pairing recommendation systems have difficulty making personalized recommendations that take into account the user's emotional state. Furthermore, the lack of real-time recommendations using virtual stores or smart glasses has led to a decline in the quality of the user experience. Furthermore, there has been a lack of a means to make more appropriate recommendations based on the user's emotions.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0670] In this invention, the server includes an input means for a user to input information about a type of drink or a meal, a transmission means for transmitting the input information to a processing device, a suggestion means for receiving the input information at the processing device and suggesting drink and meal combinations, the suggestion means including an adjustment means for adjusting the combinations based on an emotion analysis means for analyzing the user's emotional state, and a display means for displaying the suggestion results generated by the suggestion means to the user. This enables personalized drink and meal combination suggestions that take the user's emotional state into consideration. Furthermore, real-time suggestions can be made using a virtual store or smart glasses, improving the quality of the user experience.

[0671] "Input means" refers to a device or function that allows the user to input information about the type of drink or meal.

[0672] The "transmission means" refers to a function or method for transmitting input information to a processing device.

[0673] The "suggestion means" refers to a function or algorithm that receives information input into the processing device and suggests drink and meal combinations.

[0674] "Emotion analysis means" refers to software or a device for analyzing the user's emotional state, and is capable of, for example, facial expression recognition and voice analysis.

[0675] "Adjustment means" refers to a function or method for adjusting the proposal results based on sentiment analysis.

[0676] The "display means" refers to a device or method for displaying the proposal results generated by the proposal means to the user.

[0677] A "generative AI model" is an artificial intelligence model trained to calculate optimal drink and meal pairings.

[0678] This invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input means, a transmission means, a suggestion means, a display means, and an emotion analysis means. It also includes an adjustment means that adjusts the suggestion results based on the emotion analysis means. Specific embodiments are described below.

[0679] Hardware and software used

[0680] Smart glasses: facial recognition camera, microphone, display

[0681] Sentiment analysis engine: OpenFace, Microsoft Azure Emotion API

[0682] Data processing: Sending and receiving data in JSON format

[0683] Server: AI model (scikit-learn, TensorFlow, etc.)

[0684] System Operation

[0685] The user wears the smart glasses and inputs information about the type of drink and meal they want using voice or touch controls. The smart glasses' built-in facial recognition camera and microphone capture the user's facial expressions and voice tone, and an emotion analysis engine is used to analyze their emotional state.

[0686] The analysis results and input data are sent to the server in JSON format. The server uses an AI model to calculate the optimal drink and meal pairing. The results of the sentiment analysis are also taken into account, and the pairing results are adjusted accordingly. For example, if "red wine" and a "positive" emotional state are input, the server will suggest "steak."

[0687] The final, adjusted recommendations are displayed on the smart glasses' display, allowing users to see the recommendations in real time, enhancing the shopping experience in the virtual store.

[0688] Specific examples

[0689] Suppose a user verbally commands "red wine" into the smart glasses. The facial recognition camera captures the user's smile, and the emotion analysis engine determines this as "positive." This information is sent in JSON format to the server, where an AI model is used to calculate the optimal combination of "red wine" and "steak." The result is displayed on the smart glasses' display and suggested to the user.

[0690] Prompt Sentence Examples

[0691] "Suggest the perfect food pairing for red wine. Users feel positive."

[0692] This system enables personalized drink and food pairing suggestions that take into account the user's emotional state, and also enables real-time suggestions using virtual stores and smart glasses, improving the quality of the user experience.

[0693] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0694] Step 1:

[0695] The user puts on the smart glasses and inputs information about the type of drink or meal, for example, "red wine," using voice input or touch operation. At this time, the microphone in the smart glasses detects the user's voice and captures the drink information as input data.

[0696] Step 2:

[0697] A facial recognition camera built into the smart glasses captures the user's facial expressions. The facial recognition camera captures the user's face as video data and sends it to an emotion analysis engine. The emotion analysis engine (e.g., OpenFace or Microsoft Azure Emotion API) analyzes the video data and identifies the user's emotional state (e.g., "positive" or "negative").

[0698] Step 3:

[0699] The acquired beverage information and the analysis results of the emotional state are sent to the server in JSON format. The transmission means of the smart glasses processes the input data and sends it to the server via the network. The transmitted data includes beverage information (e.g., "red wine") and the emotional state (e.g., "positive").

[0700] Step 4:

[0701] The server analyzes the received data and uses a generative AI model to calculate the optimal food and drink pairing based on the beverage information and emotional state. The server-side AI model (e.g., scikit-learn or TensorFlow) performs calculations based on the received data to estimate the optimal beverage and food pairing, utilizing past data and pre-trained models.

[0702] Step 5:

[0703] The server adjusts the pairing suggestions generated by the AI ​​model based on the emotional state. For example, if the emotional state is negative, the server might suggest "steak" instead of "red wine." This adjustment is performed by a server-side adjustment mechanism.

[0704] Step 6:

[0705] The server then returns the adjusted recommendation results in JSON format to the smart glasses. The server then compiles the recommendation results as output data and sends it to the smart glasses via the network. The transmitted data includes the recommendation result (e.g., "The best food to go with red wine is steak").

[0706] Step 7:

[0707] The display means of the smart glasses displays the received recommendation results to the user. The display of the smart glasses interprets the received data and displays the recommendation results in the user's field of view. The user can view the recommendation results in real time and improve their shopping experience in the virtual store.

[0708] Through the above steps, a system is realized that takes into account the user's emotional state and suggests optimal drink and meal combinations.

[0709] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0710] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0711] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0712] [Third embodiment]

[0713] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0714] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0715] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0716] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0717] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0718] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0719] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0720] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0721] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0722] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0723] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0724] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0725] The present invention provides a system that allows a user to input information about a type of beverage or a meal and, based on that information, suggests optimal pairings (marriage). The system includes an input unit, a transmission unit, a suggestion unit, and a display unit.

[0726] User Operation

[0727] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[0728] Sending data

[0729] Next, the terminal transmits the input data to the server, and the transmission means converts the input information into JSON format and transmits it to the server via the network.

[0730] Data Receipt and Processing

[0731] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine," the AI ​​model will suggest that "steak" is the best pairing. This calculation utilizes past data and trained algorithms.

[0732] Displaying the results

[0733] After the server generates the calculation result, it sends it back to the device. The device analyzes the result and displays it to the user. For example, it may display the result in the form of "Suggested combination: Steak." This display allows the user to easily find the best drink and food combination.

[0734] Specific examples

[0735] 1. The user enters "red wine" on the terminal and submits the form.

[0736] 2. The device sends the input data to the server.

[0737] 3. The server accepts the data and processes it using the AI ​​model.

[0738] 4. The server generates the result "The best food to go with red wine is steak."

[0739] 5. The server sends the result back to the terminal, which displays "Suggested combination: steak" to the user.

[0740] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[0741] The processing flow will be explained below.

[0742] Step 1:

[0743] The user opens a form on the terminal and enters information about a type of drink or meal, for example, "red wine" or "steak."

[0744] Step 2:

[0745] The device captures the information entered by the user and stores it as a data structure (e.g., a JavaScript object).

[0746] Step 3:

[0747] The device serializes the stored data into JSON format and prepares it for transmission, specifically creating a JSON object containing the input drink or meal name.

[0748] Step 4:

[0749] The JSON data generated by the device is sent to the server via the network using an HTTP request.

[0750] Step 5:

[0751] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[0752] Step 6:

[0753] The server analyzes the acquired JSON data and extracts information about the type of drink and meal.

[0754] Step 7:

[0755] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal pairing. Specifically, the AI ​​model calculates the optimal food pairing for each type of beverage.

[0756] Step 8:

[0757] The server composes the proposal results obtained from the AI ​​model in JSON format and prepares them as a response.

[0758] Step 9:

[0759] The server sends the constructed JSON response to the device.

[0760] Step 10:

[0761] The device parses the JSON response received from the server and extracts the proposed combination information.

[0762] Step 11:

[0763] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" on the screen.

[0764] Step 12:

[0765] The user checks the suggested results and enjoys the optimal drink and meal combination.

[0766] Example 1

[0767] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0768] Systems that suggest optimal drink and food pairings (marriage) have traditionally required specialized knowledge and have been difficult for average users to use. Furthermore, there has been a lack of technological solutions that can effectively process input data and make prompt and accurate recommendations. For this reason, there has been a demand for a system that allows users to easily enjoy optimal drink and food pairings.

[0769] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0770] In this invention, the server includes an input device for a user to input information about the type of drink or meal, a transmission device for transmitting the input information to the server, a processing device that receives the input information at the server and calculates the optimal drink and meal combination using an AI model, and a display device that displays the proposal results generated by the processing device to the user. This makes it possible to calculate the optimal drink and meal combination using the AI ​​model and provide the result to the user quickly and accurately.

[0771] An "input device" is a device that allows a user to input information about a type of drink or a meal.

[0772] The "transmitting device" is a device for transmitting information input by a terminal to a server.

[0773] A "server" is a computer system that performs processing based on received information, generates results, and returns them.

[0774] "Processing device" means a device that receives data entered into the server, processes the data using an AI model, and calculates the optimal combination of beverages and meals.

[0775] An "AI model" is an algorithm or program that uses past data and trained algorithms to calculate the optimal combination based on input beverage and meal information.

[0776] A "display device" is a device or interface for visually displaying to a user the suggestion results generated by the processing device.

[0777] The present invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input device, a transmission device, a processing device, and a display device. Specific embodiments of this system are described below.

[0778] Hardware and Software Configuration

[0779] Users access the form through a web browser on a device (e.g., a PC, smartphone, or tablet) and the form is created using HTML, CSS, and JavaScript and is displayed on the device.

[0780] Input Devices

[0781] The user inputs information about the type of drink or meal, such as "red wine" or "steak," into the form. The input device is a keyboard or a touch screen.

[0782] Transmitting device

[0783] The terminal uses JavaScript to convert the input data into JSON format, which allows the data to be structured and efficiently transmitted over the network, and then sends the converted JSON data to the server as an HTTP POST request using AJAX technology.

[0784] Processing equipment

[0785] The server uses a backend framework (e.g., PHP, Node.js) to receive data sent from the device. The received data is parsed using Python libraries. The parsed data is then fed into a generative AI model to calculate the optimal drink and food pairing. The AI ​​model performs its calculations using historical data and trained algorithms.

[0786] display device

[0787] After the server generates the calculation results, it sends them back to the device as a JSON-formatted response. The device uses an AJAX response handler to parse the result data received from the server. The parsed data is visually displayed to the user using HTML and CSS. Specifically, it is displayed on the screen in the form of "Suggested combination: steak."

[0788] Specific examples

[0789] 1. User action: The user enters "red wine" on the terminal and submits the form.

[0790] 2. Sending data: The device converts the input data into JSON format and sends it to the server as an HTTP POST request.

[0791] 3. Data reception and processing: The server receives the JSON data and passes it to the AI ​​model using a Python library. The AI ​​model uses past data and algorithms to calculate the best food for red wine: steak.

[0792] 4. Generate and send results: The server sends the results obtained from the AI ​​model back to the device as a JSON response.

[0793] 5. Display the result: The device uses the AJAX response handler to parse the JSON data and displays "Suggested combination: steak" on the screen.

[0794] Example prompts for generative AI models

[0795] User entered "red wine". Please suggest the best meal.

[0796] In this way, the present invention can realize a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[0797] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0798] Step 1:

[0799] User action: The user opens a browser on their device and accesses the specified URL to display the form.

[0800] What happens: The user enters drink or meal information into the form fields and clicks the submit button.

[0801] Input: The data a user enters into a form (e.g., "red wine" or "steak")

[0802] Output: The data entered by the user is ready to be sent.

[0803] Step 2:

[0804] Terminal operation: The terminal takes the user's input data and converts it into JSON format for transmission.

[0805] Specific behavior: Uses JavaScript to get input data and generate it as a JSON object.

[0806] Input: Data entered by the user (e.g., "red wine")

[0807] Output: Data converted to JSON format (e.g. {'drink': 'red wine'})

[0808] Step 3:

[0809] Terminal operation: Data converted to JSON format is sent to the server.

[0810] What it does: It uses AJAX techniques to send data to the server as an HTTP POST request.

[0811] Input: JSON format data (e.g. {'drink': 'red wine'})

[0812] Output: Input data sent to the server

[0813] Step 4:

[0814] Server operation: The server receives the JSON data sent from the device.

[0815] What it does: Receives requests using a backend framework (e.g., PHP, Node.js).

[0816] Input: JSON data sent from the terminal (e.g., {'drink': 'red wine'})

[0817] Output: The state in which the received data is prepared for analysis

[0818] Step 5:

[0819] Server operation: The server analyzes the received data and passes it to the AI ​​model.

[0820] What it does: Uses Python libraries to parse data and feed it into an AI model.

[0821] Input: Received JSON data (e.g. {'drink': 'red wine'})

[0822] Output: Data input to the AI ​​model (e.g., data after format conversion)

[0823] Step 6:

[0824] Server operation: The AI ​​model calculates the optimal combination based on the input drink and meal information.

[0825] What it does: A generative AI model uses past data and trained algorithms to make calculations.

[0826] Input: Data fed into the AI ​​model (e.g., "red wine")

[0827] Output: Calculation result (e.g. "steak")

[0828] Step 7:

[0829] Server operation: The server generates a response based on the calculation results obtained from the AI ​​model and formats it in JSON format.

[0830] Specific behavior: Converts the calculation result into a JSON object.

[0831] Input: The calculation result from the AI ​​model (e.g., "steak")

[0832] Output: Response data in JSON format (e.g., {'suggestion': 'steak'})

[0833] Step 8:

[0834] Server operation: Returns the formatted data to the terminal.

[0835] Specific behavior: Sends JSON data as an HTTP response.

[0836] Input: JSON format response data (e.g., {'suggestion': 'steak'})

[0837] Output: Data sent to the terminal

[0838] Step 9:

[0839] Terminal operation: The terminal analyzes the result data received from the server.

[0840] Specific behavior: Receives and parses JSON data using the AJAX response handler.

[0841] Input: JSON data returned from the server (e.g., {'suggestion': 'steak'})

[0842] Output: Parsed data (e.g. "steak")

[0843] Step 10:

[0844] Terminal operation: The analyzed data is visually displayed to the user.

[0845] What it does: Displays the results on the screen using HTML and CSS.

[0846] Input: Parsed data (e.g. "steak")

[0847] Output: The suggested result displayed to the user (e.g., "Suggested combination: steak")

[0848] This process allows the user to easily find the best drink and meal combination.

[0849] (Application example 1)

[0850] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0851] In modern society, optimal drink and food pairings (marriage) are an important factor in enhancing dining experiences. However, it is difficult for individual users to determine the optimal drink and food pairing, requiring a great deal of time and knowledge. Furthermore, restaurants and food delivery services require a high level of specialized knowledge to provide satisfactory suggestions to users. This invention aims to solve these problems and provide a system that allows users to easily find the optimal pairing.

[0852] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0853] In this invention, the server includes means for converting user input into JSON format and sending it to the server via a network, means for the server to perform calculations based on the received data and return the optimal combination to the user's smartphone, and means for displaying the generated recommendation results on the user's smartphone and enabling the user to order the recommended drink and meal all at once. This enables users to quickly find the optimal drink and meal combination and easily order without specialized knowledge.

[0854] The "input means" is a means for the user to input information about the type of drink or meal.

[0855] The "transmitting means" is a means for transmitting input information to the processing device.

[0856] The "suggestion means" is a means for receiving information inputted into the processing device and proposing combinations of drinks and meals.

[0857] The "display means" is a means for displaying the proposal results generated by the proposal means to the user.

[0858] An "artificial intelligence model" is a model for calculating optimal beverage and food pairings, using data and algorithms to make predictions and recommendations.

[0859] The "JSON format" is a lightweight data exchange format that expresses data in text format and exchanges structured data.

[0860] A "network" is a collection of communication means for exchanging information, including the Internet and local area networks (LANs).

[0861] A "smartphone" is a portable communication terminal that has not only telephone functions but also the ability to connect to the Internet and run various applications.

[0862] MODE FOR CARRYING OUT THE INVENTION

[0863] This invention is a system that, when a user inputs information about the type of drink and the food, suggests the optimal combination (marriage) based on that information. The specific configuration and processing of this system are described below.

[0864] System configuration

[0865] The system consists of the following main components:

[0866] 1. Input means: A means for the user to input drink type or meal information. This may include a smartphone app or web interface.

[0867] 2. Transmission means: A means for transmitting the input information to the server. Here, the data is converted into JSON format and sent to the server via the Internet.

[0868] 3. Recommendation means: A means for suggesting optimal drink and meal combinations based on the data received by the server. Here, an artificial intelligence model is used.

[0869] 4. Display means: A means for displaying the proposed results to the user. A smartphone or other display device is used.

[0870] Processing flow

[0871] 1. User Input: The user opens the smartphone app and inputs information about their preferred beverage or food, for example, "red wine."

[0872] 2. Data transmission: The entered information is converted into JSON format and sent to the server via the network.

[0873] 3. Processing on the server: The server analyzes the received data and uses an artificial intelligence model to calculate the best food or drink pairing. For example, it might suggest "steak" as the best food to pair with red wine.

[0874] 4. Returning the results: The server converts the calculation results into JSON format and returns them to the user's smartphone.

[0875] 5. Displaying the results: The user's smartphone analyzes the received data and displays the results on the screen, for example, "Suggested combination: Steak."

[0876] Hardware and software used

[0877] Smartphone: A device for user input and display of results.

[0878] Server: Receives and processes data, often using a web framework such as Flask.

[0879] Artificial intelligence model: A model for calculating optimal combinations. It is built using libraries such as TensorFlow and PyTorch.

[0880] JSON format: A data interchange format.

[0881] Specific examples

[0882] 1. The user enters "red wine" into the smartphone app and presses the send button.

[0883] 2. The data is sent to the server, which calculates the best meal to match the "red wine."

[0884] 3. The server returns the calculation result, "steak."

[0885] 4. The user's smartphone will display "Suggested combination: Steak."

[0886] Prompt Sentence Examples

[0887] Please suggest the best food pairings for the following beverages:

[0888] Drink: Red wine

[0889] This invention allows users to easily enjoy optimal drink and food pairings without any specialized knowledge.

[0890] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0891] Step 1:

[0892] The user inputs information about the type of drink or meal through the input means of the terminal. For example, the user inputs the type of drink, "red wine." At this time, the user's input is entered into the application form, and the application is ready for the next process.

[0893] Input: User enters "red wine"

[0894] Output: User input data (e.g. red wine)

[0895] Step 2:

[0896] The device converts the input information into JSON format and sends it to the server via the network. Specifically, the sending means sends an HTTP POST request to the URL endpoint, passing the user's input data to the server.

[0897] Input: User-entered data (e.g., red wine)

[0898] Output: JSON format data (e.g. {"drink": "red wine"})

[0899] Step 3:

[0900] The server parses the JSON data received over the network and converts it into an internal data structure. Based on this data, an artificial intelligence model calculates the optimal meal combination. Here, data calculations are performed using a generative AI model.

[0901] Input: JSON format data (e.g., {"drink": "red wine"})

[0902] Output: Optimal food combination (e.g. steak)

[0903] Step 4:

[0904] The server converts the calculated optimal combination into JSON format and returns it to the device as an HTTP response. The server generates data containing the proposal results and stores it in the response body.

[0905] Input: Optimal food pairing (e.g. steak)

[0906] Output: Response data in JSON format (e.g. {"meal": "steak"})

[0907] Step 5:

[0908] The device parses the JSON format data received from the server and converts it into a format that can be visually presented to the user. Specifically, the data is passed to a UI component that displays the results, and the results are displayed on the user's screen.

[0909] Input: JSON format response data (e.g. {"meal": "steak"})

[0910] Output: User-visible suggestion results (e.g., "Suggested combination: steak")

[0911] Specific examples of operation

[0912] Example prompt (text format):

[0913] Please suggest the best food pairings for the following beverages:

[0914] Drink: Red wine

[0915] In this way, having clear inputs and outputs for each step makes it easier to understand the processing flow of the entire system, and enables the construction of a system that responds quickly and accurately to user requests.

[0916] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0917] The present invention is a system that allows a user to input information about a type of beverage or a meal and then suggests optimal pairings (marriage) based on that information. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[0918] User Operation

[0919] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[0920] Emotion analysis using an emotion engine

[0921] Before the input data is sent, the device uses an emotion engine to analyze the user's input to determine whether they are in a "positive" or "negative" mood, for example, by analyzing the entered text and the user's facial expressions.

[0922] Sending data

[0923] Next, the terminal transmits the user's input data and the result of the emotion analysis to the server. The transmission means converts the input information and the result of the emotion analysis into JSON format and transmits them to the server via the network.

[0924] Data Receipt and Processing

[0925] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine" and the user's emotion is "positive," the AI ​​model will suggest that "steak" is the optimal pairing. This calculation utilizes past data and a trained algorithm.

[0926] Adjusting and displaying results

[0927] After the server generates the calculation results, it adjusts the recommendations accordingly based on the results of the sentiment analysis. For example, if the user is in a negative mood, it might recommend a combination of red wine and chocolate. The adjusted results are then sent back to the device.

[0928] Specific examples

[0929] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[0930] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's mood is "positive."

[0931] 3. The device sends the input data and the results of emotion analysis to the server.

[0932] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[0933] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[0934] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[0935] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a way that is easy for even general users to use. By combining it with an emotion engine, more personalized suggestions that match the user's mood become possible, improving the user experience.

[0936] The processing flow will be explained below.

[0937] Step 1:

[0938] The user opens a form on the terminal and enters the type of drink or meal information, for example, "red wine" or "steak."

[0939] Step 2:

[0940] The user clicks the form submit button, which causes the device to temporarily store the entered data.

[0941] Step 3:

[0942] The emotion engine analyzes the user's emotions based on the data stored on the device. The emotion engine uses text input and facial recognition data to determine the user's emotional state (positive, negative, etc.).

[0943] Step 4:

[0944] The device combines the user's sentiment analysis results and input data into a single JSON object, which includes the drink type, meal information, and emotional state.

[0945] Step 5:

[0946] The JSON object generated by the terminal is sent to the server using an HTTP request.

[0947] Step 6:

[0948] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[0949] Step 7:

[0950] The server analyzes the JSON data it receives and extracts the type of drink, meal information, and the user's emotional state.

[0951] Step 8:

[0952] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal food pairing. Specifically, the AI ​​model calculates the optimal food pairing taking into account the type of beverage and the user's emotional state.

[0953] Step 9:

[0954] The server composes the recommendation results obtained from the AI ​​model in JSON format and prepares them as a response. For example, if the emotional state is "positive," the recommendation result will be "steak," and if the emotional state is "negative," the recommendation result will be "chocolate."

[0955] Step 10:

[0956] The server sends the constructed JSON response to the device.

[0957] Step 11:

[0958] The device parses the JSON response received from the server and extracts the proposed combination information.

[0959] Step 12:

[0960] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" or "Suggested combination: chocolate" on the screen.

[0961] Step 13:

[0962] The user checks the suggested results and enjoys the optimal drink and meal combination.

[0963] Example 2

[0964] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0965] Conventional systems that suggest drink and food pairings have the problem of not taking into account the user's emotional state, and therefore not providing suggestions that match the user's mood. This can lead to low user satisfaction and convenience. Furthermore, the level of personalization of suggestions is low, and necessary information may not be provided appropriately.

[0966] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input information about the type of drink or meal, an emotion analysis means for analyzing the input information and the user's facial expression data using an emotion engine to identify the user's emotional state, and a transmission means for transmitting the analyzed emotional state and the input information to the processing device. This makes it possible to propose optimal combinations of drinks and meals that take the user's emotional state into consideration.

[0967] The "input means" is a means for the user to input information about the type of drink or meal.

[0968] The "emotion analysis means" is a means for analyzing input information and the user's facial expression data to identify the user's emotional state.

[0969] The "transmitting means" is a means for transmitting the analyzed emotional state and input information to the processing device.

[0970] The "suggestion means" is a means for receiving the emotional state analyzed by the processing device and input information, and proposing a combination of drink and food.

[0971] The "result adjustment means" is a means for adjusting the result of the suggestion means based on the emotional state.

[0972] The "display means" is a means for displaying the adjusted proposal results to the user.

[0973] An "AI model" is a model that uses machine learning algorithms to analyze data and calculate optimal drink and meal combinations.

[0974] An "emotion engine" is software or algorithms that analyze a user's facial expressions and text data to identify their emotional state.

[0975] "Processing device" means a device that receives user input data and sentiment analysis results and calculates and adjusts beverage and food pairings using an AI model.

[0976] A "network protocol" is a communication protocol for sending and receiving data, and is used to establish communication between a terminal and a server.

[0977] The present invention provides a system for suggesting optimal pairings (marriage) based on information about drink types or meals input by a user. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[0978] First, the user enters information about the type of drink or meal using a form displayed on the device. For example, they can enter information such as "red wine" or "steak." HTML and JavaScript are used to input information into the form.

[0979] Next, the device analyzes the input information and the user's facial expression using an emotion engine. The emotion engine uses facial recognition APIs such as OpenFace and Affectiva to determine the user's emotional state. For example, if the user is expressing positive emotions, that information will be included in the analysis results.

[0980] The analyzed emotional state and input information are converted to JSON format and sent to the server using a network protocol (e.g., HTTP POST), typically using the JavaScript fetch API or XMLHttpRequest.

[0981] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. The AI ​​model uses TensorFlow and PyTorch, and performs calculations using past data and trained algorithms. The calculation results may be a recommendation such as "The best food to pair red wine with is steak."

[0982] Furthermore, the server adjusts the recommendation results based on the user's emotional state. For example, if the user is in a negative emotional state, the server will adjust the pairings based on the user's emotional state, such as red wine and chocolate. This adjustment is achieved using an emotion-based content filtering algorithm.

[0983] The adjusted results are converted back into JSON format and sent over the network to the device. The device parses the received results and displays them in an appropriate format for the user. For example, it displays "Suggested combination: Steak" on the screen using HTML and JavaScript.

[0984] As a specific example, the following operations can be considered.

[0985] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[0986] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's emotion is "positive."

[0987] 3. The device sends the input data and the results of emotion analysis to the server.

[0988] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[0989] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[0990] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[0991] Examples of prompts include:

[0992] 1. "Please suggest the best food to pair with red wine. I'm feeling positive."

[0993] 2. "For a user who is in a negative mood, suggest the best drink to pair with steak."

[0994] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0995] Step 1: User Input

[0996] The user uses a form displayed on the device to input information about the type of drink or meal. For example, they enter information such as "red wine" or "steak" into the text box and press the "Submit" button. The input information is temporarily stored in the device's memory. An HTML form and JavaScript are used for the actual input. The input data is used in the next step.

[0997] Step 2: Sentiment Analysis

[0998] The device receives information entered by the user and the user's facial expression data and analyzes it using an emotion engine. The emotion engine uses an expression recognition API (e.g., OpenFace or Affectiva) to identify the user's emotional state. For example, it analyzes entered text and captured facial expression data to determine whether the user is in a "positive" or "negative" emotional state. The analysis results of the emotional state are stored on the device.

[0999] Step 3: Send data

[1000] The device converts the emotion analysis results and input information into JSON format. This JSON data is sent to the server via a network protocol (e.g., HTTP POST). The data is sent to the server using JavaScript's fetch API or XMLHttpRequest. The specific input data is the type of drink (e.g., "red wine") and the emotional state (e.g., "positive"), and is sent as a JSON object:

[1001] json

[1002] {

[1003] "drink": "red wine",

[1004] "mood": "positive"

[1005] }

[1006] Step 4: Marriage calculation

[1007] The server receives the JSON data sent from the device and begins processing. The server uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal drink and meal pairing. For example, if the drink is "red wine" and the user's sentiment is "positive," the server generates a calculation result suggesting "steak" as the optimal combination. This calculation utilizes past data and a trained algorithm. The output is the proposed combination (e.g., "steak").

[1008] Step 5: Adjust the results

[1009] The server receives the calculated recommendation results and makes adjustments based on the results of sentiment analysis. For example, if the user is in a "negative" emotional state, the server may determine that "red wine" and "chocolate" are a good combination. Such adjustments are made using an emotion-based content filtering algorithm. The adjusted recommendation results are converted back to JSON format and sent back to the device as output.

[1010] Step 6: View the results

[1011] The device analyzes the adjusted proposal results received from the server and displays them appropriately to the user. HTML and JavaScript are used for display. Specifically, a message such as "Suggested combination: Steak" is displayed on the screen. The display results are then made available to the user, allowing them to refer to the proposed drink and food combinations.

[1012] (Application example 2)

[1013] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1014] Conventional drink and food pairing recommendation systems have difficulty making personalized recommendations that take into account the user's emotional state. Furthermore, the lack of real-time recommendations using virtual stores or smart glasses has led to a decline in the quality of the user experience. Furthermore, there has been a lack of a means to make more appropriate recommendations based on the user's emotions.

[1015] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1016] In this invention, the server includes an input means for a user to input information about a type of drink or a meal, a transmission means for transmitting the input information to a processing device, a suggestion means for receiving the input information at the processing device and suggesting drink and meal combinations, the suggestion means including an adjustment means for adjusting the combinations based on an emotion analysis means for analyzing the user's emotional state, and a display means for displaying the suggestion results generated by the suggestion means to the user. This enables personalized drink and meal combination suggestions that take the user's emotional state into consideration. Furthermore, real-time suggestions can be made using a virtual store or smart glasses, improving the quality of the user experience.

[1017] "Input means" refers to a device or function that allows the user to input information about the type of drink or meal.

[1018] The "transmission means" refers to a function or method for transmitting input information to a processing device.

[1019] The "suggestion means" refers to a function or algorithm that receives information input into the processing device and suggests drink and meal combinations.

[1020] "Emotion analysis means" refers to software or a device for analyzing the user's emotional state, and is capable of, for example, facial expression recognition and voice analysis.

[1021] "Adjustment means" refers to a function or method for adjusting the proposal results based on sentiment analysis.

[1022] The "display means" refers to a device or method for displaying the proposal results generated by the proposal means to the user.

[1023] A "generative AI model" is an artificial intelligence model trained to calculate optimal drink and meal pairings.

[1024] This invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input means, a transmission means, a suggestion means, a display means, and an emotion analysis means. It also includes an adjustment means that adjusts the suggestion results based on the emotion analysis means. Specific embodiments are described below.

[1025] Hardware and software used

[1026] Smart glasses: facial recognition camera, microphone, display

[1027] Sentiment analysis engine: OpenFace, Microsoft Azure Emotion API

[1028] Data processing: Sending and receiving data in JSON format

[1029] Server: AI model (scikit-learn, TensorFlow, etc.)

[1030] System Operation

[1031] The user wears the smart glasses and inputs information about the type of drink and meal they want using voice or touch controls. The smart glasses' built-in facial recognition camera and microphone capture the user's facial expressions and voice tone, and an emotion analysis engine is used to analyze their emotional state.

[1032] The analysis results and input data are sent to the server in JSON format. The server uses an AI model to calculate the optimal drink and meal pairing. The results of the sentiment analysis are also taken into account, and the pairing results are adjusted accordingly. For example, if "red wine" and a "positive" emotional state are input, the server will suggest "steak."

[1033] The final, adjusted recommendations are displayed on the smart glasses' display, allowing users to see the recommendations in real time, enhancing the shopping experience in the virtual store.

[1034] Specific examples

[1035] Suppose a user verbally commands "red wine" into the smart glasses. The facial recognition camera captures the user's smile, and the emotion analysis engine determines this as "positive." This information is sent in JSON format to the server, where an AI model is used to calculate the optimal combination of "red wine" and "steak." The result is displayed on the smart glasses' display and suggested to the user.

[1036] Prompt Sentence Examples

[1037] "Suggest the perfect food pairing for red wine. Users feel positive."

[1038] This system enables personalized drink and food pairing suggestions that take into account the user's emotional state, and also enables real-time suggestions using virtual stores and smart glasses, improving the quality of the user experience.

[1039] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1040] Step 1:

[1041] The user puts on the smart glasses and inputs information about the type of drink or meal, for example, "red wine," using voice input or touch operation. At this time, the microphone in the smart glasses detects the user's voice and captures the drink information as input data.

[1042] Step 2:

[1043] A facial recognition camera built into the smart glasses captures the user's facial expressions. The facial recognition camera captures the user's face as video data and sends it to an emotion analysis engine. The emotion analysis engine (e.g., OpenFace or Microsoft Azure Emotion API) analyzes the video data and identifies the user's emotional state (e.g., "positive" or "negative").

[1044] Step 3:

[1045] The acquired beverage information and the analysis results of the emotional state are sent to the server in JSON format. The transmission means of the smart glasses processes the input data and sends it to the server via the network. The transmitted data includes beverage information (e.g., "red wine") and the emotional state (e.g., "positive").

[1046] Step 4:

[1047] The server analyzes the received data and uses a generative AI model to calculate the optimal food and drink pairing based on the beverage information and emotional state. The server-side AI model (e.g., scikit-learn or TensorFlow) performs calculations based on the received data to estimate the optimal beverage and food pairing, utilizing past data and pre-trained models.

[1048] Step 5:

[1049] The server adjusts the pairing suggestions generated by the AI ​​model based on the emotional state. For example, if the emotional state is negative, the server might suggest "steak" instead of "red wine." This adjustment is performed by a server-side adjustment mechanism.

[1050] Step 6:

[1051] The server then returns the adjusted recommendation results in JSON format to the smart glasses. The server then compiles the recommendation results as output data and sends it to the smart glasses via the network. The transmitted data includes the recommendation result (e.g., "The best food to go with red wine is steak").

[1052] Step 7:

[1053] The display means of the smart glasses displays the received recommendation results to the user. The display of the smart glasses interprets the received data and displays the recommendation results in the user's field of view. The user can view the recommendation results in real time and improve their shopping experience in the virtual store.

[1054] Through the above steps, a system is realized that takes into account the user's emotional state and suggests optimal drink and meal combinations.

[1055] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1056] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1057] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1058] [Fourth embodiment]

[1059] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1060] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1061] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1062] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1063] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1064] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1065] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1066] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1067] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1068] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1069] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1070] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1071] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1072] The present invention provides a system that allows a user to input information about a type of beverage or a meal and, based on that information, suggests optimal pairings (marriage). The system includes an input unit, a transmission unit, a suggestion unit, and a display unit.

[1073] User Operation

[1074] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[1075] Sending data

[1076] Next, the terminal transmits the input data to the server, and the transmission means converts the input information into JSON format and transmits it to the server via the network.

[1077] Data Receipt and Processing

[1078] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine," the AI ​​model will suggest that "steak" is the best pairing. This calculation utilizes past data and trained algorithms.

[1079] Displaying the results

[1080] After the server generates the calculation result, it sends it back to the device. The device analyzes the result and displays it to the user. For example, it may display the result in the form of "Suggested combination: Steak." This display allows the user to easily find the best drink and food combination.

[1081] Specific examples

[1082] 1. The user enters "red wine" on the terminal and submits the form.

[1083] 2. The device sends the input data to the server.

[1084] 3. The server accepts the data and processes it using the AI ​​model.

[1085] 4. The server generates the result "The best food to go with red wine is steak."

[1086] 5. The server sends the result back to the terminal, which displays "Suggested combination: steak" to the user.

[1087] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[1088] The processing flow will be explained below.

[1089] Step 1:

[1090] The user opens a form on the terminal and enters information about a type of drink or meal, for example, "red wine" or "steak."

[1091] Step 2:

[1092] The device captures the information entered by the user and stores it as a data structure (e.g., a JavaScript object).

[1093] Step 3:

[1094] The device serializes the stored data into JSON format and prepares it for transmission, specifically creating a JSON object containing the input drink or meal name.

[1095] Step 4:

[1096] The JSON data generated by the device is sent to the server via the network using an HTTP request.

[1097] Step 5:

[1098] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[1099] Step 6:

[1100] The server analyzes the acquired JSON data and extracts information about the type of drink and meal.

[1101] Step 7:

[1102] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal pairing. Specifically, the AI ​​model calculates the optimal food pairing for each type of beverage.

[1103] Step 8:

[1104] The server composes the proposal results obtained from the AI ​​model in JSON format and prepares them as a response.

[1105] Step 9:

[1106] The server sends the constructed JSON response to the device.

[1107] Step 10:

[1108] The device parses the JSON response received from the server and extracts the proposed combination information.

[1109] Step 11:

[1110] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" on the screen.

[1111] Step 12:

[1112] The user checks the suggested results and enjoys the optimal drink and meal combination.

[1113] Example 1

[1114] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1115] Systems that suggest optimal drink and food pairings (marriage) have traditionally required specialized knowledge and have been difficult for average users to use. Furthermore, there has been a lack of technological solutions that can effectively process input data and make prompt and accurate recommendations. For this reason, there has been a demand for a system that allows users to easily enjoy optimal drink and food pairings.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1117] In this invention, the server includes an input device for a user to input information about the type of drink or meal, a transmission device for transmitting the input information to the server, a processing device that receives the input information at the server and calculates the optimal drink and meal combination using an AI model, and a display device that displays the proposal results generated by the processing device to the user. This makes it possible to calculate the optimal drink and meal combination using the AI ​​model and provide the result to the user quickly and accurately.

[1118] An "input device" is a device that allows a user to input information about a type of drink or a meal.

[1119] The "transmitting device" is a device for transmitting information input by a terminal to a server.

[1120] A "server" is a computer system that performs processing based on received information, generates results, and returns them.

[1121] "Processing device" means a device that receives data entered into the server, processes the data using an AI model, and calculates the optimal combination of beverages and meals.

[1122] An "AI model" is an algorithm or program that uses past data and trained algorithms to calculate the optimal combination based on input beverage and meal information.

[1123] A "display device" is a device or interface for visually displaying to a user the suggestion results generated by the processing device.

[1124] The present invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input device, a transmission device, a processing device, and a display device. Specific embodiments of this system are described below.

[1125] Hardware and Software Configuration

[1126] Users access the form through a web browser on a device (e.g., a PC, smartphone, or tablet) and the form is created using HTML, CSS, and JavaScript and is displayed on the device.

[1127] Input Devices

[1128] The user inputs information about the type of drink or meal, such as "red wine" or "steak," into the form. The input device is a keyboard or a touch screen.

[1129] Transmitting device

[1130] The terminal uses JavaScript to convert the input data into JSON format, which allows the data to be structured and efficiently transmitted over the network, and then sends the converted JSON data to the server as an HTTP POST request using AJAX technology.

[1131] Processing equipment

[1132] The server uses a backend framework (e.g., PHP, Node.js) to receive data sent from the device. The received data is parsed using Python libraries. The parsed data is then fed into a generative AI model to calculate the optimal drink and food pairing. The AI ​​model performs its calculations using historical data and trained algorithms.

[1133] display device

[1134] After the server generates the calculation results, it sends them back to the device as a JSON-formatted response. The device uses an AJAX response handler to parse the result data received from the server. The parsed data is visually displayed to the user using HTML and CSS. Specifically, it is displayed on the screen in the form of "Suggested combination: steak."

[1135] Specific examples

[1136] 1. User action: The user enters "red wine" on the terminal and submits the form.

[1137] 2. Sending data: The device converts the input data into JSON format and sends it to the server as an HTTP POST request.

[1138] 3. Data reception and processing: The server receives the JSON data and passes it to the AI ​​model using a Python library. The AI ​​model uses past data and algorithms to calculate the best food for red wine: steak.

[1139] 4. Generate and send results: The server sends the results obtained from the AI ​​model back to the device as a JSON response.

[1140] 5. Display the result: The device uses the AJAX response handler to parse the JSON data and displays "Suggested combination: steak" on the screen.

[1141] Example prompts for generative AI models

[1142] User entered "red wine". Please suggest the best meal.

[1143] In this way, the present invention can realize a system that can suggest optimal drink and food pairings in a format that is easy for general users to use, allowing users to enjoy drink and food pairings without requiring specialized knowledge.

[1144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1145] Step 1:

[1146] User action: The user opens a browser on their device and accesses the specified URL to display the form.

[1147] What happens: The user enters drink or meal information into the form fields and clicks the submit button.

[1148] Input: The data a user enters into a form (e.g., "red wine" or "steak")

[1149] Output: The data entered by the user is ready to be sent.

[1150] Step 2:

[1151] Terminal operation: The terminal takes the user's input data and converts it into JSON format for transmission.

[1152] Specific behavior: Uses JavaScript to get input data and generate it as a JSON object.

[1153] Input: Data entered by the user (e.g., "red wine")

[1154] Output: Data converted to JSON format (e.g. {'drink': 'red wine'})

[1155] Step 3:

[1156] Terminal operation: Data converted to JSON format is sent to the server.

[1157] What it does: It uses AJAX techniques to send data to the server as an HTTP POST request.

[1158] Input: JSON format data (e.g. {'drink': 'red wine'})

[1159] Output: Input data sent to the server

[1160] Step 4:

[1161] Server operation: The server receives the JSON data sent from the device.

[1162] What it does: Receives requests using a backend framework (e.g., PHP, Node.js).

[1163] Input: JSON data sent from the terminal (e.g., {'drink': 'red wine'})

[1164] Output: The state in which the received data is prepared for analysis

[1165] Step 5:

[1166] Server operation: The server analyzes the received data and passes it to the AI ​​model.

[1167] What it does: Uses Python libraries to parse data and feed it into an AI model.

[1168] Input: Received JSON data (e.g. {'drink': 'red wine'})

[1169] Output: Data input to the AI ​​model (e.g., data after format conversion)

[1170] Step 6:

[1171] Server operation: The AI ​​model calculates the optimal combination based on the input drink and meal information.

[1172] What it does: A generative AI model uses past data and trained algorithms to make calculations.

[1173] Input: Data fed into the AI ​​model (e.g., "red wine")

[1174] Output: Calculation result (e.g. "steak")

[1175] Step 7:

[1176] Server operation: The server generates a response based on the calculation results obtained from the AI ​​model and formats it in JSON format.

[1177] Specific behavior: Converts the calculation result into a JSON object.

[1178] Input: The calculation result from the AI ​​model (e.g., "steak")

[1179] Output: Response data in JSON format (e.g., {'suggestion': 'steak'})

[1180] Step 8:

[1181] Server operation: Returns the formatted data to the terminal.

[1182] Specific behavior: Sends JSON data as an HTTP response.

[1183] Input: JSON format response data (e.g., {'suggestion': 'steak'})

[1184] Output: Data sent to the terminal

[1185] Step 9:

[1186] Terminal operation: The terminal analyzes the result data received from the server.

[1187] Specific behavior: Receives and parses JSON data using the AJAX response handler.

[1188] Input: JSON data returned from the server (e.g., {'suggestion': 'steak'})

[1189] Output: Parsed data (e.g. "steak")

[1190] Step 10:

[1191] Terminal operation: The analyzed data is visually displayed to the user.

[1192] What it does: Displays the results on the screen using HTML and CSS.

[1193] Input: Parsed data (e.g. "steak")

[1194] Output: The suggested result displayed to the user (e.g., "Suggested combination: steak")

[1195] This process allows the user to easily find the best drink and meal combination.

[1196] (Application example 1)

[1197] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1198] In modern society, optimal drink and food pairings (marriage) are an important factor in enhancing dining experiences. However, it is difficult for individual users to determine the optimal drink and food pairing, requiring a great deal of time and knowledge. Furthermore, restaurants and food delivery services require a high level of specialized knowledge to provide satisfactory suggestions to users. This invention aims to solve these problems and provide a system that allows users to easily find the optimal pairing.

[1199] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1200] In this invention, the server includes means for converting user input into JSON format and sending it to the server via a network, means for the server to perform calculations based on the received data and return the optimal combination to the user's smartphone, and means for displaying the generated recommendation results on the user's smartphone and enabling the user to order the recommended drink and meal all at once. This enables users to quickly find the optimal drink and meal combination and easily order without specialized knowledge.

[1201] The "input means" is a means for the user to input information about the type of drink or meal.

[1202] The "transmitting means" is a means for transmitting input information to the processing device.

[1203] The "suggestion means" is a means for receiving information inputted into the processing device and proposing combinations of drinks and meals.

[1204] The "display means" is a means for displaying the proposal results generated by the proposal means to the user.

[1205] An "artificial intelligence model" is a model for calculating optimal beverage and food pairings, using data and algorithms to make predictions and recommendations.

[1206] The "JSON format" is a lightweight data exchange format that expresses data in text format and exchanges structured data.

[1207] A "network" is a collection of communication means for exchanging information, including the Internet and local area networks (LANs).

[1208] A "smartphone" is a portable communication terminal that has not only telephone functions but also the ability to connect to the Internet and run various applications.

[1209] MODE FOR CARRYING OUT THE INVENTION

[1210] This invention is a system that, when a user inputs information about the type of drink and the food, suggests the optimal combination (marriage) based on that information. The specific configuration and processing of this system are described below.

[1211] System configuration

[1212] The system consists of the following main components:

[1213] 1. Input means: A means for the user to input drink type or meal information. This may include a smartphone app or web interface.

[1214] 2. Transmission means: A means for transmitting the input information to the server. Here, the data is converted into JSON format and sent to the server via the Internet.

[1215] 3. Recommendation means: A means for suggesting optimal drink and meal combinations based on the data received by the server. Here, an artificial intelligence model is used.

[1216] 4. Display means: A means for displaying the proposed results to the user. A smartphone or other display device is used.

[1217] Processing flow

[1218] 1. User Input: The user opens the smartphone app and inputs information about their preferred beverage or food, for example, "red wine."

[1219] 2. Data transmission: The entered information is converted into JSON format and sent to the server via the network.

[1220] 3. Processing on the server: The server analyzes the received data and uses an artificial intelligence model to calculate the best food or drink pairing. For example, it might suggest "steak" as the best food to pair with red wine.

[1221] 4. Returning the results: The server converts the calculation results into JSON format and returns them to the user's smartphone.

[1222] 5. Displaying the results: The user's smartphone analyzes the received data and displays the results on the screen, for example, "Suggested combination: Steak."

[1223] Hardware and software used

[1224] Smartphone: A device for user input and display of results.

[1225] Server: Receives and processes data, often using a web framework such as Flask.

[1226] Artificial intelligence model: A model for calculating optimal combinations. It is built using libraries such as TensorFlow and PyTorch.

[1227] JSON format: A data interchange format.

[1228] Specific examples

[1229] 1. The user enters "red wine" into the smartphone app and presses the send button.

[1230] 2. The data is sent to the server, which calculates the best meal to match the "red wine."

[1231] 3. The server returns the calculation result, "steak."

[1232] 4. The user's smartphone will display "Suggested combination: Steak."

[1233] Prompt Sentence Examples

[1234] Please suggest the best food pairings for the following beverages:

[1235] Drink: Red wine

[1236] This invention allows users to easily enjoy optimal drink and food pairings without any specialized knowledge.

[1237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1238] Step 1:

[1239] The user inputs information about the type of drink or meal through the input means of the terminal. For example, the user inputs the type of drink, "red wine." At this time, the user's input is entered into the application form, and the application is ready for the next process.

[1240] Input: User enters "red wine"

[1241] Output: User input data (e.g. red wine)

[1242] Step 2:

[1243] The device converts the input information into JSON format and sends it to the server via the network. Specifically, the sending means sends an HTTP POST request to the URL endpoint, passing the user's input data to the server.

[1244] Input: User-entered data (e.g., red wine)

[1245] Output: JSON format data (e.g. {"drink": "red wine"})

[1246] Step 3:

[1247] The server parses the JSON data received over the network and converts it into an internal data structure. Based on this data, an artificial intelligence model calculates the optimal meal combination. Here, data calculations are performed using a generative AI model.

[1248] Input: JSON format data (e.g., {"drink": "red wine"})

[1249] Output: Optimal food combination (e.g. steak)

[1250] Step 4:

[1251] The server converts the calculated optimal combination into JSON format and returns it to the device as an HTTP response. The server generates data containing the proposal results and stores it in the response body.

[1252] Input: Optimal food pairing (e.g. steak)

[1253] Output: Response data in JSON format (e.g. {"meal": "steak"})

[1254] Step 5:

[1255] The device parses the JSON format data received from the server and converts it into a format that can be visually presented to the user. Specifically, the data is passed to a UI component that displays the results, and the results are displayed on the user's screen.

[1256] Input: JSON format response data (e.g. {"meal": "steak"})

[1257] Output: User-visible suggestion results (e.g., "Suggested combination: steak")

[1258] Specific examples of operation

[1259] Example prompt (text format):

[1260] Please suggest the best food pairings for the following beverages:

[1261] Drink: Red wine

[1262] In this way, having clear inputs and outputs for each step makes it easier to understand the processing flow of the entire system, and enables the construction of a system that responds quickly and accurately to user requests.

[1263] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1264] The present invention is a system that allows a user to input information about a type of beverage or a meal and then suggests optimal pairings (marriage) based on that information. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[1265] User Operation

[1266] The user first inputs information about the type of drink or meal using a form displayed on the terminal. For example, the user inputs the type of drink, "red wine," or the meal, "steak."

[1267] Emotion analysis using an emotion engine

[1268] Before the input data is sent, the device uses an emotion engine to analyze the user's input to determine whether they are in a "positive" or "negative" mood, for example, by analyzing the entered text and the user's facial expressions.

[1269] Sending data

[1270] Next, the terminal transmits the user's input data and the result of the emotion analysis to the server. The transmission means converts the input information and the result of the emotion analysis into JSON format and transmits them to the server via the network.

[1271] Data Receipt and Processing

[1272] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. For example, if the input beverage is "red wine" and the user's emotion is "positive," the AI ​​model will suggest that "steak" is the optimal pairing. This calculation utilizes past data and a trained algorithm.

[1273] Adjusting and displaying results

[1274] After the server generates the calculation results, it adjusts the recommendations accordingly based on the results of the sentiment analysis. For example, if the user is in a negative mood, it might recommend a combination of red wine and chocolate. The adjusted results are then sent back to the device.

[1275] Specific examples

[1276] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[1277] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's mood is "positive."

[1278] 3. The device sends the input data and the results of emotion analysis to the server.

[1279] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[1280] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[1281] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[1282] In this way, the present invention realizes a system that can suggest optimal drink and food pairings in a way that is easy for even general users to use. By combining it with an emotion engine, more personalized suggestions that match the user's mood become possible, improving the user experience.

[1283] The processing flow will be explained below.

[1284] Step 1:

[1285] The user opens a form on the terminal and enters the type of drink or meal information, for example, "red wine" or "steak."

[1286] Step 2:

[1287] The user clicks the form submit button, which causes the device to temporarily store the entered data.

[1288] Step 3:

[1289] The emotion engine analyzes the user's emotions based on the data stored on the device. The emotion engine uses text input and facial recognition data to determine the user's emotional state (positive, negative, etc.).

[1290] Step 4:

[1291] The device combines the user's sentiment analysis results and input data into a single JSON object, which includes the drink type, meal information, and emotional state.

[1292] Step 5:

[1293] The JSON object generated by the terminal is sent to the server using an HTTP request.

[1294] Step 6:

[1295] The server receives the HTTP request sent from the client and retrieves the JSON data from the request body.

[1296] Step 7:

[1297] The server analyzes the JSON data it receives and extracts the type of drink, meal information, and the user's emotional state.

[1298] Step 8:

[1299] Based on the information extracted by the server, the data is input into an AI model to calculate the optimal food pairing. Specifically, the AI ​​model calculates the optimal food pairing taking into account the type of beverage and the user's emotional state.

[1300] Step 9:

[1301] The server composes the recommendation results obtained from the AI ​​model in JSON format and prepares them as a response. For example, if the emotional state is "positive," the recommendation result will be "steak," and if the emotional state is "negative," the recommendation result will be "chocolate."

[1302] Step 10:

[1303] The server sends the constructed JSON response to the device.

[1304] Step 11:

[1305] The device parses the JSON response received from the server and extracts the proposed combination information.

[1306] Step 12:

[1307] The device visually displays the extracted suggestions to the user, such as "Suggested combination: steak" or "Suggested combination: chocolate" on the screen.

[1308] Step 13:

[1309] The user checks the suggested results and enjoys the optimal drink and meal combination.

[1310] Example 2

[1311] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1312] Conventional systems that suggest drink and food pairings have the problem of not taking into account the user's emotional state, and therefore not providing suggestions that match the user's mood. This can lead to low user satisfaction and convenience. Furthermore, the level of personalization of suggestions is low, and necessary information may not be provided appropriately.

[1313] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input information about the type of drink or meal, an emotion analysis means for analyzing the input information and the user's facial expression data using an emotion engine to identify the user's emotional state, and a transmission means for transmitting the analyzed emotional state and the input information to the processing device. This makes it possible to propose optimal combinations of drinks and meals that take the user's emotional state into consideration.

[1314] The "input means" is a means for the user to input information about the type of drink or meal.

[1315] The "emotion analysis means" is a means for analyzing input information and the user's facial expression data to identify the user's emotional state.

[1316] The "transmitting means" is a means for transmitting the analyzed emotional state and input information to the processing device.

[1317] The "suggestion means" is a means for receiving the emotional state analyzed by the processing device and input information, and proposing a combination of drink and food.

[1318] The "result adjustment means" is a means for adjusting the result of the suggestion means based on the emotional state.

[1319] The "display means" is a means for displaying the adjusted proposal results to the user.

[1320] An "AI model" is a model that uses machine learning algorithms to analyze data and calculate optimal drink and meal combinations.

[1321] An "emotion engine" is software or algorithms that analyze a user's facial expressions and text data to identify their emotional state.

[1322] "Processing device" means a device that receives user input data and sentiment analysis results and calculates and adjusts beverage and food pairings using an AI model.

[1323] A "network protocol" is a communication protocol for sending and receiving data, and is used to establish communication between a terminal and a server.

[1324] The present invention provides a system for suggesting optimal pairings (marriage) based on information about drink types or meals input by a user. The system includes an input unit, a transmission unit, a suggestion unit, a display unit, and an emotion engine.

[1325] First, the user enters information about the type of drink or meal using a form displayed on the device. For example, they can enter information such as "red wine" or "steak." HTML and JavaScript are used to input information into the form.

[1326] Next, the device analyzes the input information and the user's facial expression using an emotion engine. The emotion engine uses facial recognition APIs such as OpenFace and Affectiva to determine the user's emotional state. For example, if the user is expressing positive emotions, that information will be included in the analysis results.

[1327] The analyzed emotional state and input information are converted to JSON format and sent to the server using a network protocol (e.g., HTTP POST), typically using the JavaScript fetch API or XMLHttpRequest.

[1328] The server receives the data sent from the device and uses an AI model to calculate the optimal pairing. The AI ​​model uses TensorFlow and PyTorch, and performs calculations using past data and trained algorithms. The calculation results may be a recommendation such as "The best food to pair red wine with is steak."

[1329] Furthermore, the server adjusts the recommendation results based on the user's emotional state. For example, if the user is in a negative emotional state, the server will adjust the pairings based on the user's emotional state, such as red wine and chocolate. This adjustment is achieved using an emotion-based content filtering algorithm.

[1330] The adjusted results are converted back into JSON format and sent over the network to the device. The device parses the received results and displays them in an appropriate format for the user. For example, it displays "Suggested combination: Steak" on the screen using HTML and JavaScript.

[1331] As a specific example, the following operations can be considered.

[1332] 1. The user enters "red wine" on the terminal and submits the form. At this time, the user's facial expression is captured by the camera.

[1333] 2. The device analyzes the input data and facial expression data using an emotion engine and determines that the user's emotion is "positive."

[1334] 3. The device sends the input data and the results of emotion analysis to the server.

[1335] 4. The server receives the data and uses an AI model to calculate the optimal marriage.

[1336] 5. The server adjusts the result "The best food for red wine is steak" taking into account the results of sentiment analysis.

[1337] 6. The server sends the adjusted result back to the terminal, which displays "Suggested combination: steak" to the user.

[1338] Examples of prompts include:

[1339] 1. "Please suggest the best food to pair with red wine. I'm feeling positive."

[1340] 2. "For a user who is in a negative mood, suggest the best drink to pair with steak."

[1341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1342] Step 1: User Input

[1343] The user uses a form displayed on the device to input information about the type of drink or meal. For example, they enter information such as "red wine" or "steak" into the text box and press the "Submit" button. The input information is temporarily stored in the device's memory. An HTML form and JavaScript are used for the actual input. The input data is used in the next step.

[1344] Step 2: Sentiment Analysis

[1345] The device receives information entered by the user and the user's facial expression data and analyzes it using an emotion engine. The emotion engine uses an expression recognition API (e.g., OpenFace or Affectiva) to identify the user's emotional state. For example, it analyzes entered text and captured facial expression data to determine whether the user is in a "positive" or "negative" emotional state. The analysis results of the emotional state are stored on the device.

[1346] Step 3: Send data

[1347] The device converts the emotion analysis results and input information into JSON format. This JSON data is sent to the server via a network protocol (e.g., HTTP POST). The data is sent to the server using JavaScript's fetch API or XMLHttpRequest. The specific input data is the type of drink (e.g., "red wine") and the emotional state (e.g., "positive"), and is sent as a JSON object:

[1348] json

[1349] {

[1350] "drink": "red wine",

[1351] "mood": "positive"

[1352] }

[1353] Step 4: Marriage calculation

[1354] The server receives the JSON data sent from the device and begins processing. The server uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal drink and meal pairing. For example, if the drink is "red wine" and the user's sentiment is "positive," the server generates a calculation result suggesting "steak" as the optimal combination. This calculation utilizes past data and a trained algorithm. The output is the proposed combination (e.g., "steak").

[1355] Step 5: Adjust the results

[1356] The server receives the calculated recommendation results and makes adjustments based on the results of sentiment analysis. For example, if the user is in a "negative" emotional state, the server may determine that "red wine" and "chocolate" are a good combination. Such adjustments are made using an emotion-based content filtering algorithm. The adjusted recommendation results are converted back to JSON format and sent back to the device as output.

[1357] Step 6: View the results

[1358] The device analyzes the adjusted proposal results received from the server and displays them appropriately to the user. HTML and JavaScript are used for display. Specifically, a message such as "Suggested combination: Steak" is displayed on the screen. The display results are then made available to the user, allowing them to refer to the proposed drink and food combinations.

[1359] (Application example 2)

[1360] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1361] Conventional drink and food pairing recommendation systems have difficulty making personalized recommendations that take into account the user's emotional state. Furthermore, the lack of real-time recommendations using virtual stores or smart glasses has led to a decline in the quality of the user experience. Furthermore, there has been a lack of a means to make more appropriate recommendations based on the user's emotions.

[1362] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1363] In this invention, the server includes an input means for a user to input information about a type of drink or a meal, a transmission means for transmitting the input information to a processing device, a suggestion means for receiving the input information at the processing device and suggesting drink and meal combinations, the suggestion means including an adjustment means for adjusting the combinations based on an emotion analysis means for analyzing the user's emotional state, and a display means for displaying the suggestion results generated by the suggestion means to the user. This enables personalized drink and meal combination suggestions that take the user's emotional state into consideration. Furthermore, real-time suggestions can be made using a virtual store or smart glasses, improving the quality of the user experience.

[1364] "Input means" refers to a device or function that allows the user to input information about the type of drink or meal.

[1365] The "transmission means" refers to a function or method for transmitting input information to a processing device.

[1366] The "suggestion means" refers to a function or algorithm that receives information input into the processing device and suggests drink and meal combinations.

[1367] "Emotion analysis means" refers to software or a device for analyzing the user's emotional state, and is capable of, for example, facial expression recognition and voice analysis.

[1368] "Adjustment means" refers to a function or method for adjusting the proposal results based on sentiment analysis.

[1369] The "display means" refers to a device or method for displaying the proposal results generated by the proposal means to the user.

[1370] A "generative AI model" is an artificial intelligence model trained to calculate optimal drink and meal pairings.

[1371] This invention is a system that allows a user to input information about a type of beverage or a meal, and based on that information, suggests optimal pairings (marriage). This system includes an input means, a transmission means, a suggestion means, a display means, and an emotion analysis means. It also includes an adjustment means that adjusts the suggestion results based on the emotion analysis means. Specific embodiments are described below.

[1372] Hardware and software used

[1373] Smart glasses: facial recognition camera, microphone, display

[1374] Sentiment analysis engine: OpenFace, Microsoft Azure Emotion API

[1375] Data processing: Sending and receiving data in JSON format

[1376] Server: AI model (scikit-learn, TensorFlow, etc.)

[1377] System Operation

[1378] The user wears the smart glasses and inputs information about the type of drink and meal they want using voice or touch controls. The smart glasses' built-in facial recognition camera and microphone capture the user's facial expressions and voice tone, and an emotion analysis engine is used to analyze their emotional state.

[1379] The analysis results and input data are sent to the server in JSON format. The server uses an AI model to calculate the optimal drink and meal pairing. The results of the sentiment analysis are also taken into account, and the pairing results are adjusted accordingly. For example, if "red wine" and a "positive" emotional state are input, the server will suggest "steak."

[1380] The final, adjusted recommendations are displayed on the smart glasses' display, allowing users to see the recommendations in real time, enhancing the shopping experience in the virtual store.

[1381] Specific examples

[1382] Suppose a user verbally commands "red wine" into the smart glasses. The facial recognition camera captures the user's smile, and the emotion analysis engine determines this as "positive." This information is sent in JSON format to the server, where an AI model is used to calculate the optimal combination of "red wine" and "steak." The result is displayed on the smart glasses' display and suggested to the user.

[1383] Prompt Sentence Examples

[1384] "Suggest the perfect food pairing for red wine. Users feel positive."

[1385] This system enables personalized drink and food pairing suggestions that take into account the user's emotional state, and also enables real-time suggestions using virtual stores and smart glasses, improving the quality of the user experience.

[1386] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1387] Step 1:

[1388] The user puts on the smart glasses and inputs information about the type of drink or meal, for example, "red wine," using voice input or touch operation. At this time, the microphone in the smart glasses detects the user's voice and captures the drink information as input data.

[1389] Step 2:

[1390] A facial recognition camera built into the smart glasses captures the user's facial expressions. The facial recognition camera captures the user's face as video data and sends it to an emotion analysis engine. The emotion analysis engine (e.g., OpenFace or Microsoft Azure Emotion API) analyzes the video data and identifies the user's emotional state (e.g., "positive" or "negative").

[1391] Step 3:

[1392] The acquired beverage information and the analysis results of the emotional state are sent to the server in JSON format. The transmission means of the smart glasses processes the input data and sends it to the server via the network. The transmitted data includes beverage information (e.g., "red wine") and the emotional state (e.g., "positive").

[1393] Step 4:

[1394] The server analyzes the received data and uses a generative AI model to calculate the optimal food and drink pairing based on the beverage information and emotional state. The server-side AI model (e.g., scikit-learn or TensorFlow) performs calculations based on the received data to estimate the optimal beverage and food pairing, utilizing past data and pre-trained models.

[1395] Step 5:

[1396] The server adjusts the pairing suggestions generated by the AI ​​model based on the emotional state. For example, if the emotional state is negative, the server might suggest "steak" instead of "red wine." This adjustment is performed by a server-side adjustment mechanism.

[1397] Step 6:

[1398] The server then returns the adjusted recommendation results in JSON format to the smart glasses. The server then compiles the recommendation results as output data and sends it to the smart glasses via the network. The transmitted data includes the recommendation result (e.g., "The best food to go with red wine is steak").

[1399] Step 7:

[1400] The display means of the smart glasses displays the received recommendation results to the user. The display of the smart glasses interprets the received data and displays the recommendation results in the user's field of view. The user can view the recommendation results in real time and improve their shopping experience in the virtual store.

[1401] Through the above steps, a system is realized that takes into account the user's emotional state and suggests optimal drink and meal combinations.

[1402] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1403] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1404] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1405] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1406] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1407] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1408] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1409] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1410] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1411] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1412] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1413] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1414] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1415] 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.

[1416] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1417] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1418] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1419] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1420] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1421] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1422] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1423] The following is further disclosed regarding the above embodiment.

[1424] (Claim 1)

[1425] an input means for a user to input information about a type of drink or a meal;

[1426] a transmitting means for transmitting the input information to the processing device;

[1427] suggestion means for receiving information input by the processing device and suggesting beverage and meal combinations;

[1428] a display means for displaying the proposal result generated by the proposal means to the user;

[1429] A system including:

[1430] (Claim 2)

[1431] 2. The system of claim 1, wherein the suggestion means uses an AI model to calculate optimal meal combinations based on the input beverage types.

[1432] (Claim 3)

[1433] 2. The system of claim 1, wherein the suggestion means uses an AI model to calculate the optimal beverage type based on the input dietary information.

[1434] "Example 1"

[1435] (Claim 1)

[1436] an input device for a user to input information about a type of drink or a meal;

[1437] a transmitting device that transmits the input information to a server;

[1438] a processing device that receives the input information from the server and calculates the optimal combination of beverages and meals using an AI model;

[1439] a display device that displays the proposal results generated by the processing device to a user;

[1440] A system including:

[1441] (Claim 2)

[1442] 10. The system of claim 1, wherein the processing unit uses an AI model to calculate optimal meal combinations based on input beverage types.

[1443] (Claim 3)

[1444] 10. The system of claim 1, wherein the processing device uses an AI model to calculate the optimal beverage type based on the input dietary information.

[1445] "Application Example 1"

[1446] (Claim 1)

[1447] an input means for a user to input information about a type of drink or a meal;

[1448] a transmitting means for transmitting the input information to the processing device;

[1449] suggestion means for receiving information input by the processing device and suggesting beverage and meal combinations;

[1450] a display means for displaying the proposal result generated by the proposal means to the user;

[1451] A system including:

[1452] (Claim 2)

[1453] 10. The system of claim 1, wherein the suggestion means uses an artificial intelligence model to calculate optimal meal combinations based on the input beverage types.

[1454] (Claim 3)

[1455] 2. The system of claim 1, wherein the suggestion means uses an artificial intelligence model to calculate the optimal beverage type based on the input dietary information.

[1456] (Claim 4)

[1457] The function to convert user input into JSON format and send it to the server via the network.

[1458] The system of claim 1, further comprising a function for the server to perform calculations based on the received data and return the optimal combination to the user's smartphone.

[1459] (Claim 5)

[1460] The system of claim 1 further comprising a function for displaying the generated recommendation results on a user's smartphone and enabling the user to order the recommended beverages and meals together.

[1461] "Example 2: Combining Emotion Engines"

[1462] (Claim 1)

[1463] an input means for a user to input information about a type of drink or a meal;

[1464] emotion analysis means for analyzing input information and user facial expression data using an emotion engine to identify the user's emotional state;

[1465] a transmitting means for transmitting the analyzed emotional state and the input information to the processing device;

[1466] suggestion means for receiving the emotional state analyzed by the processing device and the input information and suggesting drink and meal combinations;

[1467] a result adjusting means for adjusting a result of the suggestion by the suggestion means based on the emotional state;

[1468] a display means for displaying the adjusted proposal result to the user;

[1469] A system including:

[1470] (Claim 2)

[1471] 2. The system of claim 1, wherein the suggestion means and result adjustment means use an AI model to calculate optimal meal combinations based on input beverage types and emotional state.

[1472] (Claim 3)

[1473] 2. The system of claim 1, wherein the suggestion means and result adjustment means use an AI model to calculate the optimal beverage type based on the input dietary information and emotional state.

[1474] "Application example 2 when combining emotion engines"

[1475] (Claim 1)

[1476] an input means for a user to input information about a type of drink or a meal;

[1477] a transmitting means for transmitting the input information to the processing device;

[1478] suggestion means for receiving information input by the processing device and suggesting beverage and meal combinations;

[1479] The suggestion means includes an adjustment means for adjusting the combination based on an emotion analysis means for analyzing an emotional state of a user;

[1480] a display means for displaying the proposal result generated by the proposal means to the user;

[1481] A system including:

[1482] (Claim 2)

[1483] 2. The system of claim 1, wherein the suggestion means uses a generative AI model to calculate optimal meal combinations based on the input beverage types.

[1484] (Claim 3)

[1485] 2. The system of claim 1, wherein the suggestion means uses a generative AI model to calculate the optimal beverage type based on the input dietary information. [Explanation of symbols]

[1486] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for a user to input information about a type of drink or a meal; a transmitting means for transmitting the input information to the processing device; suggestion means for receiving information input by the processing device and suggesting beverage and meal combinations; a display means for displaying the proposal result generated by the proposal means to the user; A system including:

2. 2. The system of claim 1, wherein the suggestion means uses an AI model to calculate optimal meal combinations based on the input beverage types.

3. The system of claim 1, wherein the suggestion means uses an AI model to calculate the optimal beverage type based on the input dietary information.

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