System

Abstract

A system is provided.SOLUTION: A system comprising: terminal means for inputting a desire of a customer; communication means for transmitting the input desire to a server; generation means for generating an optimal cocktail based on the desire; transmission means for returning information including a recipe, an image of appearance, and a name of the generated cocktail to the terminal; and learning means for reflecting feedback information in the generation means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When creating cocktails, bartenders often struggle to come up with new, original cocktails. Furthermore, it can be difficult to suggest the right cocktail, especially when bartenders need ideas for new cocktails tailored to specific requests or events, when they want to serve cocktails with a unique appearance, when they don't remember the preferences of past customers, or when they can't think of a cocktail to enter in a cocktail competition. The present invention aims to solve these problems and provide a means for bartenders to serve cocktails efficiently and creatively. [Means for solving the problem]

[0005] The system of the present invention includes a terminal means for inputting customer preferences, a communication means for transmitting the input preferences to a server, a generation means for generating an optimal cocktail based on the preferences, a transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, and a learning means for incorporating feedback information into the generation means. This system can respond to customers' abstract requests and propose cocktails that suit individual preferences. Furthermore, by incorporating feedback, the system can automatically generate and serve cocktails that suit the bartender's characteristics and the customer's preferences the more it is used.

[0006] "Terminal Means" means an electronic device used to input your preferences and requests.

[0007] "Communication means" is a network function for transmitting and receiving data from the terminal means to the server.

[0008] The "generation means" is an algorithm or program for generating the optimal cocktail recipe, visual image, and name based on the input desired information.

[0009] The "transmission means" is a function for returning the generated cocktail information from the server to the terminal means.

[0010] The "learning means" is a function that uses feedback information to adjust the parameters of the algorithm or program of the generating means to improve performance.

[0011] The "feedback sending means" is a function for sending feedback from customers to the server.

[0012] The "database reference means" is a function for referencing a database that stores information necessary for generating the ingredients, appearance, and name of a beverage. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention functions as a support system for bartenders to create original cocktails. An embodiment of the system and a specific method of operation thereof will be described below.

[0035] This system is composed of a terminal operated by a user, a server, and a communication means. The user uses a terminal (e.g., a tablet or smartphone) to input customer requests. The requests may include the desired taste and appearance of the cocktail, the theme of the event, etc. The input request is sent to the server via the communication means.

[0036] When the server receives the request, it uses the generation means to generate the optimal cocktail. This generation means searches for information such as the ingredients, appearance, and name of the drink using the database reference means, and determines the recipe, appearance image, and name of the cocktail that suits the request. The generated cocktail information is then sent back to the terminal by the transmission means.

[0037] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[0038] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. This process enables the system to generate cocktails that suit the bartender's individuality and the customer's preferences the more it is used.

[0039] As a concrete example, consider the following scenario.

[0040] Example 1: Creating a cocktail based on a customer request

[0041] 1. The user uses a terminal to enter a request such as "I want a sweet and fruity cocktail" and clicks the send button.

[0042] 2. The device sends a request to the server.

[0043] 3. The server receives the request and uses the generation method to generate the optimal cocktail.

[0044] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[0045] Look: Tropical imagery garnished with pineapple slices and mint leaves

[0046] Name: "Tropical Paradise"

[0047] 4. The server returns information about the created cocktail to the terminal.

[0048] 5. The terminal displays the cocktail information (recipe, image, name) to the user.

[0049] 6. The user creates a cocktail and serves it to the customer.

[0050] 7. A customer gives feedback saying, "This cocktail is delicious."

[0051] 8. The user enters the feedback into the terminal and sends it to the server.

[0052] 9. The server uses the feedback information to adjust the algorithm of the generator.

[0053] In this way, the system supports the bartender's creativity and continuously evolves to provide the perfect original cocktail for each individual customer.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user uses the device to input the customer's wishes and requests, including taste, appearance, and the theme of the event. Once the input is complete, the user clicks the send button.

[0057] Step 2:

[0058] The terminal converts the request input by the user into a data structure (e.g., JSON format), and the converted data is sent to the server using a communication means.

[0059] Step 3:

[0060] The server receives the request data sent from the terminal, analyzes the request data, and generates the parameters required to be passed to the generating means.

[0061] Step 4:

[0062] A generation means of the server uses the analyzed parameters to generate an optimal cocktail recipe, visual image, and name, and the generation process uses a database reference means to extract information about the ingredients, visual appearance, and name of the drink.

[0063] Step 5:

[0064] The server compiles the generated cocktail information (recipe, image, name) into a data structure (e.g., JSON format) and sends this data to the terminal using a transmission means.

[0065] Step 6:

[0066] The terminal analyzes the cocktail information received from the server and displays it on the user interface. The user then creates a cocktail based on the displayed information.

[0067] Step 7:

[0068] The user serves the cocktails they have created to customers, collects their ratings and feedback, and enters the collected feedback into the terminal and clicks the send button.

[0069] Step 8:

[0070] The terminal converts the feedback information input by the user into a data structure (e.g., JSON format) and transmits it to the server using the feedback transmission means.

[0071] Step 9:

[0072] The server receives feedback information sent from the terminal, analyzes the feedback information, and adjusts the algorithm and parameters of the generating means using the learning means.

[0073] Step 10:

[0074] The server's learning means optimizes the algorithm and parameters based on the received feedback information, which allows for more accurate cocktails to be generated in subsequent cocktail generation processes.

[0075] Example 1

[0076] 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."

[0077] Currently, when bartenders create original cocktails, they lack an easy way to create optimal recipes that meet the tastes and requests of specific customers. Furthermore, there are no systems in place to efficiently incorporate customer feedback on cocktails they have served and improve the quality of future cocktails. As a result, bartenders spend more time and effort creating cocktails, and it is difficult to maintain consistent quality.

[0078] 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.

[0079] In this invention, the server includes a terminal means operated by a user, a communication means for transmitting customer requests input via the terminal means to the server, a creation means for creating an optimal cocktail based on the requests, a transmission means for returning information including the recipe, an image of the appearance, and the name of the created cocktail to the terminal, and a learning means for reflecting feedback information in the creation means. This enables bartenders to easily create original cocktails that meet customer requests and improve subsequent cocktail creations based on customer feedback on the cocktails they have provided.

[0080] The "terminal means" is a device operated by a user, specifically an electronic device such as a tablet or smartphone.

[0081] The "communication means" is a function for transmitting data input by the terminal means to the server, and uses a communication path such as the Internet or a local network.

[0082] The "generation means" refers to the function that enables the server to generate the optimal cocktail based on the user's request, and includes programs that use database references and AI algorithms.

[0083] The "transmission means" is a function that allows the server to return information about the cocktail created to the terminal means, and transmits data using the HTTP protocol or the like.

[0084] The "learning means" is a function that uses feedback information received by the server to adjust the algorithms and parameters of the generating means, including retraining the machine learning model.

[0085] The "feedback transmission means" is a function for transmitting customer feedback collected by the terminal means to the server, and transmits data using the communication means.

[0086] The "database reference means" is a function that allows the generation means to search for information such as the ingredients, appearance, and name of a beverage, and refers to a large number of cocktail information items stored in a database.

[0087] The "HTTP protocol" is a communication protocol for data communication between a terminal device and a server, and is a protocol primarily used for data exchange between a web browser and a server.

[0088] The "user interface" is a screen display function that allows the terminal means to display cocktail recipes, images, and names to the user, and is implemented using HTML, JavaScript, etc.

[0089] MODE FOR CARRYING OUT THE INVENTION

[0090] This invention is a support system for bartenders to create original cocktails, and is composed of a terminal operated by a user, a server, and communication means. This system creates and serves the optimal cocktail based on the customer's wishes.

[0091] Hardware and software used

[0092] Hardware:

[0093] Devices: tablets, smartphones, etc.

[0094] Server: High-performance computer, cloud server

[0095] software:

[0096] Database: Relational database such as MySQL

[0097] Machine learning libraries: TensorFlow, PyTorch

[0098] Communication protocol: HTTP

[0099] User interface: HTML, JavaScript

[0100] System Operation Overview

[0101] The user uses the terminal to input the customer's wishes and sends the request to the server. When the server receives the request, it uses the generation means to generate the optimal cocktail. The generation means uses the database reference means to search for information such as the beverage's ingredients, appearance, and name, and determines the cocktail recipe, appearance image, and name that suits the request. The server returns the generated cocktail information to the terminal, and the user creates a cocktail based on that information and serves it to the customer. After serving, the user enters customer feedback into the terminal and sends it to the server. The server reflects the feedback information in the learning means to improve the accuracy of cocktail generation from the next time onwards.

[0102] Specific data processing and calculation

[0103] 1. User request input

[0104] Users use a tablet or smartphone to enter their customer's preferences into a text box, such as "I'd like a sweet and fruity cocktail."

[0105] 2. Submitting a Request

[0106] The terminal sends the input request to the server as an HTTP request. The request body contains the customer's request.

[0107] 3. Data reference for cocktail creation

[0108] The server analyzes the received request and uses an AI algorithm implemented in Python to generate the optimal cocktail, referencing a MySQL database to search for information such as the drink's ingredients, appearance, and name.

[0109] 4. Returning cocktail information

[0110] The server returns the generated cocktail information in JSON format to the terminal, including the recipe, image of the drink, and name.

[0111] 5. Cocktail information display

[0112] The terminal displays the cocktail information received from the server on the user interface, allowing the user to check the recipe, image, and name on the screen.

[0113] 6. Collecting and Submitting Feedback

[0114] The user enters the customer's feedback into a text box and sends it to the server as an HTTP request. The feedback includes ratings on the taste and appearance of the cocktail.

[0115] 7. Tuning the algorithm through learning methods

[0116] The server analyzes the received feedback information and uses machine learning libraries (TensorFlow, PyTorch) to adjust the algorithms and parameters of the generation method, thereby improving the accuracy of future cocktail generation.

[0117] Specific examples

[0118] Examples of prompts include:

[0119] "I want a sweet and fruity cocktail."

[0120] "I want to make a refreshing citrus cocktail."

[0121] "Can you give me a recipe for an Instagrammable rose-scented cocktail?"

[0122] The above is a specific embodiment for carrying out the present invention. This system allows bartenders to easily create and serve original cocktails tailored to customer requests. Furthermore, the system continuously evolves using feedback, improving the accuracy of subsequent cocktail creations.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] The user uses the terminal to enter a request.

[0126] Specifically, a user opens an application on a tablet or smartphone and enters a prompt statement, such as "I want a sweet and fruity cocktail," into a text box.

[0127] Input: Customer's request (prompt text)

[0128] Output: The input request data

[0129] Step 2:

[0130] The device sends a request to the server.

[0131] Specifically, the terminal sends the input request data to the server in the form of an HTTP request, which uses HTTP as the protocol.

[0132] Input: The input request data

[0133] Output: Request data sent to the server

[0134] Step 3:

[0135] The server receives the request and uses the generation means to generate the optimal cocktail.

[0136] Specifically, the server analyzes the received request and activates the generation means (an AI algorithm implemented in Python). This algorithm uses a database reference means to search for information such as the drink's ingredients, appearance, and name. Based on this information, the generation means generates the cocktail's recipe, an image of its appearance, and a name.

[0137] Input: Request data sent to the server

[0138] Output: Generated cocktail information (recipe, image, name)

[0139] Step 4:

[0140] The server returns information about the cocktail created to the terminal.

[0141] Specifically, the server returns the generated cocktail information to the terminal in JSON format as an HTTP response.

[0142] Input: Generated cocktail information (recipe, image, name)

[0143] Output: Cocktail information (recipe, image, name) returned to the device

[0144] Step 5:

[0145] The terminal displays the cocktail information to the user.

[0146] Specifically, the device displays the received cocktail information on the user interface, allowing the user to create a cocktail based on the displayed recipe and image.

[0147] Input: Cocktail information (recipe, image, name) sent back to the device

[0148] Output: Cocktail information displayed in a user interface

[0149] Step 6:

[0150] The user creates a cocktail and serves it to the customer.

[0151] Specifically, the user gathers ingredients based on the recipe displayed on the screen and creates a cocktail. For example, mix 40ml of white rum, 20ml of coconut liqueur, 60ml of pineapple juice, and 10ml of grenadine syrup, and garnish with a pineapple slice and mint leaf. The finished cocktail is then served to the customer.

[0152] Input: Cocktail information displayed in the user interface

[0153] Output: Served cocktail

[0154] Step 7:

[0155] The user inputs the customer's feedback into the terminal and sends it to the server.

[0156] Specifically, the user enters the feedback received from the customer into the text box and clicks the send button. The terminal then sends the feedback data to the server as an HTTP request.

[0157] Input: Customer Feedback

[0158] Output: Feedback data sent to the server

[0159] Step 8:

[0160] The server uses the feedback information to adjust the algorithm of the generating means using the learning means.

[0161] Specifically, the server analyzes the received feedback information and adjusts the algorithm and parameters of the generation method using machine learning libraries (TensorFlow, PyTorch), thereby improving the accuracy of cocktail generation from the next time onwards.

[0162] Input: Feedback data sent to the server

[0163] Output: Adjusted algorithms and parameters

[0164] The above is a specific description of the processing flow of the program of this system.

[0165] (Application example 1)

[0166] 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."

[0167] Traditionally, bartenders faced the problem of having to spend time and effort creating original cocktails to meet the diverse needs of customers. Furthermore, there was a lack of effective ways to incorporate feedback on the cocktails they served, making it difficult to make continuous improvements. This made it difficult to improve customer satisfaction.

[0168] 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.

[0169] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences to the server, generation means for generating an optimal cocktail based on the preferences, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and generation means for generating cocktail information using a cocktail generation AI model based on customer preferences. This makes it possible to quickly provide optimal cocktails that meet a variety of customer preferences, and by effectively reflecting feedback information, it is possible to support the bartender's creative activities and improve customer satisfaction.

[0170] "Terminal means" means a device through which you input your preferences, including a smartphone, tablet, or other computer.

[0171] "Communication means" refers to a communication protocol or communication device for transmitting the input preferences to a server, and includes the Internet, Wi-Fi, Bluetooth, etc.

[0172] "Generator" refers to an algorithm or software module that generates an optimal cocktail based on the user's preferences, and determines the ingredients, appearance, and name of the drink.

[0173] "Transmission means" refers to the communication protocol or interface for returning information including the recipe, image of the appearance, and name of the generated cocktail to the terminal, and includes HTTP, HTTPS, etc.

[0174] The "learning means" refers to a machine learning algorithm or database that reflects feedback information in the generating means, thereby improving the generating capability of the system.

[0175] "Cocktail generation AI model" refers to an artificial intelligence model used to generate cocktail information based on customer preferences, including, for example, natural language processing models such as GPT-4.

[0176] A "prompt sentence" refers to an input sentence that the generation means uses to generate the cocktail recipe, appearance, and name, and is text that includes specific generation instructions.

[0177] The present invention functions as a support system for bartenders to create original cocktails in brick-and-mortar stores. This system includes a terminal (smartphone, tablet, etc.) operated by a user, a server, and communication means.

[0178] System program configuration

[0179] The server uses multiple means to achieve its functions. First, the user inputs their preference using a terminal. For example, they may request a "bitter cocktail with an adult feel." This request is then sent to the server via a communication means.

[0180] The server uses a cocktail generation AI model (e.g., GPT-4) to generate a prompt based on the input preference. The prompt is expressed as follows:

[0181] "Create a cocktail that has a bitter, grown-up vibe."

[0182] The server uses this prompt to generate a cocktail recipe, a visual image, and a name. The generated cocktail information specifically includes the ingredients, appearance, and name of the drink, for example:

[0183] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[0184] The look: A classic cocktail glass garnished with an orange peel.

[0185] Name: "Bitter Classic"

[0186] The information about the created cocktail is sent back to the terminal by the transmission means, and the terminal displays the information to the user, allowing the user to check the cocktail recipe and appearance and actually create the cocktail.

[0187] The user can serve the cocktails they have created to customers and collect their ratings and feedback. The user uses the terminal to send the collected feedback to the server via communication means.

[0188] The server reflects the feedback information in the learning means. The learning means analyzes the feedback information using a machine learning algorithm and adjusts the algorithm and parameters to improve performance in subsequent cocktail creations. As a result, the more the system is used, the more it supports the bartender's creativity and enables the system to create cocktails that suit the preferences of each individual customer.

[0189] Hardware and software used

[0190] Hardware: Smartphones, servers

[0191] Software: Smartphone app (iOS or Android app), server (Python-based backend), database (MySQL, etc.), AI generative model (GPT-4, etc.), communication method (HTTP / HTTPS)

[0192] Specific examples

[0193] If a customer requests a "bitter cocktail with an adult feel," the AI ​​model generates the following prompt:

[0194] "Create a cocktail that has a bitter, grown-up vibe."

[0195] The server then generates the following information:

[0196] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[0197] The look: A classic cocktail glass garnished with an orange peel.

[0198] Name: "Bitter Classic"

[0199] This allows bartenders to quickly create and serve cocktails that perfectly suit customers' preferences, and the system will learn and further optimize itself based on feedback, improving the quality of future cocktails.

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

[0201] Step 1:

[0202] The user uses a terminal to input their preferences, including taste, atmosphere, and event theme.

[0203] Input: Customer's preference (e.g., "A bitter cocktail with an adult feel")

[0204] Output: The desired data has been input.

[0205] Step 2:

[0206] The terminal transmits the input desired data to the server using the communication means.

[0207] Input: Desired data

[0208] Output: Send the desired data to the server

[0209] Specific operation: Generate an HTTP request from the device and send it to the server

[0210] Step 3:

[0211] The server analyzes the received preference data and generates a prompt for the cocktail generation AI model.

[0212] Input: Desired data

[0213] Output: Prompt (e.g., "Please create a bitter cocktail with a mature feel.")

[0214] Specific operation: Analyze the desired data and generate an appropriate prompt.

[0215] Step 4:

[0216] The server inputs the generated prompt into a cocktail generation AI model to generate the cocktail recipe, an image of its appearance, and a name.

[0217] Input: prompt statement

[0218] Output: Cocktail information (recipe, image, name)

[0219] Specific operation: Input a prompt sentence into the AI ​​model and obtain cocktail information generated by the AI ​​model.

[0220] Step 5:

[0221] The server returns the generated cocktail information to the terminal.

[0222] Input: Cocktail information

[0223] Output: Send cocktail information to the terminal

[0224] Specific operation: Generate an HTTP response from the server and send it to the device

[0225] Step 6:

[0226] The terminal displays cocktail information to the user, who then creates a cocktail based on this information.

[0227] Input: Cocktail information

[0228] Output: Cocktail information displayed to the user

[0229] Specific behavior: Display recipe, image, and name on device

[0230] Step 7:

[0231] Users serve cocktails to customers and collect ratings and feedback, which is entered via a terminal.

[0232] Input: Your feedback

[0233] Output: Feedback data is input complete

[0234] Step 8:

[0235] The terminal transmits the feedback data to the server using a communication means.

[0236] Input: Feedback data

[0237] Output: Send feedback data to the server

[0238] Specific operation: Generate an HTTP request from the device and send it to the server

[0239] Step 9:

[0240] The server reflects the received feedback data in the learning process and adjusts the algorithms and parameters of the cocktail generation AI model.

[0241] Input: Feedback data

[0242] Output: Algorithm and parameters of the tuned AI model

[0243] Specific behavior: Analyzes feedback data and executes learning algorithms

[0244] In this way, the system of the present invention can quickly create cocktails that meet the diverse desires of customers and continuously incorporate feedback to improve the quality of service.

[0245] 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.

[0246] The present invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. An embodiment of the system and its specific operation method are described below.

[0247] This system is composed of a terminal operated by the user, a server, a communication means, and an emotion engine. The user uses a terminal (e.g., a tablet or smartphone) to input a customer's request. As the user inputs, the emotion engine recognizes the user's emotion from their facial expression and voice. The customer's request may include the desired taste and appearance of the cocktail, the theme of the event, etc. After inputting and collecting the emotion information, the user clicks the send button.

[0248] The terminal converts the request input by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and transmits it to the server via a communication means.

[0249] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the cocktail recipe, image of appearance, and name that are appropriate for the request and emotional information. The transmission means then returns the generated cocktail information to the terminal.

[0250] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[0251] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance in subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[0252] As a concrete example, consider the following scenario.

[0253] Example 1: Cocktail generation based on customer requests and sentiment information

[0254] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[0255] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[0256] 3. The user submits the input and emotion information by clicking the submit button.

[0257] 4. The device sends the request data and emotion information to the server.

[0258] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[0259] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[0260] Look: Tropical imagery garnished with pineapple slices and mint leaves

[0261] Name: "Tropical Paradise"

[0262] 6. The server returns the information about the cocktail to the terminal.

[0263] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[0264] 8. The user creates a cocktail and serves it to the customer.

[0265] 9. A customer gives feedback saying, "This cocktail is delicious."

[0266] 10. The user enters the feedback into the terminal and clicks the send button.

[0267] 11. The device sends the feedback information to the server.

[0268] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[0269] In this way, the system can provide original cocktails creatively and effectively, taking into account the emotional information of the bartender and the customer.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] A user uses a terminal to input a cocktail request, including desired taste (e.g., "sweet and fruity"), desired appearance, and the theme of the event.

[0273] Step 2:

[0274] The emotion engine analyzes the user's facial expressions and voice to recognize emotional information. For example, emotions such as "joy" or "surprise" can be detected from the user's facial expressions.

[0275] Step 3:

[0276] The user completes the request and emotion information and clicks the send button.

[0277] Step 4:

[0278] The device converts the user's input and the recognized emotion information into a data structure (e.g., JSON format).

[0279] Step 5:

[0280] The terminal uses a communication means to transmit a request and emotion information to the server.

[0281] Step 6:

[0282] The server receives the request data and emotion information sent from the terminal.

[0283] Step 7:

[0284] The server's generating means analyzes the received request data and uses the database reference means to search for information on the ingredients, appearance, and name of the drink, taking into account emotional information to generate a cocktail that suits the user's psychological state.

[0285] Step 8:

[0286] The server generates cocktail information including the optimal cocktail recipe, visual image, and name.

[0287] Example: The recipe is 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup.

[0288] Tropical look with pineapple slices and mint leaves

[0289] It's called "Tropical Paradise"

[0290] Step 9:

[0291] The server returns the generated cocktail information to the terminal using the transmission means.

[0292] Step 10:

[0293] The terminal analyzes the cocktail information received from the server and displays it on the user interface.

[0294] Step 11:

[0295] The user creates a cocktail based on the cocktail information displayed on the terminal.

[0296] Step 12:

[0297] Serve user-created cocktails to customers and collect customer feedback.

[0298] Step 13:

[0299] The user enters the collected feedback into the terminal and clicks the send button.

[0300] Step 14:

[0301] The device converts the feedback information into a data structure (e.g., JSON format).

[0302] Step 15:

[0303] The terminal transmits feedback information to the server via the communication means.

[0304] Step 16:

[0305] The server analyzes the feedback information received using the feedback sending means.

[0306] Step 17:

[0307] The learning means of the server adjusts the algorithms and parameters of the generation means based on the received feedback information to improve performance.

[0308] Through these steps, the system creates and suggests optimal cocktails based on the user's requests and emotional information. Furthermore, by incorporating feedback, the more the system is used, the more it can provide cocktails that suit the bartender's characteristics and the customer's preferences.

[0309] Example 2

[0310] 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."

[0311] Conventional cocktail creation systems can suggest cocktails based on a customer's preferences, but they cannot consider the user's emotions. This makes it difficult to create cocktails that match the customer's psychological state, and there are also limited means to improve the system's performance by incorporating user feedback. The present invention aims to solve these problems and provide a system that can create more personalized cocktails.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0313] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences and emotional information to the server, generation means for generating an optimal cocktail based on the preferences and emotional information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and emotion recognition means for analyzing facial expressions and voice data of the customer and recognizing their emotions. This makes it possible to suggest cocktails that suit the customer's psychological state, and further improve the performance of the system based on the feedback information.

[0314] "Terminal means" refers to the device used to input the customer's wishes and requests, specifically a mobile information terminal such as a tablet or smartphone.

[0315] "Communication means" refers to the Internet or other network communication technology for transmitting input preferences, requests, and emotional information to the server.

[0316] "Generation means" refers to an algorithm or program for generating the optimal cocktail based on input desires and emotional information.

[0317] "Transmission means" refers to the system or technology for sending information about the created cocktail back to the terminal.

[0318] "Learning means" refers to a mechanism or method for adjusting the system's generation algorithm or parameters based on feedback information to improve the performance of cocktail generation from the next time onwards.

[0319] "Emotion recognition means" refers to the technology and algorithms used to analyze a customer's facial expressions and voice data and recognize their emotions.

[0320] "Feedback Transmission Means" refers to the method or technology used to collect Customer Feedback and transmit that information to the Server.

[0321] The term "database reference means" refers to a mechanism or method for referencing a database that holds information such as the ingredients, appearance, and name of beverages, and obtaining the required information.

[0322] The present invention is a support system for bartenders to create original cocktails, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest cocktails that suit the user's psychological state. This system is configured to include a terminal operated by the user, a server, communication means, and the emotion engine. Specific embodiments of the system and their operation methods are described below.

[0323] In this system, users use devices such as tablets or smartphones to input customer requests. Requests can include the desired taste and appearance of the cocktail, the theme of the event, and so on. As the request is input, the emotion engine recognizes emotions from the user's facial expressions and voice. For example, facial expression and voice data can be collected through a camera or microphone, and analyzed to identify the user's psychological state.

[0324] After collecting the input and emotion information, the user clicks the send button. The device converts the request entered by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and sends it to the server via a communication method. Specific communication technologies used include the Internet and Wi-Fi.

[0325] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines a cocktail recipe, appearance image, and name that are appropriate for the request and emotional information. For example, the generation means uses a pre-registered cocktail database to select a recipe that uses white rum, coconut liqueur, pineapple juice, and grenadine syrup.

[0326] The generated cocktail information is then sent back to the terminal by the sending means. The terminal displays the returned cocktail information on the user interface. The user then creates a cocktail based on the displayed cocktail information and serves it to the customer.

[0327] A feedback transmission means is used to collect customer ratings and feedback. The user inputs customer feedback into the terminal and clicks the send button. For example, the user inputs feedback such as "This cocktail is very delicious." The feedback information is again transmitted to the server via the communication means.

[0328] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[0329] As a concrete example, consider the following scenario.

[0330] Example 1: Cocktail generation based on customer requests and sentiment information

[0331] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[0332] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[0333] 3. The user submits the input and emotion information by clicking the submit button.

[0334] 4. The device sends the request data and emotion information to the server.

[0335] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[0336] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[0337] Look: Tropical imagery garnished with pineapple slices and mint leaves

[0338] Name: "Tropical Paradise"

[0339] 6. The server returns the information about the cocktail to the terminal.

[0340] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[0341] 8. The user creates a cocktail and serves it to the customer.

[0342] 9. A customer gives feedback saying, "This cocktail is delicious."

[0343] 10. The user enters the feedback into the terminal and clicks the send button.

[0344] 11. The device sends the feedback information to the server.

[0345] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[0346] This makes it possible to create cocktails based on the customer's emotional information and to gradually improve the system's performance.

[0347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0348] Program processing flow (processing steps)

[0349] Step 1:

[0350] The user enters a request

[0351] Users use devices such as tablets and smartphones to input customer requests, including desired cocktail flavors and appearances, and the theme of the event. The input information is stored in text format in the device's memory.

[0352] input:

[0353] Request information such as cocktail taste, appearance, event theme, etc.

[0354] output:

[0355] The input request information is saved in text format.

[0356] Step 2:

[0357] Emotion engine recognizes emotions

[0358] The emotion engine uses the device's built-in camera and microphone to collect the user's facial expressions and voice data, which is then analyzed by an algorithm to recognize the user's emotional state (e.g., "joy").

[0359] input:

[0360] The user's facial expression data and voice data.

[0361] output:

[0362] The analyzed emotional state (e.g., "joy").

[0363] Specific behavior:

[0364] The camera captures the user's face and the microphone records the user's voice, and the emotion engine analyzes this data to identify their emotional state.

[0365] Step 3:

[0366] Sending requests and emotional information

[0367] The user clicks the send button to send the input request and the recognized emotion information to the server. The device converts this information into JSON format and sends it to the server via a communication method.

[0368] input:

[0369] Input request information and recognized emotion information.

[0370] output:

[0371] The request information and emotion information converted into JSON format are sent to the server.

[0372] Specific behavior:

[0373] When the send button is clicked, the device converts the text request information and emotional state into JSON format and sends it to the server via the Internet.

[0374] Step 4:

[0375] The server analyzes the data

[0376] The server receives the request data and emotional information sent from the device, analyzes them, and then selects the optimal cocktail based on the analysis using a query-based algorithm.

[0377] input:

[0378] Request and sentiment information in JSON format.

[0379] output:

[0380] The analysis results include a list of cocktail candidates and information on the optimal cocktail.

[0381] Specific behavior:

[0382] The server analyzes the request information and emotional information, and selects the most suitable cocktail by referring to existing cocktail information in a database.

[0383] Step 5:

[0384] Cocktail Creation

[0385] The generating means uses the database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the recipe, appearance image, and name of the cocktail that is suitable for the request and emotional information.

[0386] input:

[0387] Cocktail information such as drink ingredients, appearance, and name retrieved from a database.

[0388] output:

[0389] The best cocktail recipes, images and names.

[0390] Specific behavior:

[0391] The generator queries the database and extracts the cocktail information that best matches the request and emotional information (e.g., 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup, name "Tropical Paradise", image).

[0392] Step 6:

[0393] Submit cocktail information

[0394] The server sends the generated cocktail information back to the terminal, which displays the received information on the user interface.

[0395] input:

[0396] The generated cocktail recipe, image, and name.

[0397] output:

[0398] The returned cocktail information will be displayed on the terminal.

[0399] Specific behavior:

[0400] The server sends the generated cocktail information back to the terminal, which receives the information and displays it on the user interface.

[0401] Step 7:

[0402] Cocktail creation and serving

[0403] The user actually creates a cocktail based on the displayed cocktail information and serves it to the customer.

[0404] input:

[0405] The cocktail recipe, image, and name are displayed on the device.

[0406] output:

[0407] Cocktails served to guests.

[0408] Specific behavior:

[0409] The user creates a cocktail according to the optimal cocktail recipe and serves it to the customer.

[0410] Step 8:

[0411] Collecting feedback

[0412] To collect customer ratings and feedback, the user enters the feedback into the terminal and clicks the submit button.

[0413] input:

[0414] Customer feedback information.

[0415] output:

[0416] The feedback information is stored on the device.

[0417] Specific behavior:

[0418] After the customer samples the cocktail, the user enters feedback into the terminal, such as "This cocktail is very delicious," and clicks the send button.

[0419] Step 9:

[0420] Give feedback and learn

[0421] The terminal again transmits the feedback information to the server via the communication means, and the server receives the feedback information and adjusts the algorithm and parameters of the generation means using the learning means.

[0422] input:

[0423] Customer feedback information.

[0424] output:

[0425] Adjusted generation algorithm.

[0426] Specific behavior:

[0427] The terminal transmits feedback information to the server, which receives it and adjusts the algorithms and parameters using a learning means.

[0428] (Application example 2)

[0429] 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."

[0430] Conventional cocktail creation systems did not adequately consider the customer's emotional state when suggesting drinks, leaving the user experience unsatisfactory. Furthermore, there was no mechanism for collecting feedback and continuously improving the system, making it difficult for bartenders to improve the quality of the cocktails they served. Furthermore, the lack of a real-time cocktail creation guide sometimes caused bartenders to struggle.

[0431] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes terminal means for inputting the user's preferences, communication means for transmitting the input preferences to the server, emotion recognition means for recognizing emotions from the user's facial expressions and voice, generation means for generating an optimal cocktail based on the preferences and emotion information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, and learning means for reflecting feedback information in the generation means. This enables the system to suggest optimal cocktails taking the user's emotion information into consideration, provide cocktail creation guidance in real time, and continuously improve the system based on feedback information.

[0432] "Terminal means" refers to a device for inputting customer preferences, including tablets and smartphones.

[0433] "Communication means" refers to a method for transmitting the input preference to the server, and includes internet communication, wireless communication, and the like.

[0434] "Emotion recognition means" refers to technology or devices that recognize emotions from the user's facial expressions and voice.

[0435] The "creation means" is a method or device for creating an optimal cocktail based on the desire and emotional information.

[0436] The "transmission means" refers to a method or device for returning information including the recipe, image of the appearance, and name of the created cocktail to the terminal.

[0437] The "learning means" is an algorithm or device for making the generating means reflect feedback information.

[0438] The "feedback transmission means" refers to a method or device for transmitting customer feedback collected by the terminal means to the server.

[0439] "Database reference means" refers to a method or device for referencing a database to generate the ingredients, appearance, and name of a beverage.

[0440] This invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. Below, an embodiment of the system and its specific operation method are described.

[0441] The system for realizing the present invention uses smartphones, tablets, smart glasses, and servers as hardware, and an emotion recognition engine, a cocktail database, and a communication platform (e.g., HTTP communication means) as software.

[0442] First, the user (bartender) inputs the customer's preferences using a terminal such as a smartphone or tablet. These preferences include the desired taste and appearance of the cocktail, the theme of the event, etc. Next, an emotion recognition tool is used to recognize the customer's emotions from their facial expressions and voice. The software uses face recognition technology and voice analysis technology to do this.

[0443] When the user presses the send button for the preference and emotion information, the device converts this information into JSON format and sends it to the server via the communication means. The server analyzes the received information and generates an optimal cocktail using the generation means. This generation means refers to a cocktail database and searches for information such as the drink's ingredients, appearance, and name. Once the optimal cocktail is generated, the server returns the recipe, an image of the appearance, and the name of the generated cocktail to the device via the transmission means.

[0444] The user creates a cocktail based on the information displayed on the terminal and serves it to the customer. The terminal collects the customer's reactions and feedback and sends them back to the server. The server then reflects this feedback information in the learning means and adjusts the algorithms and parameters of the generation means. This allows the system to improve its performance in subsequent cocktail generation.

[0445] For a concrete scenario, the following prompt sentences can be used:

[0446] If a user requests a "sweet and fruity cocktail" and the emotion engine recognizes "joy," the server will suggest a recipe for the perfect cocktail, "Tropical Paradise." The prompt text is entered as follows:

[0447] Generate the best cocktail based on the following desires and emotions: Desire: Sweet and fruity cocktail, Emotion: Joy

[0448] As described above, the present invention can provide original cocktails creatively and effectively while taking into consideration the emotional information of the bartender and the customer.

[0449] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0450] Step 1:

[0451] The user inputs their preferences using a terminal such as a smartphone or tablet. This input includes preferences for the taste and appearance of the cocktail, the theme of the event, etc. The input preferences are saved as text information on the terminal.

[0452] Step 2:

[0453] The terminal uses emotion recognition means to recognize emotions from the customer's facial expressions and voice. The emotion recognition means uses a camera and microphone to collect facial and voice data, which is then analyzed by the Emotion Engine. This allows emotions such as "happiness" and "sadness" to be recognized.

[0454] Step 3:

[0455] The user presses the send button to send their input and emotion information. At this time, the device converts their wishes and emotion information into JSON format and sends it to the server via a communication method. The input data is sent to the server as an HTTP request.

[0456] Step 4:

[0457] The server receives the request data and emotion information sent from the device, parses the received data, and extracts the desired content and emotion.

[0458] Step 5:

[0459] The server's generation means generates the optimal cocktail based on the received desired data and emotional information. The generation process involves referencing a cocktail database to search for the ingredients, appearance, and name of a drink that matches the criteria. For example, based on the data "sweet and fruity cocktail, emotion: joy," a cocktail called "Tropical Paradise" is selected.

[0460] Step 6:

[0461] The server sends information about the created cocktail (recipe, image, name) to the terminal via a transmission means. The query result is sent to the terminal in JSON format.

[0462] Step 7:

[0463] The terminal receives the cocktail information returned from the server and displays it on the user interface. At this time, the cocktail recipe, image of its appearance, and name are displayed on the screen. The user creates a cocktail based on this information.

[0464] Step 8:

[0465] The cocktail created by the user is served to the customer, and feedback such as "very delicious" is collected from the customer. The terminal means inputs and saves the feedback information as text data.

[0466] Step 9:

[0467] The feedback information collected by the terminal means is sent to the server using the feedback sending means. The feedback data is also sent to the server in JSON format.

[0468] Step 10:

[0469] The server analyzes the received feedback information and reflects it in the learning process. Based on the feedback data, the algorithm and parameters of the generation process are adjusted to improve the accuracy of cocktail generation from the next time onwards. This allows the system to improve with each use.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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."

[0486] The present invention functions as a support system for bartenders to create original cocktails. An embodiment of the system and a specific method of operation thereof will be described below.

[0487] This system is composed of a terminal operated by a user, a server, and a communication means. The user uses a terminal (e.g., a tablet or smartphone) to input customer requests. The requests may include the desired taste and appearance of the cocktail, the theme of the event, etc. The input request is sent to the server via the communication means.

[0488] When the server receives the request, it uses the generation means to generate the optimal cocktail. This generation means searches for information such as the ingredients, appearance, and name of the drink using the database reference means, and determines the recipe, appearance image, and name of the cocktail that suits the request. The generated cocktail information is then sent back to the terminal by the transmission means.

[0489] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[0490] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. This process enables the system to generate cocktails that suit the bartender's individuality and the customer's preferences the more it is used.

[0491] As a concrete example, consider the following scenario.

[0492] Example 1: Creating a cocktail based on a customer request

[0493] 1. The user uses a terminal to enter a request such as "I want a sweet and fruity cocktail" and clicks the send button.

[0494] 2. The device sends a request to the server.

[0495] 3. The server receives the request and uses the generation method to generate the optimal cocktail.

[0496] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[0497] Look: Tropical imagery garnished with pineapple slices and mint leaves

[0498] Name: "Tropical Paradise"

[0499] 4. The server returns information about the created cocktail to the terminal.

[0500] 5. The terminal displays the cocktail information (recipe, image, name) to the user.

[0501] 6. The user creates a cocktail and serves it to the customer.

[0502] 7. A customer gives feedback saying, "This cocktail is delicious."

[0503] 8. The user enters the feedback into the terminal and sends it to the server.

[0504] 9. The server uses the feedback information to adjust the algorithm of the generator.

[0505] In this way, the system supports the bartender's creativity and continuously evolves to provide the perfect original cocktail for each individual customer.

[0506] The processing flow will be explained below.

[0507] Step 1:

[0508] The user uses the device to input the customer's wishes and requests, including taste, appearance, and the theme of the event. Once the input is complete, the user clicks the send button.

[0509] Step 2:

[0510] The terminal converts the request input by the user into a data structure (e.g., JSON format), and the converted data is sent to the server using a communication means.

[0511] Step 3:

[0512] The server receives the request data sent from the terminal, analyzes the request data, and generates the parameters required to be passed to the generating means.

[0513] Step 4:

[0514] A generation means of the server uses the analyzed parameters to generate an optimal cocktail recipe, visual image, and name, and the generation process uses a database reference means to extract information about the ingredients, visual appearance, and name of the drink.

[0515] Step 5:

[0516] The server compiles the generated cocktail information (recipe, image, name) into a data structure (e.g., JSON format) and sends this data to the terminal using a transmission means.

[0517] Step 6:

[0518] The terminal analyzes the cocktail information received from the server and displays it on the user interface. The user then creates a cocktail based on the displayed information.

[0519] Step 7:

[0520] The user serves the cocktails they have created to customers, collects their ratings and feedback, and enters the collected feedback into the terminal and clicks the send button.

[0521] Step 8:

[0522] The terminal converts the feedback information input by the user into a data structure (e.g., JSON format) and transmits it to the server using the feedback transmission means.

[0523] Step 9:

[0524] The server receives feedback information sent from the terminal, analyzes the feedback information, and adjusts the algorithm and parameters of the generating means using the learning means.

[0525] Step 10:

[0526] The server's learning means optimizes the algorithm and parameters based on the received feedback information, which allows for more accurate cocktails to be generated in subsequent cocktail generation processes.

[0527] Example 1

[0528] 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."

[0529] Currently, when bartenders create original cocktails, they lack an easy way to create optimal recipes that meet the tastes and requests of specific customers. Furthermore, there are no systems in place to efficiently incorporate customer feedback on cocktails they have served and improve the quality of future cocktails. As a result, bartenders spend more time and effort creating cocktails, and it is difficult to maintain consistent quality.

[0530] 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.

[0531] In this invention, the server includes a terminal means operated by a user, a communication means for transmitting customer requests input via the terminal means to the server, a creation means for creating an optimal cocktail based on the requests, a transmission means for returning information including the recipe, an image of the appearance, and the name of the created cocktail to the terminal, and a learning means for reflecting feedback information in the creation means. This enables bartenders to easily create original cocktails that meet customer requests and improve subsequent cocktail creations based on customer feedback on the cocktails they have provided.

[0532] The "terminal means" is a device operated by a user, specifically an electronic device such as a tablet or smartphone.

[0533] The "communication means" is a function for transmitting data input by the terminal means to the server, and uses a communication path such as the Internet or a local network.

[0534] The "generation means" refers to the function that enables the server to generate the optimal cocktail based on the user's request, and includes programs that use database references and AI algorithms.

[0535] The "transmission means" is a function that allows the server to return information about the cocktail created to the terminal means, and transmits data using the HTTP protocol or the like.

[0536] The "learning means" is a function that uses feedback information received by the server to adjust the algorithms and parameters of the generating means, including retraining the machine learning model.

[0537] The "feedback transmission means" is a function for transmitting customer feedback collected by the terminal means to the server, and transmits data using the communication means.

[0538] The "database reference means" is a function that allows the generation means to search for information such as the ingredients, appearance, and name of a beverage, and refers to a large number of cocktail information items stored in a database.

[0539] The "HTTP protocol" is a communication protocol for data communication between a terminal device and a server, and is a protocol primarily used for data exchange between a web browser and a server.

[0540] The "user interface" is a screen display function that allows the terminal means to display cocktail recipes, images, and names to the user, and is implemented using HTML, JavaScript, etc.

[0541] MODE FOR CARRYING OUT THE INVENTION

[0542] This invention is a support system for bartenders to create original cocktails, and is composed of a terminal operated by a user, a server, and communication means. This system creates and serves the optimal cocktail based on the customer's wishes.

[0543] Hardware and software used

[0544] Hardware:

[0545] Devices: tablets, smartphones, etc.

[0546] Server: High-performance computer, cloud server

[0547] software:

[0548] Database: Relational database such as MySQL

[0549] Machine learning libraries: TensorFlow, PyTorch

[0550] Communication protocol: HTTP

[0551] User interface: HTML, JavaScript

[0552] System Operation Overview

[0553] The user uses the terminal to input the customer's wishes and sends the request to the server. When the server receives the request, it uses the generation means to generate the optimal cocktail. The generation means uses the database reference means to search for information such as the beverage's ingredients, appearance, and name, and determines the cocktail recipe, appearance image, and name that suits the request. The server returns the generated cocktail information to the terminal, and the user creates a cocktail based on that information and serves it to the customer. After serving, the user enters customer feedback into the terminal and sends it to the server. The server reflects the feedback information in the learning means to improve the accuracy of cocktail generation from the next time onwards.

[0554] Specific data processing and calculation

[0555] 1. User request input

[0556] Users use a tablet or smartphone to enter their customer's preferences into a text box, such as "I'd like a sweet and fruity cocktail."

[0557] 2. Submitting a Request

[0558] The terminal sends the input request to the server as an HTTP request. The request body contains the customer's request.

[0559] 3. Data reference for cocktail creation

[0560] The server analyzes the received request and uses an AI algorithm implemented in Python to generate the optimal cocktail, referencing a MySQL database to search for information such as the drink's ingredients, appearance, and name.

[0561] 4. Returning cocktail information

[0562] The server returns the generated cocktail information in JSON format to the terminal, including the recipe, image of the drink, and name.

[0563] 5. Cocktail information display

[0564] The terminal displays the cocktail information received from the server on the user interface, allowing the user to check the recipe, image, and name on the screen.

[0565] 6. Collecting and Submitting Feedback

[0566] The user enters the customer's feedback into a text box and sends it to the server as an HTTP request. The feedback includes ratings on the taste and appearance of the cocktail.

[0567] 7. Tuning the algorithm through learning methods

[0568] The server analyzes the received feedback information and uses machine learning libraries (TensorFlow, PyTorch) to adjust the algorithms and parameters of the generation method, thereby improving the accuracy of future cocktail generation.

[0569] Specific examples

[0570] Examples of prompts include:

[0571] "I want a sweet and fruity cocktail."

[0572] "I want to make a refreshing citrus cocktail."

[0573] "Can you give me a recipe for an Instagrammable rose-scented cocktail?"

[0574] The above is a specific embodiment for carrying out the present invention. This system allows bartenders to easily create and serve original cocktails tailored to customer requests. Furthermore, the system continuously evolves using feedback, improving the accuracy of subsequent cocktail creations.

[0575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0576] Step 1:

[0577] The user uses the terminal to enter a request.

[0578] Specifically, a user opens an application on a tablet or smartphone and enters a prompt statement, such as "I want a sweet and fruity cocktail," into a text box.

[0579] Input: Customer's request (prompt text)

[0580] Output: The input request data

[0581] Step 2:

[0582] The device sends a request to the server.

[0583] Specifically, the terminal sends the input request data to the server in the form of an HTTP request, which uses HTTP as the protocol.

[0584] Input: The input request data

[0585] Output: Request data sent to the server

[0586] Step 3:

[0587] The server receives the request and uses the generation means to generate the optimal cocktail.

[0588] Specifically, the server analyzes the received request and activates the generation means (an AI algorithm implemented in Python). This algorithm uses a database reference means to search for information such as the drink's ingredients, appearance, and name. Based on this information, the generation means generates the cocktail's recipe, an image of its appearance, and a name.

[0589] Input: Request data sent to the server

[0590] Output: Generated cocktail information (recipe, image, name)

[0591] Step 4:

[0592] The server returns information about the cocktail created to the terminal.

[0593] Specifically, the server returns the generated cocktail information to the terminal in JSON format as an HTTP response.

[0594] Input: Generated cocktail information (recipe, image, name)

[0595] Output: Cocktail information (recipe, image, name) returned to the device

[0596] Step 5:

[0597] The terminal displays the cocktail information to the user.

[0598] Specifically, the device displays the received cocktail information on the user interface, allowing the user to create a cocktail based on the displayed recipe and image.

[0599] Input: Cocktail information (recipe, image, name) sent back to the device

[0600] Output: Cocktail information displayed in a user interface

[0601] Step 6:

[0602] The user creates a cocktail and serves it to the customer.

[0603] Specifically, the user gathers ingredients based on the recipe displayed on the screen and creates a cocktail. For example, mix 40ml of white rum, 20ml of coconut liqueur, 60ml of pineapple juice, and 10ml of grenadine syrup, and garnish with a pineapple slice and mint leaf. The finished cocktail is then served to the customer.

[0604] Input: Cocktail information displayed in the user interface

[0605] Output: Served cocktail

[0606] Step 7:

[0607] The user inputs the customer's feedback into the terminal and sends it to the server.

[0608] Specifically, the user enters the feedback received from the customer into the text box and clicks the send button. The terminal then sends the feedback data to the server as an HTTP request.

[0609] Input: Customer Feedback

[0610] Output: Feedback data sent to the server

[0611] Step 8:

[0612] The server uses the feedback information to adjust the algorithm of the generating means using the learning means.

[0613] Specifically, the server analyzes the received feedback information and adjusts the algorithm and parameters of the generation method using machine learning libraries (TensorFlow, PyTorch), thereby improving the accuracy of cocktail generation from the next time onwards.

[0614] Input: Feedback data sent to the server

[0615] Output: Adjusted algorithms and parameters

[0616] The above is a specific description of the processing flow of the program of this system.

[0617] (Application example 1)

[0618] 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."

[0619] Traditionally, bartenders faced the problem of having to spend time and effort creating original cocktails to meet the diverse needs of customers. Furthermore, there was a lack of effective ways to incorporate feedback on the cocktails they served, making it difficult to make continuous improvements. This made it difficult to improve customer satisfaction.

[0620] 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.

[0621] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences to the server, generation means for generating an optimal cocktail based on the preferences, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and generation means for generating cocktail information using a cocktail generation AI model based on customer preferences. This makes it possible to quickly provide optimal cocktails that meet a variety of customer preferences, and by effectively reflecting feedback information, it is possible to support the bartender's creative activities and improve customer satisfaction.

[0622] "Terminal means" means a device through which you input your preferences, including a smartphone, tablet, or other computer.

[0623] "Communication means" refers to a communication protocol or communication device for transmitting the input preferences to a server, and includes the Internet, Wi-Fi, Bluetooth, etc.

[0624] "Generator" refers to an algorithm or software module that generates an optimal cocktail based on the user's preferences, and determines the ingredients, appearance, and name of the drink.

[0625] "Transmission means" refers to the communication protocol or interface for returning information including the recipe, image of the appearance, and name of the generated cocktail to the terminal, and includes HTTP, HTTPS, etc.

[0626] The "learning means" refers to a machine learning algorithm or database that reflects feedback information in the generating means, thereby improving the generating capability of the system.

[0627] "Cocktail generation AI model" refers to an artificial intelligence model used to generate cocktail information based on customer preferences, including, for example, natural language processing models such as GPT-4.

[0628] A "prompt sentence" refers to an input sentence that the generation means uses to generate the cocktail recipe, appearance, and name, and is text that includes specific generation instructions.

[0629] The present invention functions as a support system for bartenders to create original cocktails in brick-and-mortar stores. This system includes a terminal (smartphone, tablet, etc.) operated by a user, a server, and communication means.

[0630] System program configuration

[0631] The server uses multiple means to achieve its functions. First, the user inputs their preference using a terminal. For example, they may request a "bitter cocktail with an adult feel." This request is then sent to the server via a communication means.

[0632] The server uses a cocktail generation AI model (e.g., GPT-4) to generate a prompt based on the input preference. The prompt is expressed as follows:

[0633] "Create a cocktail that has a bitter, grown-up vibe."

[0634] The server uses this prompt to generate a cocktail recipe, a visual image, and a name. The generated cocktail information specifically includes the ingredients, appearance, and name of the drink, for example:

[0635] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[0636] The look: A classic cocktail glass garnished with an orange peel.

[0637] Name: "Bitter Classic"

[0638] The information about the created cocktail is sent back to the terminal by the transmission means, and the terminal displays the information to the user, allowing the user to check the cocktail recipe and appearance and actually create the cocktail.

[0639] The user can serve the cocktails they have created to customers and collect their ratings and feedback. The user uses the terminal to send the collected feedback to the server via communication means.

[0640] The server reflects the feedback information in the learning means. The learning means analyzes the feedback information using a machine learning algorithm and adjusts the algorithm and parameters to improve performance in subsequent cocktail creations. As a result, the more the system is used, the more it supports the bartender's creativity and enables the system to create cocktails that suit the preferences of each individual customer.

[0641] Hardware and software used

[0642] Hardware: Smartphones, servers

[0643] Software: Smartphone app (iOS or Android app), server (Python-based backend), database (MySQL, etc.), AI generative model (GPT-4, etc.), communication method (HTTP / HTTPS)

[0644] Specific examples

[0645] If a customer requests a "bitter cocktail with an adult feel," the AI ​​model generates the following prompt:

[0646] "Create a cocktail that has a bitter, grown-up vibe."

[0647] The server then generates the following information:

[0648] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[0649] The look: A classic cocktail glass garnished with an orange peel.

[0650] Name: "Bitter Classic"

[0651] This allows bartenders to quickly create and serve cocktails that perfectly suit customers' preferences, and the system will learn and further optimize itself based on feedback, improving the quality of future cocktails.

[0652] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0653] Step 1:

[0654] The user uses a terminal to input their preferences, including taste, atmosphere, and event theme.

[0655] Input: Customer's preference (e.g., "A bitter cocktail with an adult feel")

[0656] Output: The desired data has been input.

[0657] Step 2:

[0658] The terminal transmits the input desired data to the server using the communication means.

[0659] Input: Desired data

[0660] Output: Send the desired data to the server

[0661] Specific operation: Generate an HTTP request from the device and send it to the server

[0662] Step 3:

[0663] The server analyzes the received preference data and generates a prompt for the cocktail generation AI model.

[0664] Input: Desired data

[0665] Output: Prompt (e.g., "Please create a bitter cocktail with a mature feel.")

[0666] Specific operation: Analyze the desired data and generate an appropriate prompt.

[0667] Step 4:

[0668] The server inputs the generated prompt into a cocktail generation AI model to generate the cocktail recipe, an image of its appearance, and a name.

[0669] Input: prompt statement

[0670] Output: Cocktail information (recipe, image, name)

[0671] Specific operation: Input a prompt sentence into the AI ​​model and obtain cocktail information generated by the AI ​​model.

[0672] Step 5:

[0673] The server returns the generated cocktail information to the terminal.

[0674] Input: Cocktail information

[0675] Output: Send cocktail information to the terminal

[0676] Specific operation: Generate an HTTP response from the server and send it to the device

[0677] Step 6:

[0678] The terminal displays cocktail information to the user, who then creates a cocktail based on this information.

[0679] Input: Cocktail information

[0680] Output: Cocktail information displayed to the user

[0681] Specific behavior: Display recipe, image, and name on device

[0682] Step 7:

[0683] Users serve cocktails to customers and collect ratings and feedback, which is entered via a terminal.

[0684] Input: Your feedback

[0685] Output: Feedback data is input complete

[0686] Step 8:

[0687] The terminal transmits the feedback data to the server using a communication means.

[0688] Input: Feedback data

[0689] Output: Send feedback data to the server

[0690] Specific operation: Generate an HTTP request from the device and send it to the server

[0691] Step 9:

[0692] The server reflects the received feedback data in the learning process and adjusts the algorithms and parameters of the cocktail generation AI model.

[0693] Input: Feedback data

[0694] Output: Algorithm and parameters of the tuned AI model

[0695] Specific behavior: Analyzes feedback data and executes learning algorithms

[0696] In this way, the system of the present invention can quickly create cocktails that meet the diverse desires of customers and continuously incorporate feedback to improve the quality of service.

[0697] 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.

[0698] The present invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. An embodiment of the system and its specific operation method are described below.

[0699] This system is composed of a terminal operated by the user, a server, a communication means, and an emotion engine. The user uses a terminal (e.g., a tablet or smartphone) to input a customer's request. As the user inputs, the emotion engine recognizes the user's emotion from their facial expression and voice. The customer's request may include the desired taste and appearance of the cocktail, the theme of the event, etc. After inputting and collecting the emotion information, the user clicks the send button.

[0700] The terminal converts the request input by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and transmits it to the server via a communication means.

[0701] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the cocktail recipe, image of appearance, and name that are appropriate for the request and emotional information. The transmission means then returns the generated cocktail information to the terminal.

[0702] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[0703] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance in subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[0704] As a concrete example, consider the following scenario.

[0705] Example 1: Cocktail generation based on customer requests and sentiment information

[0706] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[0707] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[0708] 3. The user submits the input and emotion information by clicking the submit button.

[0709] 4. The device sends the request data and emotion information to the server.

[0710] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[0711] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[0712] Look: Tropical imagery garnished with pineapple slices and mint leaves

[0713] Name: "Tropical Paradise"

[0714] 6. The server returns the information about the cocktail to the terminal.

[0715] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[0716] 8. The user creates a cocktail and serves it to the customer.

[0717] 9. A customer gives feedback saying, "This cocktail is delicious."

[0718] 10. The user enters the feedback into the terminal and clicks the send button.

[0719] 11. The device sends the feedback information to the server.

[0720] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[0721] In this way, the system can provide original cocktails creatively and effectively, taking into account the emotional information of the bartender and the customer.

[0722] The processing flow will be explained below.

[0723] Step 1:

[0724] A user uses a terminal to input a cocktail request, including desired taste (e.g., "sweet and fruity"), desired appearance, and the theme of the event.

[0725] Step 2:

[0726] The emotion engine analyzes the user's facial expressions and voice to recognize emotional information. For example, emotions such as "joy" or "surprise" can be detected from the user's facial expressions.

[0727] Step 3:

[0728] The user completes the request and emotion information and clicks the send button.

[0729] Step 4:

[0730] The device converts the user's input and the recognized emotion information into a data structure (e.g., JSON format).

[0731] Step 5:

[0732] The terminal uses a communication means to transmit a request and emotion information to the server.

[0733] Step 6:

[0734] The server receives the request data and emotion information sent from the terminal.

[0735] Step 7:

[0736] The server's generating means analyzes the received request data and uses the database reference means to search for information on the ingredients, appearance, and name of the drink, taking into account emotional information to generate a cocktail that suits the user's psychological state.

[0737] Step 8:

[0738] The server generates cocktail information including the optimal cocktail recipe, visual image, and name.

[0739] Example: The recipe is 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup.

[0740] Tropical look with pineapple slices and mint leaves

[0741] It's called "Tropical Paradise"

[0742] Step 9:

[0743] The server returns the generated cocktail information to the terminal using the transmission means.

[0744] Step 10:

[0745] The terminal analyzes the cocktail information received from the server and displays it on the user interface.

[0746] Step 11:

[0747] The user creates a cocktail based on the cocktail information displayed on the terminal.

[0748] Step 12:

[0749] Serve user-created cocktails to customers and collect customer feedback.

[0750] Step 13:

[0751] The user enters the collected feedback into the terminal and clicks the send button.

[0752] Step 14:

[0753] The device converts the feedback information into a data structure (e.g., JSON format).

[0754] Step 15:

[0755] The terminal transmits feedback information to the server via the communication means.

[0756] Step 16:

[0757] The server analyzes the feedback information received using the feedback sending means.

[0758] Step 17:

[0759] The learning means of the server adjusts the algorithms and parameters of the generation means based on the received feedback information to improve performance.

[0760] Through these steps, the system creates and suggests optimal cocktails based on the user's requests and emotional information. Furthermore, by incorporating feedback, the more the system is used, the more it can provide cocktails that suit the bartender's characteristics and the customer's preferences.

[0761] Example 2

[0762] 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."

[0763] Conventional cocktail creation systems can suggest cocktails based on a customer's preferences, but they cannot consider the user's emotions. This makes it difficult to create cocktails that match the customer's psychological state, and there are also limited means to improve the system's performance by incorporating user feedback. The present invention aims to solve these problems and provide a system that can create more personalized cocktails.

[0764] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0765] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences and emotional information to the server, generation means for generating an optimal cocktail based on the preferences and emotional information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and emotion recognition means for analyzing facial expressions and voice data of the customer and recognizing their emotions. This makes it possible to suggest cocktails that suit the customer's psychological state, and further improve the performance of the system based on the feedback information.

[0766] "Terminal means" refers to the device used to input the customer's wishes and requests, specifically a mobile information terminal such as a tablet or smartphone.

[0767] "Communication means" refers to the Internet or other network communication technology for transmitting input preferences, requests, and emotional information to the server.

[0768] "Generation means" refers to an algorithm or program for generating the optimal cocktail based on input desires and emotional information.

[0769] "Transmission means" refers to the system or technology for sending information about the created cocktail back to the terminal.

[0770] "Learning means" refers to a mechanism or method for adjusting the system's generation algorithm or parameters based on feedback information to improve the performance of cocktail generation from the next time onwards.

[0771] "Emotion recognition means" refers to the technology and algorithms used to analyze a customer's facial expressions and voice data and recognize their emotions.

[0772] "Feedback Transmission Means" refers to the method or technology used to collect Customer Feedback and transmit that information to the Server.

[0773] The term "database reference means" refers to a mechanism or method for referencing a database that holds information such as the ingredients, appearance, and name of beverages, and obtaining the required information.

[0774] The present invention is a support system for bartenders to create original cocktails, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest cocktails that suit the user's psychological state. This system is configured to include a terminal operated by the user, a server, communication means, and the emotion engine. Specific embodiments of the system and their operation methods are described below.

[0775] In this system, users use devices such as tablets or smartphones to input customer requests. Requests can include the desired taste and appearance of the cocktail, the theme of the event, and so on. As the request is input, the emotion engine recognizes emotions from the user's facial expressions and voice. For example, facial expression and voice data can be collected through a camera or microphone, and analyzed to identify the user's psychological state.

[0776] After collecting the input and emotion information, the user clicks the send button. The device converts the request entered by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and sends it to the server via a communication method. Specific communication technologies used include the Internet and Wi-Fi.

[0777] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines a cocktail recipe, appearance image, and name that are appropriate for the request and emotional information. For example, the generation means uses a pre-registered cocktail database to select a recipe that uses white rum, coconut liqueur, pineapple juice, and grenadine syrup.

[0778] The generated cocktail information is then sent back to the terminal by the sending means. The terminal displays the returned cocktail information on the user interface. The user then creates a cocktail based on the displayed cocktail information and serves it to the customer.

[0779] A feedback transmission means is used to collect customer ratings and feedback. The user inputs customer feedback into the terminal and clicks the send button. For example, the user inputs feedback such as "This cocktail is very delicious." The feedback information is again transmitted to the server via the communication means.

[0780] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[0781] As a concrete example, consider the following scenario.

[0782] Example 1: Cocktail generation based on customer requests and sentiment information

[0783] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[0784] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[0785] 3. The user submits the input and emotion information by clicking the submit button.

[0786] 4. The device sends the request data and emotion information to the server.

[0787] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[0788] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[0789] Look: Tropical imagery garnished with pineapple slices and mint leaves

[0790] Name: "Tropical Paradise"

[0791] 6. The server returns the information about the cocktail to the terminal.

[0792] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[0793] 8. The user creates a cocktail and serves it to the customer.

[0794] 9. A customer gives feedback saying, "This cocktail is delicious."

[0795] 10. The user enters the feedback into the terminal and clicks the send button.

[0796] 11. The device sends the feedback information to the server.

[0797] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[0798] This makes it possible to create cocktails based on the customer's emotional information and to gradually improve the system's performance.

[0799] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0800] Program processing flow (processing steps)

[0801] Step 1:

[0802] The user enters a request

[0803] Users use devices such as tablets and smartphones to input customer requests, including desired cocktail flavors and appearances, and the theme of the event. The input information is stored in text format in the device's memory.

[0804] input:

[0805] Request information such as cocktail taste, appearance, event theme, etc.

[0806] output:

[0807] The input request information is saved in text format.

[0808] Step 2:

[0809] Emotion engine recognizes emotions

[0810] The emotion engine uses the device's built-in camera and microphone to collect the user's facial expressions and voice data, which is then analyzed by an algorithm to recognize the user's emotional state (e.g., "joy").

[0811] input:

[0812] The user's facial expression data and voice data.

[0813] output:

[0814] The analyzed emotional state (e.g., "joy").

[0815] Specific behavior:

[0816] The camera captures the user's face and the microphone records the user's voice, and the emotion engine analyzes this data to identify their emotional state.

[0817] Step 3:

[0818] Sending requests and emotional information

[0819] The user clicks the send button to send the input request and the recognized emotion information to the server. The device converts this information into JSON format and sends it to the server via a communication method.

[0820] input:

[0821] Input request information and recognized emotion information.

[0822] output:

[0823] The request information and emotion information converted into JSON format are sent to the server.

[0824] Specific behavior:

[0825] When the send button is clicked, the device converts the text request information and emotional state into JSON format and sends it to the server via the Internet.

[0826] Step 4:

[0827] The server analyzes the data

[0828] The server receives the request data and emotional information sent from the device, analyzes them, and then selects the optimal cocktail based on the analysis using a query-based algorithm.

[0829] input:

[0830] Request and sentiment information in JSON format.

[0831] output:

[0832] The analysis results include a list of cocktail candidates and information on the optimal cocktail.

[0833] Specific behavior:

[0834] The server analyzes the request information and emotional information, and selects the most suitable cocktail by referring to existing cocktail information in a database.

[0835] Step 5:

[0836] Cocktail Creation

[0837] The generating means uses the database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the recipe, appearance image, and name of the cocktail that is suitable for the request and emotional information.

[0838] input:

[0839] Cocktail information such as drink ingredients, appearance, and name retrieved from a database.

[0840] output:

[0841] The best cocktail recipes, images and names.

[0842] Specific behavior:

[0843] The generator queries the database and extracts the cocktail information that best matches the request and emotional information (e.g., 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup, name "Tropical Paradise", image).

[0844] Step 6:

[0845] Submit cocktail information

[0846] The server sends the generated cocktail information back to the terminal, which displays the received information on the user interface.

[0847] input:

[0848] The generated cocktail recipe, image, and name.

[0849] output:

[0850] The returned cocktail information will be displayed on the terminal.

[0851] Specific behavior:

[0852] The server sends the generated cocktail information back to the terminal, which receives the information and displays it on the user interface.

[0853] Step 7:

[0854] Cocktail creation and serving

[0855] The user actually creates a cocktail based on the displayed cocktail information and serves it to the customer.

[0856] input:

[0857] The cocktail recipe, image, and name are displayed on the device.

[0858] output:

[0859] Cocktails served to guests.

[0860] Specific behavior:

[0861] The user creates a cocktail according to the optimal cocktail recipe and serves it to the customer.

[0862] Step 8:

[0863] Collecting feedback

[0864] To collect customer ratings and feedback, the user enters the feedback into the terminal and clicks the submit button.

[0865] input:

[0866] Customer feedback information.

[0867] output:

[0868] The feedback information is stored on the device.

[0869] Specific behavior:

[0870] After the customer samples the cocktail, the user enters feedback into the terminal, such as "This cocktail is very delicious," and clicks the send button.

[0871] Step 9:

[0872] Give feedback and learn

[0873] The terminal again transmits the feedback information to the server via the communication means, and the server receives the feedback information and adjusts the algorithm and parameters of the generation means using the learning means.

[0874] input:

[0875] Customer feedback information.

[0876] output:

[0877] Adjusted generation algorithm.

[0878] Specific behavior:

[0879] The terminal transmits feedback information to the server, which receives it and adjusts the algorithms and parameters using a learning means.

[0880] (Application example 2)

[0881] 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."

[0882] Conventional cocktail creation systems did not adequately consider the customer's emotional state when suggesting drinks, leaving the user experience unsatisfactory. Furthermore, there was no mechanism for collecting feedback and continuously improving the system, making it difficult for bartenders to improve the quality of the cocktails they served. Furthermore, the lack of a real-time cocktail creation guide sometimes caused bartenders to struggle.

[0883] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes terminal means for inputting the user's preferences, communication means for transmitting the input preferences to the server, emotion recognition means for recognizing emotions from the user's facial expressions and voice, generation means for generating an optimal cocktail based on the preferences and emotion information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, and learning means for reflecting feedback information in the generation means. This enables the system to suggest optimal cocktails taking the user's emotion information into consideration, provide cocktail creation guidance in real time, and continuously improve the system based on feedback information.

[0884] "Terminal means" refers to a device for inputting customer preferences, including tablets and smartphones.

[0885] "Communication means" refers to a method for transmitting the input preference to the server, and includes internet communication, wireless communication, and the like.

[0886] "Emotion recognition means" refers to technology or devices that recognize emotions from the user's facial expressions and voice.

[0887] The "creation means" is a method or device for creating an optimal cocktail based on the desire and emotional information.

[0888] The "transmission means" refers to a method or device for returning information including the recipe, image of the appearance, and name of the created cocktail to the terminal.

[0889] The "learning means" is an algorithm or device for making the generating means reflect feedback information.

[0890] The "feedback transmission means" refers to a method or device for transmitting customer feedback collected by the terminal means to the server.

[0891] "Database reference means" refers to a method or device for referencing a database to generate the ingredients, appearance, and name of a beverage.

[0892] This invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. Below, an embodiment of the system and its specific operation method are described.

[0893] The system for realizing the present invention uses smartphones, tablets, smart glasses, and servers as hardware, and an emotion recognition engine, a cocktail database, and a communication platform (e.g., HTTP communication means) as software.

[0894] First, the user (bartender) inputs the customer's preferences using a terminal such as a smartphone or tablet. These preferences include the desired taste and appearance of the cocktail, the theme of the event, etc. Next, an emotion recognition tool is used to recognize the customer's emotions from their facial expressions and voice. The software uses face recognition technology and voice analysis technology to do this.

[0895] When the user presses the send button for the preference and emotion information, the device converts this information into JSON format and sends it to the server via the communication means. The server analyzes the received information and generates an optimal cocktail using the generation means. This generation means refers to a cocktail database and searches for information such as the drink's ingredients, appearance, and name. Once the optimal cocktail is generated, the server returns the recipe, an image of the appearance, and the name of the generated cocktail to the device via the transmission means.

[0896] The user creates a cocktail based on the information displayed on the terminal and serves it to the customer. The terminal collects the customer's reactions and feedback and sends them back to the server. The server then reflects this feedback information in the learning means and adjusts the algorithms and parameters of the generation means. This allows the system to improve its performance in subsequent cocktail generation.

[0897] For a concrete scenario, the following prompt sentences can be used:

[0898] If a user requests a "sweet and fruity cocktail" and the emotion engine recognizes "joy," the server will suggest a recipe for the perfect cocktail, "Tropical Paradise." The prompt text is entered as follows:

[0899] Generate the best cocktail based on the following desires and emotions: Desire: Sweet and fruity cocktail, Emotion: Joy

[0900] As described above, the present invention can provide original cocktails creatively and effectively while taking into consideration the emotional information of the bartender and the customer.

[0901] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0902] Step 1:

[0903] The user inputs their preferences using a terminal such as a smartphone or tablet. This input includes preferences for the taste and appearance of the cocktail, the theme of the event, etc. The input preferences are saved as text information on the terminal.

[0904] Step 2:

[0905] The terminal uses emotion recognition means to recognize emotions from the customer's facial expressions and voice. The emotion recognition means uses a camera and microphone to collect facial and voice data, which is then analyzed by the Emotion Engine. This allows emotions such as "happiness" and "sadness" to be recognized.

[0906] Step 3:

[0907] The user presses the send button to send their input and emotion information. At this time, the device converts their wishes and emotion information into JSON format and sends it to the server via a communication method. The input data is sent to the server as an HTTP request.

[0908] Step 4:

[0909] The server receives the request data and emotion information sent from the device, parses the received data, and extracts the desired content and emotion.

[0910] Step 5:

[0911] The server's generation means generates the optimal cocktail based on the received desired data and emotional information. The generation process involves referencing a cocktail database to search for the ingredients, appearance, and name of a drink that matches the criteria. For example, based on the data "sweet and fruity cocktail, emotion: joy," a cocktail called "Tropical Paradise" is selected.

[0912] Step 6:

[0913] The server sends information about the created cocktail (recipe, image, name) to the terminal via a transmission means. The query result is sent to the terminal in JSON format.

[0914] Step 7:

[0915] The terminal receives the cocktail information returned from the server and displays it on the user interface. At this time, the cocktail recipe, image of its appearance, and name are displayed on the screen. The user creates a cocktail based on this information.

[0916] Step 8:

[0917] The cocktail created by the user is served to the customer, and feedback such as "very delicious" is collected from the customer. The terminal means inputs and saves the feedback information as text data.

[0918] Step 9:

[0919] The feedback information collected by the terminal means is sent to the server using the feedback sending means. The feedback data is also sent to the server in JSON format.

[0920] Step 10:

[0921] The server analyzes the received feedback information and reflects it in the learning process. Based on the feedback data, the algorithm and parameters of the generation process are adjusted to improve the accuracy of cocktail generation from the next time onwards. This allows the system to improve with each use.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] [Third embodiment]

[0926] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0927] 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.

[0928] 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).

[0929] 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.

[0930] 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.

[0931] 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).

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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."

[0938] The present invention functions as a support system for bartenders to create original cocktails. An embodiment of the system and a specific method of operation thereof will be described below.

[0939] This system is composed of a terminal operated by a user, a server, and a communication means. The user uses a terminal (e.g., a tablet or smartphone) to input customer requests. The requests may include the desired taste and appearance of the cocktail, the theme of the event, etc. The input request is sent to the server via the communication means.

[0940] When the server receives the request, it uses the generation means to generate the optimal cocktail. This generation means searches for information such as the ingredients, appearance, and name of the drink using the database reference means, and determines the recipe, appearance image, and name of the cocktail that suits the request. The generated cocktail information is then sent back to the terminal by the transmission means.

[0941] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[0942] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. This process enables the system to generate cocktails that suit the bartender's individuality and the customer's preferences the more it is used.

[0943] As a concrete example, consider the following scenario.

[0944] Example 1: Creating a cocktail based on a customer request

[0945] 1. The user uses a terminal to enter a request such as "I want a sweet and fruity cocktail" and clicks the send button.

[0946] 2. The device sends a request to the server.

[0947] 3. The server receives the request and uses the generation method to generate the optimal cocktail.

[0948] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[0949] Look: Tropical imagery garnished with pineapple slices and mint leaves

[0950] Name: "Tropical Paradise"

[0951] 4. The server returns information about the created cocktail to the terminal.

[0952] 5. The terminal displays the cocktail information (recipe, image, name) to the user.

[0953] 6. The user creates a cocktail and serves it to the customer.

[0954] 7. A customer gives feedback saying, "This cocktail is delicious."

[0955] 8. The user enters the feedback into the terminal and sends it to the server.

[0956] 9. The server uses the feedback information to adjust the algorithm of the generator.

[0957] In this way, the system supports the bartender's creativity and continuously evolves to provide the perfect original cocktail for each individual customer.

[0958] The processing flow will be explained below.

[0959] Step 1:

[0960] The user uses the device to input the customer's wishes and requests, including taste, appearance, and the theme of the event. Once the input is complete, the user clicks the send button.

[0961] Step 2:

[0962] The terminal converts the request input by the user into a data structure (e.g., JSON format), and the converted data is sent to the server using a communication means.

[0963] Step 3:

[0964] The server receives the request data sent from the terminal, analyzes the request data, and generates the parameters required to be passed to the generating means.

[0965] Step 4:

[0966] A generation means of the server uses the analyzed parameters to generate an optimal cocktail recipe, visual image, and name, and the generation process uses a database reference means to extract information about the ingredients, visual appearance, and name of the drink.

[0967] Step 5:

[0968] The server compiles the generated cocktail information (recipe, image, name) into a data structure (e.g., JSON format) and sends this data to the terminal using a transmission means.

[0969] Step 6:

[0970] The terminal analyzes the cocktail information received from the server and displays it on the user interface. The user then creates a cocktail based on the displayed information.

[0971] Step 7:

[0972] The user serves the cocktails they have created to customers, collects their ratings and feedback, and enters the collected feedback into the terminal and clicks the send button.

[0973] Step 8:

[0974] The terminal converts the feedback information input by the user into a data structure (e.g., JSON format) and transmits it to the server using the feedback transmission means.

[0975] Step 9:

[0976] The server receives feedback information sent from the terminal, analyzes the feedback information, and adjusts the algorithm and parameters of the generating means using the learning means.

[0977] Step 10:

[0978] The server's learning means optimizes the algorithm and parameters based on the received feedback information, which allows for more accurate cocktails to be generated in subsequent cocktail generation processes.

[0979] Example 1

[0980] 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."

[0981] Currently, when bartenders create original cocktails, they lack an easy way to create optimal recipes that meet the tastes and requests of specific customers. Furthermore, there are no systems in place to efficiently incorporate customer feedback on cocktails they have served and improve the quality of future cocktails. As a result, bartenders spend more time and effort creating cocktails, and it is difficult to maintain consistent quality.

[0982] 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.

[0983] In this invention, the server includes a terminal means operated by a user, a communication means for transmitting customer requests input via the terminal means to the server, a creation means for creating an optimal cocktail based on the requests, a transmission means for returning information including the recipe, an image of the appearance, and the name of the created cocktail to the terminal, and a learning means for reflecting feedback information in the creation means. This enables bartenders to easily create original cocktails that meet customer requests and improve subsequent cocktail creations based on customer feedback on the cocktails they have provided.

[0984] The "terminal means" is a device operated by a user, specifically an electronic device such as a tablet or smartphone.

[0985] The "communication means" is a function for transmitting data input by the terminal means to the server, and uses a communication path such as the Internet or a local network.

[0986] The "generation means" refers to the function that enables the server to generate the optimal cocktail based on the user's request, and includes programs that use database references and AI algorithms.

[0987] The "transmission means" is a function that allows the server to return information about the cocktail created to the terminal means, and transmits data using the HTTP protocol or the like.

[0988] The "learning means" is a function that uses feedback information received by the server to adjust the algorithms and parameters of the generating means, including retraining the machine learning model.

[0989] The "feedback transmission means" is a function for transmitting customer feedback collected by the terminal means to the server, and transmits data using the communication means.

[0990] The "database reference means" is a function that allows the generation means to search for information such as the ingredients, appearance, and name of a beverage, and refers to a large number of cocktail information items stored in a database.

[0991] The "HTTP protocol" is a communication protocol for data communication between a terminal device and a server, and is a protocol primarily used for data exchange between a web browser and a server.

[0992] The "user interface" is a screen display function that allows the terminal means to display cocktail recipes, images, and names to the user, and is implemented using HTML, JavaScript, etc.

[0993] MODE FOR CARRYING OUT THE INVENTION

[0994] This invention is a support system for bartenders to create original cocktails, and is composed of a terminal operated by a user, a server, and communication means. This system creates and serves the optimal cocktail based on the customer's wishes.

[0995] Hardware and software used

[0996] Hardware:

[0997] Devices: tablets, smartphones, etc.

[0998] Server: High-performance computer, cloud server

[0999] software:

[1000] Database: Relational database such as MySQL

[1001] Machine learning libraries: TensorFlow, PyTorch

[1002] Communication protocol: HTTP

[1003] User interface: HTML, JavaScript

[1004] System Operation Overview

[1005] The user uses the terminal to input the customer's wishes and sends the request to the server. When the server receives the request, it uses the generation means to generate the optimal cocktail. The generation means uses the database reference means to search for information such as the beverage's ingredients, appearance, and name, and determines the cocktail recipe, appearance image, and name that suits the request. The server returns the generated cocktail information to the terminal, and the user creates a cocktail based on that information and serves it to the customer. After serving, the user enters customer feedback into the terminal and sends it to the server. The server reflects the feedback information in the learning means to improve the accuracy of cocktail generation from the next time onwards.

[1006] Specific data processing and calculation

[1007] 1. User request input

[1008] Users use a tablet or smartphone to enter their customer's preferences into a text box, such as "I'd like a sweet and fruity cocktail."

[1009] 2. Submitting a Request

[1010] The terminal sends the input request to the server as an HTTP request. The request body contains the customer's request.

[1011] 3. Data reference for cocktail creation

[1012] The server analyzes the received request and uses an AI algorithm implemented in Python to generate the optimal cocktail, referencing a MySQL database to search for information such as the drink's ingredients, appearance, and name.

[1013] 4. Returning cocktail information

[1014] The server returns the generated cocktail information in JSON format to the terminal, including the recipe, image of the drink, and name.

[1015] 5. Cocktail information display

[1016] The terminal displays the cocktail information received from the server on the user interface, allowing the user to check the recipe, image, and name on the screen.

[1017] 6. Collecting and Submitting Feedback

[1018] The user enters the customer's feedback into a text box and sends it to the server as an HTTP request. The feedback includes ratings on the taste and appearance of the cocktail.

[1019] 7. Tuning the algorithm through learning methods

[1020] The server analyzes the received feedback information and uses machine learning libraries (TensorFlow, PyTorch) to adjust the algorithms and parameters of the generation method, thereby improving the accuracy of future cocktail generation.

[1021] Specific examples

[1022] Examples of prompts include:

[1023] "I want a sweet and fruity cocktail."

[1024] "I want to make a refreshing citrus cocktail."

[1025] "Can you give me a recipe for an Instagrammable rose-scented cocktail?"

[1026] The above is a specific embodiment for carrying out the present invention. This system allows bartenders to easily create and serve original cocktails tailored to customer requests. Furthermore, the system continuously evolves using feedback, improving the accuracy of subsequent cocktail creations.

[1027] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1028] Step 1:

[1029] The user uses the terminal to enter a request.

[1030] Specifically, a user opens an application on a tablet or smartphone and enters a prompt statement, such as "I want a sweet and fruity cocktail," into a text box.

[1031] Input: Customer's request (prompt text)

[1032] Output: The input request data

[1033] Step 2:

[1034] The device sends a request to the server.

[1035] Specifically, the terminal sends the input request data to the server in the form of an HTTP request, which uses HTTP as the protocol.

[1036] Input: The input request data

[1037] Output: Request data sent to the server

[1038] Step 3:

[1039] The server receives the request and uses the generation means to generate the optimal cocktail.

[1040] Specifically, the server analyzes the received request and activates the generation means (an AI algorithm implemented in Python). This algorithm uses a database reference means to search for information such as the drink's ingredients, appearance, and name. Based on this information, the generation means generates the cocktail's recipe, an image of its appearance, and a name.

[1041] Input: Request data sent to the server

[1042] Output: Generated cocktail information (recipe, image, name)

[1043] Step 4:

[1044] The server returns information about the cocktail created to the terminal.

[1045] Specifically, the server returns the generated cocktail information to the terminal in JSON format as an HTTP response.

[1046] Input: Generated cocktail information (recipe, image, name)

[1047] Output: Cocktail information (recipe, image, name) returned to the device

[1048] Step 5:

[1049] The terminal displays the cocktail information to the user.

[1050] Specifically, the device displays the received cocktail information on the user interface, allowing the user to create a cocktail based on the displayed recipe and image.

[1051] Input: Cocktail information (recipe, image, name) sent back to the device

[1052] Output: Cocktail information displayed in a user interface

[1053] Step 6:

[1054] The user creates a cocktail and serves it to the customer.

[1055] Specifically, the user gathers ingredients based on the recipe displayed on the screen and creates a cocktail. For example, mix 40ml of white rum, 20ml of coconut liqueur, 60ml of pineapple juice, and 10ml of grenadine syrup, and garnish with a pineapple slice and mint leaf. The finished cocktail is then served to the customer.

[1056] Input: Cocktail information displayed in the user interface

[1057] Output: Served cocktail

[1058] Step 7:

[1059] The user inputs the customer's feedback into the terminal and sends it to the server.

[1060] Specifically, the user enters the feedback received from the customer into the text box and clicks the send button. The terminal then sends the feedback data to the server as an HTTP request.

[1061] Input: Customer Feedback

[1062] Output: Feedback data sent to the server

[1063] Step 8:

[1064] The server uses the feedback information to adjust the algorithm of the generating means using the learning means.

[1065] Specifically, the server analyzes the received feedback information and adjusts the algorithm and parameters of the generation method using machine learning libraries (TensorFlow, PyTorch), thereby improving the accuracy of cocktail generation from the next time onwards.

[1066] Input: Feedback data sent to the server

[1067] Output: Adjusted algorithms and parameters

[1068] The above is a specific description of the processing flow of the program of this system.

[1069] (Application example 1)

[1070] 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."

[1071] Traditionally, bartenders faced the problem of having to spend time and effort creating original cocktails to meet the diverse needs of customers. Furthermore, there was a lack of effective ways to incorporate feedback on the cocktails they served, making it difficult to make continuous improvements. This made it difficult to improve customer satisfaction.

[1072] 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.

[1073] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences to the server, generation means for generating an optimal cocktail based on the preferences, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and generation means for generating cocktail information using a cocktail generation AI model based on customer preferences. This makes it possible to quickly provide optimal cocktails that meet a variety of customer preferences, and by effectively reflecting feedback information, it is possible to support the bartender's creative activities and improve customer satisfaction.

[1074] "Terminal means" means a device through which you input your preferences, including a smartphone, tablet, or other computer.

[1075] "Communication means" refers to a communication protocol or communication device for transmitting the input preferences to a server, and includes the Internet, Wi-Fi, Bluetooth, etc.

[1076] "Generator" refers to an algorithm or software module that generates an optimal cocktail based on the user's preferences, and determines the ingredients, appearance, and name of the drink.

[1077] "Transmission means" refers to the communication protocol or interface for returning information including the recipe, image of the appearance, and name of the generated cocktail to the terminal, and includes HTTP, HTTPS, etc.

[1078] The "learning means" refers to a machine learning algorithm or database that reflects feedback information in the generating means, thereby improving the generating capability of the system.

[1079] "Cocktail generation AI model" refers to an artificial intelligence model used to generate cocktail information based on customer preferences, including, for example, natural language processing models such as GPT-4.

[1080] A "prompt sentence" refers to an input sentence that the generation means uses to generate the cocktail recipe, appearance, and name, and is text that includes specific generation instructions.

[1081] The present invention functions as a support system for bartenders to create original cocktails in brick-and-mortar stores. This system includes a terminal (smartphone, tablet, etc.) operated by a user, a server, and communication means.

[1082] System program configuration

[1083] The server uses multiple means to achieve its functions. First, the user inputs their preference using a terminal. For example, they may request a "bitter cocktail with an adult feel." This request is then sent to the server via a communication means.

[1084] The server uses a cocktail generation AI model (e.g., GPT-4) to generate a prompt based on the input preference. The prompt is expressed as follows:

[1085] "Create a cocktail that has a bitter, grown-up vibe."

[1086] The server uses this prompt to generate a cocktail recipe, a visual image, and a name. The generated cocktail information specifically includes the ingredients, appearance, and name of the drink, for example:

[1087] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[1088] The look: A classic cocktail glass garnished with an orange peel.

[1089] Name: "Bitter Classic"

[1090] The information about the created cocktail is sent back to the terminal by the transmission means, and the terminal displays the information to the user, allowing the user to check the cocktail recipe and appearance and actually create the cocktail.

[1091] The user can serve the cocktails they have created to customers and collect their ratings and feedback. The user uses the terminal to send the collected feedback to the server via communication means.

[1092] The server reflects the feedback information in the learning means. The learning means analyzes the feedback information using a machine learning algorithm and adjusts the algorithm and parameters to improve performance in subsequent cocktail creations. As a result, the more the system is used, the more it supports the bartender's creativity and enables the system to create cocktails that suit the preferences of each individual customer.

[1093] Hardware and software used

[1094] Hardware: Smartphones, servers

[1095] Software: Smartphone app (iOS or Android app), server (Python-based backend), database (MySQL, etc.), AI generative model (GPT-4, etc.), communication method (HTTP / HTTPS)

[1096] Specific examples

[1097] If a customer requests a "bitter cocktail with an adult feel," the AI ​​model generates the following prompt:

[1098] "Create a cocktail that has a bitter, grown-up vibe."

[1099] The server then generates the following information:

[1100] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[1101] The look: A classic cocktail glass garnished with an orange peel.

[1102] Name: "Bitter Classic"

[1103] This allows bartenders to quickly create and serve cocktails that perfectly suit customers' preferences, and the system will learn and further optimize itself based on feedback, improving the quality of future cocktails.

[1104] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1105] Step 1:

[1106] The user uses a terminal to input their preferences, including taste, atmosphere, and event theme.

[1107] Input: Customer's preference (e.g., "A bitter cocktail with an adult feel")

[1108] Output: The desired data has been input.

[1109] Step 2:

[1110] The terminal transmits the input desired data to the server using the communication means.

[1111] Input: Desired data

[1112] Output: Send the desired data to the server

[1113] Specific operation: Generate an HTTP request from the device and send it to the server

[1114] Step 3:

[1115] The server analyzes the received preference data and generates a prompt for the cocktail generation AI model.

[1116] Input: Desired data

[1117] Output: Prompt (e.g., "Please create a bitter cocktail with a mature feel.")

[1118] Specific operation: Analyze the desired data and generate an appropriate prompt.

[1119] Step 4:

[1120] The server inputs the generated prompt into a cocktail generation AI model to generate the cocktail recipe, an image of its appearance, and a name.

[1121] Input: prompt statement

[1122] Output: Cocktail information (recipe, image, name)

[1123] Specific operation: Input a prompt sentence into the AI ​​model and obtain cocktail information generated by the AI ​​model.

[1124] Step 5:

[1125] The server returns the generated cocktail information to the terminal.

[1126] Input: Cocktail information

[1127] Output: Send cocktail information to the terminal

[1128] Specific operation: Generate an HTTP response from the server and send it to the device

[1129] Step 6:

[1130] The terminal displays cocktail information to the user, who then creates a cocktail based on this information.

[1131] Input: Cocktail information

[1132] Output: Cocktail information displayed to the user

[1133] Specific behavior: Display recipe, image, and name on device

[1134] Step 7:

[1135] Users serve cocktails to customers and collect ratings and feedback, which is entered via a terminal.

[1136] Input: Your feedback

[1137] Output: Feedback data is input complete

[1138] Step 8:

[1139] The terminal transmits the feedback data to the server using a communication means.

[1140] Input: Feedback data

[1141] Output: Send feedback data to the server

[1142] Specific operation: Generate an HTTP request from the device and send it to the server

[1143] Step 9:

[1144] The server reflects the received feedback data in the learning process and adjusts the algorithms and parameters of the cocktail generation AI model.

[1145] Input: Feedback data

[1146] Output: Algorithm and parameters of the tuned AI model

[1147] Specific behavior: Analyzes feedback data and executes learning algorithms

[1148] In this way, the system of the present invention can quickly create cocktails that meet the diverse desires of customers and continuously incorporate feedback to improve the quality of service.

[1149] 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.

[1150] The present invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. An embodiment of the system and its specific operation method are described below.

[1151] This system is composed of a terminal operated by the user, a server, a communication means, and an emotion engine. The user uses a terminal (e.g., a tablet or smartphone) to input a customer's request. As the user inputs, the emotion engine recognizes the user's emotion from their facial expression and voice. The customer's request may include the desired taste and appearance of the cocktail, the theme of the event, etc. After inputting and collecting the emotion information, the user clicks the send button.

[1152] The terminal converts the request input by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and transmits it to the server via a communication means.

[1153] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the cocktail recipe, image of appearance, and name that are appropriate for the request and emotional information. The transmission means then returns the generated cocktail information to the terminal.

[1154] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[1155] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance in subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[1156] As a concrete example, consider the following scenario.

[1157] Example 1: Cocktail generation based on customer requests and sentiment information

[1158] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[1159] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[1160] 3. The user submits the input and emotion information by clicking the submit button.

[1161] 4. The device sends the request data and emotion information to the server.

[1162] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[1163] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[1164] Look: Tropical imagery garnished with pineapple slices and mint leaves

[1165] Name: "Tropical Paradise"

[1166] 6. The server returns the information about the cocktail to the terminal.

[1167] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[1168] 8. The user creates a cocktail and serves it to the customer.

[1169] 9. A customer gives feedback saying, "This cocktail is delicious."

[1170] 10. The user enters the feedback into the terminal and clicks the send button.

[1171] 11. The device sends the feedback information to the server.

[1172] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[1173] In this way, the system can provide original cocktails creatively and effectively, taking into account the emotional information of the bartender and the customer.

[1174] The processing flow will be explained below.

[1175] Step 1:

[1176] A user uses a terminal to input a cocktail request, including desired taste (e.g., "sweet and fruity"), desired appearance, and the theme of the event.

[1177] Step 2:

[1178] The emotion engine analyzes the user's facial expressions and voice to recognize emotional information. For example, emotions such as "joy" or "surprise" can be detected from the user's facial expressions.

[1179] Step 3:

[1180] The user completes the request and emotion information and clicks the send button.

[1181] Step 4:

[1182] The device converts the user's input and the recognized emotion information into a data structure (e.g., JSON format).

[1183] Step 5:

[1184] The terminal uses a communication means to transmit a request and emotion information to the server.

[1185] Step 6:

[1186] The server receives the request data and emotion information sent from the terminal.

[1187] Step 7:

[1188] The server's generating means analyzes the received request data and uses the database reference means to search for information on the ingredients, appearance, and name of the drink, taking into account emotional information to generate a cocktail that suits the user's psychological state.

[1189] Step 8:

[1190] The server generates cocktail information including the optimal cocktail recipe, visual image, and name.

[1191] Example: The recipe is 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup.

[1192] Tropical look with pineapple slices and mint leaves

[1193] It's called "Tropical Paradise"

[1194] Step 9:

[1195] The server returns the generated cocktail information to the terminal using the transmission means.

[1196] Step 10:

[1197] The terminal analyzes the cocktail information received from the server and displays it on the user interface.

[1198] Step 11:

[1199] The user creates a cocktail based on the cocktail information displayed on the terminal.

[1200] Step 12:

[1201] Serve user-created cocktails to customers and collect customer feedback.

[1202] Step 13:

[1203] The user enters the collected feedback into the terminal and clicks the send button.

[1204] Step 14:

[1205] The device converts the feedback information into a data structure (e.g., JSON format).

[1206] Step 15:

[1207] The terminal transmits feedback information to the server via the communication means.

[1208] Step 16:

[1209] The server analyzes the feedback information received using the feedback sending means.

[1210] Step 17:

[1211] The learning means of the server adjusts the algorithms and parameters of the generation means based on the received feedback information to improve performance.

[1212] Through these steps, the system creates and suggests optimal cocktails based on the user's requests and emotional information. Furthermore, by incorporating feedback, the more the system is used, the more it can provide cocktails that suit the bartender's characteristics and the customer's preferences.

[1213] Example 2

[1214] 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."

[1215] Conventional cocktail creation systems can suggest cocktails based on a customer's preferences, but they cannot consider the user's emotions. This makes it difficult to create cocktails that match the customer's psychological state, and there are also limited means to improve the system's performance by incorporating user feedback. The present invention aims to solve these problems and provide a system that can create more personalized cocktails.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1217] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences and emotional information to the server, generation means for generating an optimal cocktail based on the preferences and emotional information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and emotion recognition means for analyzing facial expressions and voice data of the customer and recognizing their emotions. This makes it possible to suggest cocktails that suit the customer's psychological state, and further improve the performance of the system based on the feedback information.

[1218] "Terminal means" refers to the device used to input the customer's wishes and requests, specifically a mobile information terminal such as a tablet or smartphone.

[1219] "Communication means" refers to the Internet or other network communication technology for transmitting input preferences, requests, and emotional information to the server.

[1220] "Generation means" refers to an algorithm or program for generating the optimal cocktail based on input desires and emotional information.

[1221] "Transmission means" refers to the system or technology for sending information about the created cocktail back to the terminal.

[1222] "Learning means" refers to a mechanism or method for adjusting the system's generation algorithm or parameters based on feedback information to improve the performance of cocktail generation from the next time onwards.

[1223] "Emotion recognition means" refers to the technology and algorithms used to analyze a customer's facial expressions and voice data and recognize their emotions.

[1224] "Feedback Transmission Means" refers to the method or technology used to collect Customer Feedback and transmit that information to the Server.

[1225] The term "database reference means" refers to a mechanism or method for referencing a database that holds information such as the ingredients, appearance, and name of beverages, and obtaining the required information.

[1226] The present invention is a support system for bartenders to create original cocktails, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest cocktails that suit the user's psychological state. This system is configured to include a terminal operated by the user, a server, communication means, and the emotion engine. Specific embodiments of the system and their operation methods are described below.

[1227] In this system, users use devices such as tablets or smartphones to input customer requests. Requests can include the desired taste and appearance of the cocktail, the theme of the event, and so on. As the request is input, the emotion engine recognizes emotions from the user's facial expressions and voice. For example, facial expression and voice data can be collected through a camera or microphone, and analyzed to identify the user's psychological state.

[1228] After collecting the input and emotion information, the user clicks the send button. The device converts the request entered by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and sends it to the server via a communication method. Specific communication technologies used include the Internet and Wi-Fi.

[1229] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines a cocktail recipe, appearance image, and name that are appropriate for the request and emotional information. For example, the generation means uses a pre-registered cocktail database to select a recipe that uses white rum, coconut liqueur, pineapple juice, and grenadine syrup.

[1230] The generated cocktail information is then sent back to the terminal by the sending means. The terminal displays the returned cocktail information on the user interface. The user then creates a cocktail based on the displayed cocktail information and serves it to the customer.

[1231] A feedback transmission means is used to collect customer ratings and feedback. The user inputs customer feedback into the terminal and clicks the send button. For example, the user inputs feedback such as "This cocktail is very delicious." The feedback information is again transmitted to the server via the communication means.

[1232] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[1233] As a concrete example, consider the following scenario.

[1234] Example 1: Cocktail generation based on customer requests and sentiment information

[1235] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[1236] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[1237] 3. The user submits the input and emotion information by clicking the submit button.

[1238] 4. The device sends the request data and emotion information to the server.

[1239] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[1240] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[1241] Look: Tropical imagery garnished with pineapple slices and mint leaves

[1242] Name: "Tropical Paradise"

[1243] 6. The server returns the information about the cocktail to the terminal.

[1244] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[1245] 8. The user creates a cocktail and serves it to the customer.

[1246] 9. A customer gives feedback saying, "This cocktail is delicious."

[1247] 10. The user enters the feedback into the terminal and clicks the send button.

[1248] 11. The device sends the feedback information to the server.

[1249] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[1250] This makes it possible to create cocktails based on the customer's emotional information and to gradually improve the system's performance.

[1251] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1252] Program processing flow (processing steps)

[1253] Step 1:

[1254] The user enters a request

[1255] Users use devices such as tablets and smartphones to input customer requests, including desired cocktail flavors and appearances, and the theme of the event. The input information is stored in text format in the device's memory.

[1256] input:

[1257] Request information such as cocktail taste, appearance, event theme, etc.

[1258] output:

[1259] The input request information is saved in text format.

[1260] Step 2:

[1261] Emotion engine recognizes emotions

[1262] The emotion engine uses the device's built-in camera and microphone to collect the user's facial expressions and voice data, which is then analyzed by an algorithm to recognize the user's emotional state (e.g., "joy").

[1263] input:

[1264] The user's facial expression data and voice data.

[1265] output:

[1266] The analyzed emotional state (e.g., "joy").

[1267] Specific behavior:

[1268] The camera captures the user's face and the microphone records the user's voice, and the emotion engine analyzes this data to identify their emotional state.

[1269] Step 3:

[1270] Sending requests and emotional information

[1271] The user clicks the send button to send the input request and the recognized emotion information to the server. The device converts this information into JSON format and sends it to the server via a communication method.

[1272] input:

[1273] Input request information and recognized emotion information.

[1274] output:

[1275] The request information and emotion information converted into JSON format are sent to the server.

[1276] Specific behavior:

[1277] When the send button is clicked, the device converts the text request information and emotional state into JSON format and sends it to the server via the Internet.

[1278] Step 4:

[1279] The server analyzes the data

[1280] The server receives the request data and emotional information sent from the device, analyzes them, and then selects the optimal cocktail based on the analysis using a query-based algorithm.

[1281] input:

[1282] Request and sentiment information in JSON format.

[1283] output:

[1284] The analysis results include a list of cocktail candidates and information on the optimal cocktail.

[1285] Specific behavior:

[1286] The server analyzes the request information and emotional information, and selects the most suitable cocktail by referring to existing cocktail information in a database.

[1287] Step 5:

[1288] Cocktail Creation

[1289] The generating means uses the database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the recipe, appearance image, and name of the cocktail that is suitable for the request and emotional information.

[1290] input:

[1291] Cocktail information such as drink ingredients, appearance, and name retrieved from a database.

[1292] output:

[1293] The best cocktail recipes, images and names.

[1294] Specific behavior:

[1295] The generator queries the database and extracts the cocktail information that best matches the request and emotional information (e.g., 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup, name "Tropical Paradise", image).

[1296] Step 6:

[1297] Submit cocktail information

[1298] The server sends the generated cocktail information back to the terminal, which displays the received information on the user interface.

[1299] input:

[1300] The generated cocktail recipe, image, and name.

[1301] output:

[1302] The returned cocktail information will be displayed on the terminal.

[1303] Specific behavior:

[1304] The server sends the generated cocktail information back to the terminal, which receives the information and displays it on the user interface.

[1305] Step 7:

[1306] Cocktail creation and serving

[1307] The user actually creates a cocktail based on the displayed cocktail information and serves it to the customer.

[1308] input:

[1309] The cocktail recipe, image, and name are displayed on the device.

[1310] output:

[1311] Cocktails served to guests.

[1312] Specific behavior:

[1313] The user creates a cocktail according to the optimal cocktail recipe and serves it to the customer.

[1314] Step 8:

[1315] Collecting feedback

[1316] To collect customer ratings and feedback, the user enters the feedback into the terminal and clicks the submit button.

[1317] input:

[1318] Customer feedback information.

[1319] output:

[1320] The feedback information is stored on the device.

[1321] Specific behavior:

[1322] After the customer samples the cocktail, the user enters feedback into the terminal, such as "This cocktail is very delicious," and clicks the send button.

[1323] Step 9:

[1324] Give feedback and learn

[1325] The terminal again transmits the feedback information to the server via the communication means, and the server receives the feedback information and adjusts the algorithm and parameters of the generation means using the learning means.

[1326] input:

[1327] Customer feedback information.

[1328] output:

[1329] Adjusted generation algorithm.

[1330] Specific behavior:

[1331] The terminal transmits feedback information to the server, which receives it and adjusts the algorithms and parameters using a learning means.

[1332] (Application example 2)

[1333] 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."

[1334] Conventional cocktail creation systems did not adequately consider the customer's emotional state when suggesting drinks, leaving the user experience unsatisfactory. Furthermore, there was no mechanism for collecting feedback and continuously improving the system, making it difficult for bartenders to improve the quality of the cocktails they served. Furthermore, the lack of a real-time cocktail creation guide sometimes caused bartenders to struggle.

[1335] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes terminal means for inputting the user's preferences, communication means for transmitting the input preferences to the server, emotion recognition means for recognizing emotions from the user's facial expressions and voice, generation means for generating an optimal cocktail based on the preferences and emotion information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, and learning means for reflecting feedback information in the generation means. This enables the system to suggest optimal cocktails taking the user's emotion information into consideration, provide cocktail creation guidance in real time, and continuously improve the system based on feedback information.

[1336] "Terminal means" refers to a device for inputting customer preferences, including tablets and smartphones.

[1337] "Communication means" refers to a method for transmitting the input preference to the server, and includes internet communication, wireless communication, and the like.

[1338] "Emotion recognition means" refers to technology or devices that recognize emotions from the user's facial expressions and voice.

[1339] The "creation means" is a method or device for creating an optimal cocktail based on the desire and emotional information.

[1340] The "transmission means" refers to a method or device for returning information including the recipe, image of the appearance, and name of the created cocktail to the terminal.

[1341] The "learning means" is an algorithm or device for making the generating means reflect feedback information.

[1342] The "feedback transmission means" refers to a method or device for transmitting customer feedback collected by the terminal means to the server.

[1343] "Database reference means" refers to a method or device for referencing a database to generate the ingredients, appearance, and name of a beverage.

[1344] This invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. Below, an embodiment of the system and its specific operation method are described.

[1345] The system for realizing the present invention uses smartphones, tablets, smart glasses, and servers as hardware, and an emotion recognition engine, a cocktail database, and a communication platform (e.g., HTTP communication means) as software.

[1346] First, the user (bartender) inputs the customer's preferences using a terminal such as a smartphone or tablet. These preferences include the desired taste and appearance of the cocktail, the theme of the event, etc. Next, an emotion recognition tool is used to recognize the customer's emotions from their facial expressions and voice. The software uses face recognition technology and voice analysis technology to do this.

[1347] When the user presses the send button for the preference and emotion information, the device converts this information into JSON format and sends it to the server via the communication means. The server analyzes the received information and generates an optimal cocktail using the generation means. This generation means refers to a cocktail database and searches for information such as the drink's ingredients, appearance, and name. Once the optimal cocktail is generated, the server returns the recipe, an image of the appearance, and the name of the generated cocktail to the device via the transmission means.

[1348] The user creates a cocktail based on the information displayed on the terminal and serves it to the customer. The terminal collects the customer's reactions and feedback and sends them back to the server. The server then reflects this feedback information in the learning means and adjusts the algorithms and parameters of the generation means. This allows the system to improve its performance in subsequent cocktail generation.

[1349] For a concrete scenario, the following prompt sentences can be used:

[1350] If a user requests a "sweet and fruity cocktail" and the emotion engine recognizes "joy," the server will suggest a recipe for the perfect cocktail, "Tropical Paradise." The prompt text is entered as follows:

[1351] Generate the best cocktail based on the following desires and emotions: Desire: Sweet and fruity cocktail, Emotion: Joy

[1352] As described above, the present invention can provide original cocktails creatively and effectively while taking into consideration the emotional information of the bartender and the customer.

[1353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1354] Step 1:

[1355] The user inputs their preferences using a terminal such as a smartphone or tablet. This input includes preferences for the taste and appearance of the cocktail, the theme of the event, etc. The input preferences are saved as text information on the terminal.

[1356] Step 2:

[1357] The terminal uses emotion recognition means to recognize emotions from the customer's facial expressions and voice. The emotion recognition means uses a camera and microphone to collect facial and voice data, which is then analyzed by the Emotion Engine. This allows emotions such as "happiness" and "sadness" to be recognized.

[1358] Step 3:

[1359] The user presses the send button to send their input and emotion information. At this time, the device converts their wishes and emotion information into JSON format and sends it to the server via a communication method. The input data is sent to the server as an HTTP request.

[1360] Step 4:

[1361] The server receives the request data and emotion information sent from the device, parses the received data, and extracts the desired content and emotion.

[1362] Step 5:

[1363] The server's generation means generates the optimal cocktail based on the received desired data and emotional information. The generation process involves referencing a cocktail database to search for the ingredients, appearance, and name of a drink that matches the criteria. For example, based on the data "sweet and fruity cocktail, emotion: joy," a cocktail called "Tropical Paradise" is selected.

[1364] Step 6:

[1365] The server sends information about the created cocktail (recipe, image, name) to the terminal via a transmission means. The query result is sent to the terminal in JSON format.

[1366] Step 7:

[1367] The terminal receives the cocktail information returned from the server and displays it on the user interface. At this time, the cocktail recipe, image of its appearance, and name are displayed on the screen. The user creates a cocktail based on this information.

[1368] Step 8:

[1369] The cocktail created by the user is served to the customer, and feedback such as "very delicious" is collected from the customer. The terminal means inputs and saves the feedback information as text data.

[1370] Step 9:

[1371] The feedback information collected by the terminal means is sent to the server using the feedback sending means. The feedback data is also sent to the server in JSON format.

[1372] Step 10:

[1373] The server analyzes the received feedback information and reflects it in the learning process. Based on the feedback data, the algorithm and parameters of the generation process are adjusted to improve the accuracy of cocktail generation from the next time onwards. This allows the system to improve with each use.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] [Fourth embodiment]

[1378] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1379] 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.

[1380] 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).

[1381] 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.

[1382] 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.

[1383] 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).

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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."

[1391] The present invention functions as a support system for bartenders to create original cocktails. An embodiment of the system and a specific method of operation thereof will be described below.

[1392] This system is composed of a terminal operated by a user, a server, and a communication means. The user uses a terminal (e.g., a tablet or smartphone) to input customer requests. The requests may include the desired taste and appearance of the cocktail, the theme of the event, etc. The input request is sent to the server via the communication means.

[1393] When the server receives the request, it uses the generation means to generate the optimal cocktail. This generation means searches for information such as the ingredients, appearance, and name of the drink using the database reference means, and determines the recipe, appearance image, and name of the cocktail that suits the request. The generated cocktail information is then sent back to the terminal by the transmission means.

[1394] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[1395] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. This process enables the system to generate cocktails that suit the bartender's individuality and the customer's preferences the more it is used.

[1396] As a concrete example, consider the following scenario.

[1397] Example 1: Creating a cocktail based on a customer request

[1398] 1. The user uses a terminal to enter a request such as "I want a sweet and fruity cocktail" and clicks the send button.

[1399] 2. The device sends a request to the server.

[1400] 3. The server receives the request and uses the generation method to generate the optimal cocktail.

[1401] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[1402] Look: Tropical imagery garnished with pineapple slices and mint leaves

[1403] Name: "Tropical Paradise"

[1404] 4. The server returns information about the created cocktail to the terminal.

[1405] 5. The terminal displays the cocktail information (recipe, image, name) to the user.

[1406] 6. The user creates a cocktail and serves it to the customer.

[1407] 7. A customer gives feedback saying, "This cocktail is delicious."

[1408] 8. The user enters the feedback into the terminal and sends it to the server.

[1409] 9. The server uses the feedback information to adjust the algorithm of the generator.

[1410] In this way, the system supports the bartender's creativity and continuously evolves to provide the perfect original cocktail for each individual customer.

[1411] The processing flow will be explained below.

[1412] Step 1:

[1413] The user uses the device to input the customer's wishes and requests, including taste, appearance, and the theme of the event. Once the input is complete, the user clicks the send button.

[1414] Step 2:

[1415] The terminal converts the request input by the user into a data structure (e.g., JSON format), and the converted data is sent to the server using a communication means.

[1416] Step 3:

[1417] The server receives the request data sent from the terminal, analyzes the request data, and generates the parameters required to be passed to the generating means.

[1418] Step 4:

[1419] A generation means of the server uses the analyzed parameters to generate an optimal cocktail recipe, visual image, and name, and the generation process uses a database reference means to extract information about the ingredients, visual appearance, and name of the drink.

[1420] Step 5:

[1421] The server compiles the generated cocktail information (recipe, image, name) into a data structure (e.g., JSON format) and sends this data to the terminal using a transmission means.

[1422] Step 6:

[1423] The terminal analyzes the cocktail information received from the server and displays it on the user interface. The user then creates a cocktail based on the displayed information.

[1424] Step 7:

[1425] The user serves the cocktails they have created to customers, collects their ratings and feedback, and enters the collected feedback into the terminal and clicks the send button.

[1426] Step 8:

[1427] The terminal converts the feedback information input by the user into a data structure (e.g., JSON format) and transmits it to the server using the feedback transmission means.

[1428] Step 9:

[1429] The server receives feedback information sent from the terminal, analyzes the feedback information, and adjusts the algorithm and parameters of the generating means using the learning means.

[1430] Step 10:

[1431] The server's learning means optimizes the algorithm and parameters based on the received feedback information, which allows for more accurate cocktails to be generated in subsequent cocktail generation processes.

[1432] Example 1

[1433] 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."

[1434] Currently, when bartenders create original cocktails, they lack an easy way to create optimal recipes that meet the tastes and requests of specific customers. Furthermore, there are no systems in place to efficiently incorporate customer feedback on cocktails they have served and improve the quality of future cocktails. As a result, bartenders spend more time and effort creating cocktails, and it is difficult to maintain consistent quality.

[1435] 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.

[1436] In this invention, the server includes a terminal means operated by a user, a communication means for transmitting customer requests input via the terminal means to the server, a creation means for creating an optimal cocktail based on the requests, a transmission means for returning information including the recipe, an image of the appearance, and the name of the created cocktail to the terminal, and a learning means for reflecting feedback information in the creation means. This enables bartenders to easily create original cocktails that meet customer requests and improve subsequent cocktail creations based on customer feedback on the cocktails they have provided.

[1437] The "terminal means" is a device operated by a user, specifically an electronic device such as a tablet or smartphone.

[1438] The "communication means" is a function for transmitting data input by the terminal means to the server, and uses a communication path such as the Internet or a local network.

[1439] The "generation means" refers to the function that enables the server to generate the optimal cocktail based on the user's request, and includes programs that use database references and AI algorithms.

[1440] The "transmission means" is a function that allows the server to return information about the cocktail created to the terminal means, and transmits data using the HTTP protocol or the like.

[1441] The "learning means" is a function that uses feedback information received by the server to adjust the algorithms and parameters of the generating means, including retraining the machine learning model.

[1442] The "feedback transmission means" is a function for transmitting customer feedback collected by the terminal means to the server, and transmits data using the communication means.

[1443] The "database reference means" is a function that allows the generation means to search for information such as the ingredients, appearance, and name of a beverage, and refers to a large number of cocktail information items stored in a database.

[1444] The "HTTP protocol" is a communication protocol for data communication between a terminal device and a server, and is a protocol primarily used for data exchange between a web browser and a server.

[1445] The "user interface" is a screen display function that allows the terminal means to display cocktail recipes, images, and names to the user, and is implemented using HTML, JavaScript, etc.

[1446] MODE FOR CARRYING OUT THE INVENTION

[1447] This invention is a support system for bartenders to create original cocktails, and is composed of a terminal operated by a user, a server, and communication means. This system creates and serves the optimal cocktail based on the customer's wishes.

[1448] Hardware and software used

[1449] Hardware:

[1450] Devices: tablets, smartphones, etc.

[1451] Server: High-performance computer, cloud server

[1452] software:

[1453] Database: Relational database such as MySQL

[1454] Machine learning libraries: TensorFlow, PyTorch

[1455] Communication protocol: HTTP

[1456] User interface: HTML, JavaScript

[1457] System Operation Overview

[1458] The user uses the terminal to input the customer's wishes and sends the request to the server. When the server receives the request, it uses the generation means to generate the optimal cocktail. The generation means uses the database reference means to search for information such as the beverage's ingredients, appearance, and name, and determines the cocktail recipe, appearance image, and name that suits the request. The server returns the generated cocktail information to the terminal, and the user creates a cocktail based on that information and serves it to the customer. After serving, the user enters customer feedback into the terminal and sends it to the server. The server reflects the feedback information in the learning means to improve the accuracy of cocktail generation from the next time onwards.

[1459] Specific data processing and calculation

[1460] 1. User request input

[1461] Users use a tablet or smartphone to enter their customer's preferences into a text box, such as "I'd like a sweet and fruity cocktail."

[1462] 2. Submitting a Request

[1463] The terminal sends the input request to the server as an HTTP request. The request body contains the customer's request.

[1464] 3. Data reference for cocktail creation

[1465] The server analyzes the received request and uses an AI algorithm implemented in Python to generate the optimal cocktail, referencing a MySQL database to search for information such as the drink's ingredients, appearance, and name.

[1466] 4. Returning cocktail information

[1467] The server returns the generated cocktail information in JSON format to the terminal, including the recipe, image of the drink, and name.

[1468] 5. Cocktail information display

[1469] The terminal displays the cocktail information received from the server on the user interface, allowing the user to check the recipe, image, and name on the screen.

[1470] 6. Collecting and Submitting Feedback

[1471] The user enters the customer's feedback into a text box and sends it to the server as an HTTP request. The feedback includes ratings on the taste and appearance of the cocktail.

[1472] 7. Tuning the algorithm through learning methods

[1473] The server analyzes the received feedback information and uses machine learning libraries (TensorFlow, PyTorch) to adjust the algorithms and parameters of the generation method, thereby improving the accuracy of future cocktail generation.

[1474] Specific examples

[1475] Examples of prompts include:

[1476] "I want a sweet and fruity cocktail."

[1477] "I want to make a refreshing citrus cocktail."

[1478] "Can you give me a recipe for an Instagrammable rose-scented cocktail?"

[1479] The above is a specific embodiment for carrying out the present invention. This system allows bartenders to easily create and serve original cocktails tailored to customer requests. Furthermore, the system continuously evolves using feedback, improving the accuracy of subsequent cocktail creations.

[1480] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1481] Step 1:

[1482] The user uses the terminal to enter a request.

[1483] Specifically, a user opens an application on a tablet or smartphone and enters a prompt statement, such as "I want a sweet and fruity cocktail," into a text box.

[1484] Input: Customer's request (prompt text)

[1485] Output: The input request data

[1486] Step 2:

[1487] The device sends a request to the server.

[1488] Specifically, the terminal sends the input request data to the server in the form of an HTTP request, which uses HTTP as the protocol.

[1489] Input: The input request data

[1490] Output: Request data sent to the server

[1491] Step 3:

[1492] The server receives the request and uses the generation means to generate the optimal cocktail.

[1493] Specifically, the server analyzes the received request and activates the generation means (an AI algorithm implemented in Python). This algorithm uses a database reference means to search for information such as the drink's ingredients, appearance, and name. Based on this information, the generation means generates the cocktail's recipe, an image of its appearance, and a name.

[1494] Input: Request data sent to the server

[1495] Output: Generated cocktail information (recipe, image, name)

[1496] Step 4:

[1497] The server returns information about the cocktail created to the terminal.

[1498] Specifically, the server returns the generated cocktail information to the terminal in JSON format as an HTTP response.

[1499] Input: Generated cocktail information (recipe, image, name)

[1500] Output: Cocktail information (recipe, image, name) returned to the device

[1501] Step 5:

[1502] The terminal displays the cocktail information to the user.

[1503] Specifically, the device displays the received cocktail information on the user interface, allowing the user to create a cocktail based on the displayed recipe and image.

[1504] Input: Cocktail information (recipe, image, name) sent back to the device

[1505] Output: Cocktail information displayed in a user interface

[1506] Step 6:

[1507] The user creates a cocktail and serves it to the customer.

[1508] Specifically, the user gathers ingredients based on the recipe displayed on the screen and creates a cocktail. For example, mix 40ml of white rum, 20ml of coconut liqueur, 60ml of pineapple juice, and 10ml of grenadine syrup, and garnish with a pineapple slice and mint leaf. The finished cocktail is then served to the customer.

[1509] Input: Cocktail information displayed in the user interface

[1510] Output: Served cocktail

[1511] Step 7:

[1512] The user inputs the customer's feedback into the terminal and sends it to the server.

[1513] Specifically, the user enters the feedback received from the customer into the text box and clicks the send button. The terminal then sends the feedback data to the server as an HTTP request.

[1514] Input: Customer Feedback

[1515] Output: Feedback data sent to the server

[1516] Step 8:

[1517] The server uses the feedback information to adjust the algorithm of the generating means using the learning means.

[1518] Specifically, the server analyzes the received feedback information and adjusts the algorithm and parameters of the generation method using machine learning libraries (TensorFlow, PyTorch), thereby improving the accuracy of cocktail generation from the next time onwards.

[1519] Input: Feedback data sent to the server

[1520] Output: Adjusted algorithms and parameters

[1521] The above is a specific description of the processing flow of the program of this system.

[1522] (Application example 1)

[1523] 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."

[1524] Traditionally, bartenders faced the problem of having to spend time and effort creating original cocktails to meet the diverse needs of customers. Furthermore, there was a lack of effective ways to incorporate feedback on the cocktails they served, making it difficult to make continuous improvements. This made it difficult to improve customer satisfaction.

[1525] 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.

[1526] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences to the server, generation means for generating an optimal cocktail based on the preferences, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and generation means for generating cocktail information using a cocktail generation AI model based on customer preferences. This makes it possible to quickly provide optimal cocktails that meet a variety of customer preferences, and by effectively reflecting feedback information, it is possible to support the bartender's creative activities and improve customer satisfaction.

[1527] "Terminal means" means a device through which you input your preferences, including a smartphone, tablet, or other computer.

[1528] "Communication means" refers to a communication protocol or communication device for transmitting the input preferences to a server, and includes the Internet, Wi-Fi, Bluetooth, etc.

[1529] "Generator" refers to an algorithm or software module that generates an optimal cocktail based on the user's preferences, and determines the ingredients, appearance, and name of the drink.

[1530] "Transmission means" refers to the communication protocol or interface for returning information including the recipe, image of the appearance, and name of the generated cocktail to the terminal, and includes HTTP, HTTPS, etc.

[1531] The "learning means" refers to a machine learning algorithm or database that reflects feedback information in the generating means, thereby improving the generating capability of the system.

[1532] "Cocktail generation AI model" refers to an artificial intelligence model used to generate cocktail information based on customer preferences, including, for example, natural language processing models such as GPT-4.

[1533] A "prompt sentence" refers to an input sentence that the generation means uses to generate the cocktail recipe, appearance, and name, and is text that includes specific generation instructions.

[1534] The present invention functions as a support system for bartenders to create original cocktails in brick-and-mortar stores. This system includes a terminal (smartphone, tablet, etc.) operated by a user, a server, and communication means.

[1535] System program configuration

[1536] The server uses multiple means to achieve its functions. First, the user inputs their preference using a terminal. For example, they may request a "bitter cocktail with an adult feel." This request is then sent to the server via a communication means.

[1537] The server uses a cocktail generation AI model (e.g., GPT-4) to generate a prompt based on the input preference. The prompt is expressed as follows:

[1538] "Create a cocktail that has a bitter, grown-up vibe."

[1539] The server uses this prompt to generate a cocktail recipe, a visual image, and a name. The generated cocktail information specifically includes the ingredients, appearance, and name of the drink, for example:

[1540] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[1541] The look: A classic cocktail glass garnished with an orange peel.

[1542] Name: "Bitter Classic"

[1543] The information about the created cocktail is sent back to the terminal by the transmission means, and the terminal displays the information to the user, allowing the user to check the cocktail recipe and appearance and actually create the cocktail.

[1544] The user can serve the cocktails they have created to customers and collect their ratings and feedback. The user uses the terminal to send the collected feedback to the server via communication means.

[1545] The server reflects the feedback information in the learning means. The learning means analyzes the feedback information using a machine learning algorithm and adjusts the algorithm and parameters to improve performance in subsequent cocktail creations. As a result, the more the system is used, the more it supports the bartender's creativity and enables the system to create cocktails that suit the preferences of each individual customer.

[1546] Hardware and software used

[1547] Hardware: Smartphones, servers

[1548] Software: Smartphone app (iOS or Android app), server (Python-based backend), database (MySQL, etc.), AI generative model (GPT-4, etc.), communication method (HTTP / HTTPS)

[1549] Specific examples

[1550] If a customer requests a "bitter cocktail with an adult feel," the AI ​​model generates the following prompt:

[1551] "Create a cocktail that has a bitter, grown-up vibe."

[1552] The server then generates the following information:

[1553] Recipe: 50ml dry gin, 20ml Campari, 15ml sweet vermouth, 2 dashes of Angostura bitters

[1554] The look: A classic cocktail glass garnished with an orange peel.

[1555] Name: "Bitter Classic"

[1556] This allows bartenders to quickly create and serve cocktails that perfectly suit customers' preferences, and the system will learn and further optimize itself based on feedback, improving the quality of future cocktails.

[1557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1558] Step 1:

[1559] The user uses a terminal to input their preferences, including taste, atmosphere, and event theme.

[1560] Input: Customer's preference (e.g., "A bitter cocktail with an adult feel")

[1561] Output: The desired data has been input.

[1562] Step 2:

[1563] The terminal transmits the input desired data to the server using the communication means.

[1564] Input: Desired data

[1565] Output: Send the desired data to the server

[1566] Specific operation: Generate an HTTP request from the device and send it to the server

[1567] Step 3:

[1568] The server analyzes the received preference data and generates a prompt for the cocktail generation AI model.

[1569] Input: Desired data

[1570] Output: Prompt (e.g., "Please create a bitter cocktail with a mature feel.")

[1571] Specific operation: Analyze the desired data and generate an appropriate prompt.

[1572] Step 4:

[1573] The server inputs the generated prompt into a cocktail generation AI model to generate the cocktail recipe, an image of its appearance, and a name.

[1574] Input: prompt statement

[1575] Output: Cocktail information (recipe, image, name)

[1576] Specific operation: Input a prompt sentence into the AI ​​model and obtain cocktail information generated by the AI ​​model.

[1577] Step 5:

[1578] The server returns the generated cocktail information to the terminal.

[1579] Input: Cocktail information

[1580] Output: Send cocktail information to the terminal

[1581] Specific operation: Generate an HTTP response from the server and send it to the device

[1582] Step 6:

[1583] The terminal displays cocktail information to the user, who then creates a cocktail based on this information.

[1584] Input: Cocktail information

[1585] Output: Cocktail information displayed to the user

[1586] Specific behavior: Display recipe, image, and name on device

[1587] Step 7:

[1588] Users serve cocktails to customers and collect ratings and feedback, which is entered via a terminal.

[1589] Input: Your feedback

[1590] Output: Feedback data is input complete

[1591] Step 8:

[1592] The terminal transmits the feedback data to the server using a communication means.

[1593] Input: Feedback data

[1594] Output: Send feedback data to the server

[1595] Specific operation: Generate an HTTP request from the device and send it to the server

[1596] Step 9:

[1597] The server reflects the received feedback data in the learning process and adjusts the algorithms and parameters of the cocktail generation AI model.

[1598] Input: Feedback data

[1599] Output: Algorithm and parameters of the tuned AI model

[1600] Specific behavior: Analyzes feedback data and executes learning algorithms

[1601] In this way, the system of the present invention can quickly create cocktails that meet the diverse desires of customers and continuously incorporate feedback to improve the quality of service.

[1602] 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.

[1603] The present invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. An embodiment of the system and its specific operation method are described below.

[1604] This system is composed of a terminal operated by the user, a server, a communication means, and an emotion engine. The user uses a terminal (e.g., a tablet or smartphone) to input a customer's request. As the user inputs, the emotion engine recognizes the user's emotion from their facial expression and voice. The customer's request may include the desired taste and appearance of the cocktail, the theme of the event, etc. After inputting and collecting the emotion information, the user clicks the send button.

[1605] The terminal converts the request input by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and transmits it to the server via a communication means.

[1606] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the cocktail recipe, image of appearance, and name that are appropriate for the request and emotional information. The transmission means then returns the generated cocktail information to the terminal.

[1607] The user creates a cocktail based on the cocktail information displayed on the device. The created cocktail is served to the customer, and their evaluation and feedback are collected. The feedback information is then sent back to the server via communication means.

[1608] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance in subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[1609] As a concrete example, consider the following scenario.

[1610] Example 1: Cocktail generation based on customer requests and sentiment information

[1611] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[1612] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[1613] 3. The user submits the input and emotion information by clicking the submit button.

[1614] 4. The device sends the request data and emotion information to the server.

[1615] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[1616] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[1617] Look: Tropical imagery garnished with pineapple slices and mint leaves

[1618] Name: "Tropical Paradise"

[1619] 6. The server returns the information about the cocktail to the terminal.

[1620] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[1621] 8. The user creates a cocktail and serves it to the customer.

[1622] 9. A customer gives feedback saying, "This cocktail is delicious."

[1623] 10. The user enters the feedback into the terminal and clicks the send button.

[1624] 11. The device sends the feedback information to the server.

[1625] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[1626] In this way, the system can provide original cocktails creatively and effectively, taking into account the emotional information of the bartender and the customer.

[1627] The processing flow will be explained below.

[1628] Step 1:

[1629] A user uses a terminal to input a cocktail request, including desired taste (e.g., "sweet and fruity"), desired appearance, and the theme of the event.

[1630] Step 2:

[1631] The emotion engine analyzes the user's facial expressions and voice to recognize emotional information. For example, emotions such as "joy" or "surprise" can be detected from the user's facial expressions.

[1632] Step 3:

[1633] The user completes the request and emotion information and clicks the send button.

[1634] Step 4:

[1635] The device converts the user's input and the recognized emotion information into a data structure (e.g., JSON format).

[1636] Step 5:

[1637] The terminal uses a communication means to transmit a request and emotion information to the server.

[1638] Step 6:

[1639] The server receives the request data and emotion information sent from the terminal.

[1640] Step 7:

[1641] The server's generating means analyzes the received request data and uses the database reference means to search for information on the ingredients, appearance, and name of the drink, taking into account emotional information to generate a cocktail that suits the user's psychological state.

[1642] Step 8:

[1643] The server generates cocktail information including the optimal cocktail recipe, visual image, and name.

[1644] Example: The recipe is 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup.

[1645] Tropical look with pineapple slices and mint leaves

[1646] It's called "Tropical Paradise"

[1647] Step 9:

[1648] The server returns the generated cocktail information to the terminal using the transmission means.

[1649] Step 10:

[1650] The terminal analyzes the cocktail information received from the server and displays it on the user interface.

[1651] Step 11:

[1652] The user creates a cocktail based on the cocktail information displayed on the terminal.

[1653] Step 12:

[1654] Serve user-created cocktails to customers and collect customer feedback.

[1655] Step 13:

[1656] The user enters the collected feedback into the terminal and clicks the send button.

[1657] Step 14:

[1658] The device converts the feedback information into a data structure (e.g., JSON format).

[1659] Step 15:

[1660] The terminal transmits feedback information to the server via the communication means.

[1661] Step 16:

[1662] The server analyzes the feedback information received using the feedback sending means.

[1663] Step 17:

[1664] The learning means of the server adjusts the algorithms and parameters of the generation means based on the received feedback information to improve performance.

[1665] Through these steps, the system creates and suggests optimal cocktails based on the user's requests and emotional information. Furthermore, by incorporating feedback, the more the system is used, the more it can provide cocktails that suit the bartender's characteristics and the customer's preferences.

[1666] Example 2

[1667] 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."

[1668] Conventional cocktail creation systems can suggest cocktails based on a customer's preferences, but they cannot consider the user's emotions. This makes it difficult to create cocktails that match the customer's psychological state, and there are also limited means to improve the system's performance by incorporating user feedback. The present invention aims to solve these problems and provide a system that can create more personalized cocktails.

[1669] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1670] In this invention, the server includes terminal means for inputting customer preferences, communication means for transmitting the input preferences and emotional information to the server, generation means for generating an optimal cocktail based on the preferences and emotional information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, learning means for reflecting feedback information in the generation means, and emotion recognition means for analyzing facial expressions and voice data of the customer and recognizing their emotions. This makes it possible to suggest cocktails that suit the customer's psychological state, and further improve the performance of the system based on the feedback information.

[1671] "Terminal means" refers to the device used to input the customer's wishes and requests, specifically a mobile information terminal such as a tablet or smartphone.

[1672] "Communication means" refers to the Internet or other network communication technology for transmitting input preferences, requests, and emotional information to the server.

[1673] "Generation means" refers to an algorithm or program for generating the optimal cocktail based on input desires and emotional information.

[1674] "Transmission means" refers to the system or technology for sending information about the created cocktail back to the terminal.

[1675] "Learning means" refers to a mechanism or method for adjusting the system's generation algorithm or parameters based on feedback information to improve the performance of cocktail generation from the next time onwards.

[1676] "Emotion recognition means" refers to the technology and algorithms used to analyze a customer's facial expressions and voice data and recognize their emotions.

[1677] "Feedback Transmission Means" refers to the method or technology used to collect Customer Feedback and transmit that information to the Server.

[1678] The term "database reference means" refers to a mechanism or method for referencing a database that holds information such as the ingredients, appearance, and name of beverages, and obtaining the required information.

[1679] The present invention is a support system for bartenders to create original cocktails, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest cocktails that suit the user's psychological state. This system is configured to include a terminal operated by the user, a server, communication means, and the emotion engine. Specific embodiments of the system and their operation methods are described below.

[1680] In this system, users use devices such as tablets or smartphones to input customer requests. Requests can include the desired taste and appearance of the cocktail, the theme of the event, and so on. As the request is input, the emotion engine recognizes emotions from the user's facial expressions and voice. For example, facial expression and voice data can be collected through a camera or microphone, and analyzed to identify the user's psychological state.

[1681] After collecting the input and emotion information, the user clicks the send button. The device converts the request entered by the user and the emotion information recognized by the emotion engine into a data structure (e.g., JSON format) and sends it to the server via a communication method. Specific communication technologies used include the Internet and Wi-Fi.

[1682] The server receives the request data and emotional information sent from the terminal and analyzes them. Based on the analysis, the server uses a generation means to generate an optimal cocktail. The generation means uses a database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines a cocktail recipe, appearance image, and name that are appropriate for the request and emotional information. For example, the generation means uses a pre-registered cocktail database to select a recipe that uses white rum, coconut liqueur, pineapple juice, and grenadine syrup.

[1683] The generated cocktail information is then sent back to the terminal by the sending means. The terminal displays the returned cocktail information on the user interface. The user then creates a cocktail based on the displayed cocktail information and serves it to the customer.

[1684] A feedback transmission means is used to collect customer ratings and feedback. The user inputs customer feedback into the terminal and clicks the send button. For example, the user inputs feedback such as "This cocktail is very delicious." The feedback information is again transmitted to the server via the communication means.

[1685] The server reflects the feedback information received by the feedback transmission means in the learning means. The learning means adjusts the algorithms and parameters of the generation means based on the feedback information, improving performance for subsequent cocktail generation. Through this process, the more the system is used, the more it can generate cocktails that suit the bartender's personality and the customer's emotions.

[1686] As a concrete example, consider the following scenario.

[1687] Example 1: Cocktail generation based on customer requests and sentiment information

[1688] 1. A user uses a terminal to input a request such as "I want a sweet and fruity cocktail."

[1689] 2. The emotion engine recognizes the emotion of "happiness" from the user's facial expressions and voice.

[1690] 3. The user submits the input and emotion information by clicking the submit button.

[1691] 4. The device sends the request data and emotion information to the server.

[1692] 5. The server receives the request and emotion information and generates the optimal cocktail using the generation means.

[1693] Recipe: 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup

[1694] Look: Tropical imagery garnished with pineapple slices and mint leaves

[1695] Name: "Tropical Paradise"

[1696] 6. The server returns the information about the cocktail to the terminal.

[1697] 7. The device displays the cocktail information (recipe, image, name) on the user interface.

[1698] 8. The user creates a cocktail and serves it to the customer.

[1699] 9. A customer gives feedback saying, "This cocktail is delicious."

[1700] 10. The user enters the feedback into the terminal and clicks the send button.

[1701] 11. The device sends the feedback information to the server.

[1702] 12. The server receives the feedback information and uses the learning means to adjust the algorithm of the generating means.

[1703] This makes it possible to create cocktails based on the customer's emotional information and to gradually improve the system's performance.

[1704] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1705] Program processing flow (processing steps)

[1706] Step 1:

[1707] The user enters a request

[1708] Users use devices such as tablets and smartphones to input customer requests, including desired cocktail flavors and appearances, and the theme of the event. The input information is stored in text format in the device's memory.

[1709] input:

[1710] Request information such as cocktail taste, appearance, event theme, etc.

[1711] output:

[1712] The input request information is saved in text format.

[1713] Step 2:

[1714] Emotion engine recognizes emotions

[1715] The emotion engine uses the device's built-in camera and microphone to collect the user's facial expressions and voice data, which is then analyzed by an algorithm to recognize the user's emotional state (e.g., "joy").

[1716] input:

[1717] The user's facial expression data and voice data.

[1718] output:

[1719] The analyzed emotional state (e.g., "joy").

[1720] Specific behavior:

[1721] The camera captures the user's face and the microphone records the user's voice, and the emotion engine analyzes this data to identify their emotional state.

[1722] Step 3:

[1723] Sending requests and emotional information

[1724] The user clicks the send button to send the input request and the recognized emotion information to the server. The device converts this information into JSON format and sends it to the server via a communication method.

[1725] input:

[1726] Input request information and recognized emotion information.

[1727] output:

[1728] The request information and emotion information converted into JSON format are sent to the server.

[1729] Specific behavior:

[1730] When the send button is clicked, the device converts the text request information and emotional state into JSON format and sends it to the server via the Internet.

[1731] Step 4:

[1732] The server analyzes the data

[1733] The server receives the request data and emotional information sent from the device, analyzes them, and then selects the optimal cocktail based on the analysis using a query-based algorithm.

[1734] input:

[1735] Request and sentiment information in JSON format.

[1736] output:

[1737] The analysis results include a list of cocktail candidates and information on the optimal cocktail.

[1738] Specific behavior:

[1739] The server analyzes the request information and emotional information, and selects the most suitable cocktail by referring to existing cocktail information in a database.

[1740] Step 5:

[1741] Cocktail Creation

[1742] The generating means uses the database reference means to search for information such as the ingredients, appearance, and name of the drink, and determines the recipe, appearance image, and name of the cocktail that is suitable for the request and emotional information.

[1743] input:

[1744] Cocktail information such as drink ingredients, appearance, and name retrieved from a database.

[1745] output:

[1746] The best cocktail recipes, images and names.

[1747] Specific behavior:

[1748] The generator queries the database and extracts the cocktail information that best matches the request and emotional information (e.g., 40ml white rum, 20ml coconut liqueur, 60ml pineapple juice, 10ml grenadine syrup, name "Tropical Paradise", image).

[1749] Step 6:

[1750] Submit cocktail information

[1751] The server sends the generated cocktail information back to the terminal, which displays the received information on the user interface.

[1752] input:

[1753] The generated cocktail recipe, image, and name.

[1754] output:

[1755] The returned cocktail information will be displayed on the terminal.

[1756] Specific behavior:

[1757] The server sends the generated cocktail information back to the terminal, which receives the information and displays it on the user interface.

[1758] Step 7:

[1759] Cocktail creation and serving

[1760] The user actually creates a cocktail based on the displayed cocktail information and serves it to the customer.

[1761] input:

[1762] The cocktail recipe, image, and name are displayed on the device.

[1763] output:

[1764] Cocktails served to guests.

[1765] Specific behavior:

[1766] The user creates a cocktail according to the optimal cocktail recipe and serves it to the customer.

[1767] Step 8:

[1768] Collecting feedback

[1769] To collect customer ratings and feedback, the user enters the feedback into the terminal and clicks the submit button.

[1770] input:

[1771] Customer feedback information.

[1772] output:

[1773] The feedback information is stored on the device.

[1774] Specific behavior:

[1775] After the customer samples the cocktail, the user enters feedback into the terminal, such as "This cocktail is very delicious," and clicks the send button.

[1776] Step 9:

[1777] Give feedback and learn

[1778] The terminal again transmits the feedback information to the server via the communication means, and the server receives the feedback information and adjusts the algorithm and parameters of the generation means using the learning means.

[1779] input:

[1780] Customer feedback information.

[1781] output:

[1782] Adjusted generation algorithm.

[1783] Specific behavior:

[1784] The terminal transmits feedback information to the server, which receives it and adjusts the algorithms and parameters using a learning means.

[1785] (Application example 2)

[1786] 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."

[1787] Conventional cocktail creation systems did not adequately consider the customer's emotional state when suggesting drinks, leaving the user experience unsatisfactory. Furthermore, there was no mechanism for collecting feedback and continuously improving the system, making it difficult for bartenders to improve the quality of the cocktails they served. Furthermore, the lack of a real-time cocktail creation guide sometimes caused bartenders to struggle.

[1788] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes terminal means for inputting the user's preferences, communication means for transmitting the input preferences to the server, emotion recognition means for recognizing emotions from the user's facial expressions and voice, generation means for generating an optimal cocktail based on the preferences and emotion information, transmission means for returning information including the recipe, visual image, and name of the generated cocktail to the terminal, and learning means for reflecting feedback information in the generation means. This enables the system to suggest optimal cocktails taking the user's emotion information into consideration, provide cocktail creation guidance in real time, and continuously improve the system based on feedback information.

[1789] "Terminal means" refers to a device for inputting customer preferences, including tablets and smartphones.

[1790] "Communication means" refers to a method for transmitting the input preference to the server, and includes internet communication, wireless communication, and the like.

[1791] "Emotion recognition means" refers to technology or devices that recognize emotions from the user's facial expressions and voice.

[1792] The "creation means" is a method or device for creating an optimal cocktail based on the desire and emotional information.

[1793] The "transmission means" refers to a method or device for returning information including the recipe, image of the appearance, and name of the created cocktail to the terminal.

[1794] The "learning means" is an algorithm or device for making the generating means reflect feedback information.

[1795] The "feedback transmission means" refers to a method or device for transmitting customer feedback collected by the terminal means to the server.

[1796] "Database reference means" refers to a method or device for referencing a database to generate the ingredients, appearance, and name of a beverage.

[1797] This invention functions as a support system for bartenders to create original cocktails. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can suggest cocktails that suit the user's psychological state. Below, an embodiment of the system and its specific operation method are described.

[1798] The system for realizing the present invention uses smartphones, tablets, smart glasses, and servers as hardware, and an emotion recognition engine, a cocktail database, and a communication platform (e.g., HTTP communication means) as software.

[1799] First, the user (bartender) inputs the customer's preferences using a terminal such as a smartphone or tablet. These preferences include the desired taste and appearance of the cocktail, the theme of the event, etc. Next, an emotion recognition tool is used to recognize the customer's emotions from their facial expressions and voice. The software uses face recognition technology and voice analysis technology to do this.

[1800] When the user presses the send button for the preference and emotion information, the device converts this information into JSON format and sends it to the server via the communication means. The server analyzes the received information and generates an optimal cocktail using the generation means. This generation means refers to a cocktail database and searches for information such as the drink's ingredients, appearance, and name. Once the optimal cocktail is generated, the server returns the recipe, an image of the appearance, and the name of the generated cocktail to the device via the transmission means.

[1801] The user creates a cocktail based on the information displayed on the terminal and serves it to the customer. The terminal collects the customer's reactions and feedback and sends them back to the server. The server then reflects this feedback information in the learning means and adjusts the algorithms and parameters of the generation means. This allows the system to improve its performance in subsequent cocktail generation.

[1802] For a concrete scenario, the following prompt sentences can be used:

[1803] If a user requests a "sweet and fruity cocktail" and the emotion engine recognizes "joy," the server will suggest a recipe for the perfect cocktail, "Tropical Paradise." The prompt text is entered as follows:

[1804] Generate the best cocktail based on the following desires and emotions: Desire: Sweet and fruity cocktail, Emotion: Joy

[1805] As described above, the present invention can provide original cocktails creatively and effectively while taking into consideration the emotional information of the bartender and the customer.

[1806] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1807] Step 1:

[1808] The user inputs their preferences using a terminal such as a smartphone or tablet. This input includes preferences for the taste and appearance of the cocktail, the theme of the event, etc. The input preferences are saved as text information on the terminal.

[1809] Step 2:

[1810] The terminal uses emotion recognition means to recognize emotions from the customer's facial expressions and voice. The emotion recognition means uses a camera and microphone to collect facial and voice data, which is then analyzed by the Emotion Engine. This allows emotions such as "happiness" and "sadness" to be recognized.

[1811] Step 3:

[1812] The user presses the send button to send their input and emotion information. At this time, the device converts their wishes and emotion information into JSON format and sends it to the server via a communication method. The input data is sent to the server as an HTTP request.

[1813] Step 4:

[1814] The server receives the request data and emotion information sent from the device, parses the received data, and extracts the desired content and emotion.

[1815] Step 5:

[1816] The server's generation means generates the optimal cocktail based on the received desired data and emotional information. The generation process involves referencing a cocktail database to search for the ingredients, appearance, and name of a drink that matches the criteria. For example, based on the data "sweet and fruity cocktail, emotion: joy," a cocktail called "Tropical Paradise" is selected.

[1817] Step 6:

[1818] The server sends information about the created cocktail (recipe, image, name) to the terminal via a transmission means. The query result is sent to the terminal in JSON format.

[1819] Step 7:

[1820] The terminal receives the cocktail information returned from the server and displays it on the user interface. At this time, the cocktail recipe, image of its appearance, and name are displayed on the screen. The user creates a cocktail based on this information.

[1821] Step 8:

[1822] The cocktail created by the user is served to the customer, and feedback such as "very delicious" is collected from the customer. The terminal means inputs and saves the feedback information as text data.

[1823] Step 9:

[1824] The feedback information collected by the terminal means is sent to the server using the feedback sending means. The feedback data is also sent to the server in JSON format.

[1825] Step 10:

[1826] The server analyzes the received feedback information and reflects it in the learning process. Based on the feedback data, the algorithm and parameters of the generation process are adjusted to improve the accuracy of cocktail generation from the next time onwards. This allows the system to improve with each use.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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).

[1834] 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.

[1835] 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."

[1836] 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.

[1837] 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).

[1838] 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.

[1839] 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.

[1840] 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.

[1841] 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.

[1842] 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.

[1843] 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.

[1844] 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.

[1845] 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.

[1846] 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.

[1847] 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.

[1848] The following is further disclosed regarding the above embodiment.

[1849] (Claim 1)

[1850] a terminal means for inputting customer preferences;

[1851] communication means for transmitting the input preference to a server;

[1852] A means for generating an optimal cocktail based on the desire;

[1853] a transmitting means for returning information including the recipe, the image of the appearance, and the name of the generated cocktail to the terminal;

[1854] learning means for reflecting feedback information in the generating means;

[1855] A system including:

[1856] (Claim 2)

[1857] 2. The system according to claim 1, further comprising a feedback sending means for sending customer feedback collected by said terminal means to said server.

[1858] (Claim 3)

[1859] 2. The system of claim 1, wherein said generating means includes database lookup means for generating beverage ingredients, appearances, and names.

[1860] "Example 1"

[1861] (Claim 1)

[1862] a terminal means operated by a user;

[1863] a communication means for transmitting the customer's request input via the terminal means to a server;

[1864] A means for generating an optimal cocktail based on the desire;

[1865] a transmitting means for returning information including the recipe, the image of the appearance, and the name of the generated cocktail to the terminal;

[1866] learning means for reflecting feedback information in the generating means;

[1867] A system including:

[1868] (Claim 2)

[1869] 2. The system according to claim 1, further comprising a feedback sending means for sending customer feedback collected by said terminal means to said server.

[1870] (Claim 3)

[1871] 2. The system of claim 1, wherein said generating means includes database lookup means for generating beverage ingredients, appearances, and names.

[1872] (Claim 4)

[1873] 2. The system of claim 1, wherein the generating means includes a learning means for utilizing feedback information to adjust the algorithms and parameters of the generating means to improve subsequent cocktail generation.

[1874] (Claim 5)

[1875] 2. The system according to claim 1, wherein said communication means includes means for performing data communication between the terminal and the server using the HTTP protocol.

[1876] (Claim 6)

[1877] 10. The system of claim 1, wherein the terminal means includes means for displaying cocktail recipes, images, and names through a user interface.

[1878] "Application Example 1"

[1879] (Claim 1)

[1880] a terminal means for inputting customer preferences;

[1881] communication means for transmitting the input preference to a server;

[1882] A means for generating an optimal cocktail based on the desire;

[1883] a transmitting means for returning information including the recipe, the image of the appearance, and the name of the generated cocktail to the terminal;

[1884] learning means for reflecting feedback information in the generating means;

[1885] A generation means for generating cocktail information using a cocktail generation AI model based on customer requests;

[1886] A system including:

[1887] (Claim 2)

[1888] 2. The system according to claim 1, wherein customer feedback collected by said terminal means is transmitted to said server, and the feedback information is reflected in said generating means.

[1889] (Claim 3)

[1890] 2. The system according to claim 1, wherein the generating means includes a database reference means for generating ingredients, appearance, and name of the beverage using a prompt sentence.

[1891] "Example 2: Combining Emotion Engines"

[1892] (Claim 1)

[1893] a terminal means for inputting customer preferences;

[1894] a communication means for transmitting the input desire and emotion information to a server;

[1895] A means for generating an optimal cocktail based on the desire and emotion information;

[1896] a transmitting means for returning information including the recipe, the image of the appearance, and the name of the generated cocktail to the terminal;

[1897] learning means for reflecting feedback information in the generating means;

[1898] Emotion recognition means for analyzing facial expressions and voice data of customers to recognize their emotions;

[1899] A system including:

[1900] (Claim 2)

[1901] 2. The system according to claim 1, further comprising a feedback sending means for sending customer feedback collected by said terminal means to said server.

[1902] (Claim 3)

[1903] 2. The system of claim 1, wherein said generating means includes database lookup means for generating beverage ingredients, appearances, and names.

[1904] "Application example 2 when combining emotion engines"

[1905] (Claim 1)

[1906] a terminal means for inputting customer preferences;

[1907] communication means for transmitting the input preference to a server;

[1908] emotion recognition means for recognizing emotions from facial expressions and voice of a user;

[1909] A means for generating an optimal cocktail based on the desire and emotion information;

[1910] a transmitting means for returning information including the recipe, the image of the appearance, and the name of the generated cocktail to the terminal;

[1911] learning means for reflecting feedback information in the generating means;

[1912] A system including:

[1913] (Claim 2)

[1914] 2. The system according to claim 1, further comprising a feedback sending means for sending customer feedback collected by said terminal means to said server.

[1915] (Claim 3)

[1916] 2. The system of claim 1, wherein said generating means includes database lookup means for generating beverage ingredients, appearances, and names. [Explanation of symbols]

[1917] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal means for inputting customer preferences; communication means for transmitting the input preference to a server; A means for generating an optimal cocktail based on the desire; a transmitting means for returning information including the recipe, the image of the appearance, and the name of the generated cocktail to the terminal; learning means for reflecting feedback information in the generating means; A system including:

2. 2. The system according to claim 1, further comprising a feedback sending means for sending customer feedback collected by said terminal means to said server.

3. 2. The system of claim 1, wherein said generating means includes database lookup means for generating beverage ingredients, appearances, and names.

Images