system

Abstract

We provide the system. [Solution] An interface means that receives information entered by the user and converts it into text data, This text data is analyzed and a processing means is used to generate multiple suggested candidates based on the user's preferences. A presentation means that presents proposed options to the user and outputs the selected information according to the user's choice, A mixing means that automatically generates instructions for mixing beverages based on user selection and transmits them to the device, A means of receiving user feedback and updating the accumulated database, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the selection of cocktails, it is required to provide beverages that suit the preferences and current moods of each user. However, there is a problem that it is difficult to respond quickly and accurately with conventional manual methods. Also, while a flexible system is needed to cope with changes in user preferences, it is required to solve the problem that such a system has not existed so far.

Means for Solving the Problems

[0005] This invention provides a system that analyzes text data using natural language processing technology based on user input information and automatically suggests the optimal cocktail according to the user's preferences and mood. Specifically, by combining interface means, analysis means, presentation means, blending means, and storage means, the system generates and presents multiple cocktail candidates, taking into account the user's past selection history and trend information, and quickly blends the beverage based on the user's selection. Furthermore, by accumulating user feedback and adjusting the suggestion algorithm in real time, the system provides a personalized experience.

[0006] A "user" refers to a person who uses the system to receive cocktail suggestions.

[0007] "Input information" refers to information such as preferences, mood, and allergies that users provide to the system in text or voice.

[0008] "Interface means" refers to a function that receives input information from the user and converts speech into text data as needed.

[0009] "Analysis means" refers to a function that analyzes received text data and uses natural language processing technology to understand the user's preferences and mood.

[0010] "Presentation method" refers to a function that presents multiple cocktail suggestions to the user based on the analysis results and accepts their selection.

[0011] "Blending means" refers to the function that generates instructions for automatically blending the cocktail selected by the user and transmits them to the device.

[0012] "Storage method" refers to a function that saves user feedback in a database and uses it to improve the accuracy of cocktail suggestions in the future.

[0013] "Past selection history" refers to the cocktails and related data that the user has selected in the past.

[0014] "Trend information" refers to information on popular cocktails and new beverages among the market and users.

[0015] "Proposed algorithm" refers to a calculation procedure for selecting cocktails suitable for users based on the analyzed input information.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiment for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a system that automates the process of assisting a user in selecting a cocktail, and the system comprises an interface means, an analysis means, a presentation means, a mixing means, and a storage means. The operation of this system can be described as follows.

[0038] When a user inputs information such as their mood or preferences via voice or text through the device, that input information is captured by the interface. In the case of voice input, the device uses speech recognition technology to convert it into text data.

[0039] The server analyzes text data acquired through the interface using natural language processing technology. The analysis accurately grasps the user's mood and preferences and compares them with past selection history and trend information. Based on this information, the server generates multiple cocktail suggestions that are best suited to the user.

[0040] The terminal displays cocktail suggestions sent from the server to the user, allowing the user to select from the suggested options. Once the user selects a cocktail, that information is sent to the mixing device, and the server sends precise instructions to the cocktail machine, which automatically mixes the selected cocktail.

[0041] Furthermore, feedback provided by users after the blending process is transmitted from the terminal to the server via a storage mechanism and stored in a database. This stored data is used to improve future suggestions and enable more personalized suggestions for users.

[0042] For example, if a user enters "I want a refreshing cocktail," the server analyzes this information and generates options such as "Mojito" or "Gin and Tonic" based on past selection history and trend information. If the user selects Mojito, that selection is quickly realized through the mixing process and served.

[0043] In this way, the present invention makes it possible to quickly provide a personalized cocktail experience for each user.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users input their mood, preferences, and allergy information via voice or text through their device. The device uses speech recognition technology to convert the voice input into text data.

[0047] Step 2:

[0048] The terminal sends the converted text data to the server. The server receives this data and begins natural language processing.

[0049] Step 3:

[0050] The server analyzes text data to extract user preferences and keywords. It also checks the user's past selection history by referring to past databases and queries for trend information.

[0051] Step 4:

[0052] Based on the extracted information, the server runs an algorithm to generate multiple cocktail suggestions and creates a list of candidates.

[0053] Step 5:

[0054] The server sends a list of potential cocktails to the terminal. The terminal then presents this list to the user, allowing them to make a selection.

[0055] Step 6:

[0056] The user selects their desired cocktail from the presented cocktail options. The terminal then sends this selection to the server.

[0057] Step 7:

[0058] The server generates instructions for mixing the selected cocktail and sends those instructions to the cocktail machine.

[0059] Step 8:

[0060] The cocktail machine automatically mixes and serves cocktails based on instructions.

[0061] Step 9:

[0062] After receiving their cocktail, the user enters feedback via a terminal. The terminal then sends this feedback to the server.

[0063] Step 10:

[0064] The server collects feedback and updates the database. This information is used to improve the accuracy of future suggestions.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] Choosing the optimal beverage based on a user's mood and preferences can be complicated by the sheer number of options and the diversity of individual tastes. Furthermore, providing personalized recommendations quickly while considering past selection history and trends is challenging. Moreover, effectively utilizing user feedback and continuously improving the quality of recommendations is essential.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes a medium for receiving information entered by the user and converting voice data into text data, a technology for analyzing the user's preferences using natural language processing technology, and means for generating multiple suggestions using a generative AI model. This enables the rapid and appropriate suggestion of beverages based on the user's preferences.

[0070] A "user" is an individual who uses the system to select and evaluate beverages based on their preferences.

[0071] "Interface means" refers to a device or software that receives information input by a user and converts audio data into text data as needed.

[0072] "Natural language processing technology" is a technique that analyzes text data obtained from users, understands its content, and identifies the user's preferences.

[0073] A "generative AI model" is an artificial intelligence technology used to create multiple suggestions, generating the optimal candidates based on the user's past selection history and trends.

[0074] A "presentation means" is a mechanism for visually or audibly informing the user of the generated proposal.

[0075] "Blending means" refers to a device or process that automatically generates instructions for blending a beverage based on the user's selection and transmits them to a device.

[0076] A "storage method" refers to a means of receiving user feedback, storing it in a database, and using it to improve future proposals.

[0077] "Past selection history" refers to a record of suggestions previously selected by the user, and is used as reference when generating future suggestion candidates.

[0078] "Trend information" refers to data that indicates current trends in the market and society, and is used as a reference when generating potential proposals.

[0079] "Feedback" refers to user evaluations and opinions on suggested beverages, and is information that can be used to improve the system.

[0080] This invention is a system for quickly suggesting beverages tailored to user preferences and automating the blending process. The system mainly consists of a user terminal, a server, and external blending equipment.

[0081] The user uses a device to input their mood and preferences for the day via voice or text. The device uses speech recognition technology (e.g., a common speech recognition API) for voice input, converting the voice into text data. This allows smartphones or dedicated devices to be used as the user interface.

[0082] Next, the device sends text data to the server. The server analyzes this data using natural language processing techniques (e.g., a general natural language processing library) to understand the user's mood and preferences. Based on this analysis, it refers to past selection history and trend information, and uses a generative AI model to generate suggested beverages that are best suited to the user.

[0083] The generated suggestions are displayed to the user via a terminal, and the user selects their desired beverage from among them. This selection information is transmitted via a server to an external mixing device. The server then issues instructions to the mixing device to accurately mix the selected beverage. As a result, the beverage is automatically mixed and served to the user.

[0084] Furthermore, after the beverage is served, the user sends feedback via their device. This feedback information is stored in a database and used to improve the accuracy of future suggestions, making it possible to provide users with a more personalized experience.

[0085] For example, if a user enters "I want a refreshing cocktail" into their device, the server uses this information and, based on the results of analysis using natural language processing technology, generates suggestions such as "Mojito" or "Gin and Tonic." If the user selects Mojito, the mixing machine automatically creates a Mojito based on that information. The generative AI model that underpins this process is continuously improved based on user feedback.

[0086] An example of a prompt to input into a generative AI model is: "Generate options to suggest the best cocktail based on the user's preferences. User's mood: Refreshing."

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user inputs their mood and preferences for the day into the device. Input can be done via voice or text. In the case of voice input, the device uses speech recognition technology to convert the voice into text data. At this stage, the input is raw voice or text data, and the output is the converted text data.

[0090] Step 2:

[0091] The terminal sends text data obtained from the user to the server. The server receives this data and analyzes it using natural language processing techniques. Through this analysis, the user's mood and preferences are identified. The input is text data from the terminal, and the output is preference information extracted through the analysis.

[0092] Step 3:

[0093] The server uses the analysis results to reference the user's past selection history and trend information, and generates multiple suggested options using a generative AI model. The generative AI model uses prompt sentences to generate suggestions that match the user's preferences. Specifically, prompt sentences such as "User's mood: Refreshing" are used. The input is preference information and historical data, and the output is a list of suggested cocktails.

[0094] Step 4:

[0095] The terminal presents the user with a list of suggested beverages received from the server. The user selects their desired beverage from the presented options. At this stage, the input is the list of suggestions from the server, and the output is the beverage information selected by the user.

[0096] Step 5:

[0097] The server transmits instructions for preparing beverages to an external mixing device based on the user's selection. Specifically, it transmits commands to the device based on the selected beverage recipe. The input is the user's selection information, and the output is the commands to the mixing device.

[0098] Step 6:

[0099] After being served their beverage, the user enters feedback into a terminal. The terminal sends this feedback to a server, which stores the feedback information in a database. The input is the user's feedback, and the output is the updated database information. This information is used to inform future suggestions.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] When users choose a cocktail at a bar or restaurant, there is a need for an appropriate support system that can quickly select a beverage that suits their preferences and mood, and automatically mix it for them. Traditional methods often present challenges, such as making difficult choices and providing personalized suggestions.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes terminal means for receiving emotional information input by the user and converting it into information data, calculation means for generating a number of options based on the user's preferences, and display means for presenting the options to the user and outputting the selected information according to the user's choice. This enables the rapid and accurate provision of cocktails based on the individual user's preferences.

[0105] "Emotional information" refers to information related to the user's mood and preferences, and is data entered as voice or text.

[0106] "Terminal means" refers to devices or systems that receive input information from users and perform data conversion as needed.

[0107] "Information data" refers to digital data obtained by converting a user's emotional information into a specific format.

[0108] "Computational means" refers to systems or algorithms that perform processing to generate a large number of options based on user preferences.

[0109] "Options" refer to multiple suggested choices presented to the user, which are generated based on specific conditions.

[0110] "Display means" refers to devices or interfaces that provide users with options and further support the user's selection.

[0111] The following describes embodiments for carrying out the invention. This invention is a system that personalizes the user's cocktail selection experience and provides it quickly and accurately. This system is composed of multiple hardware and software components.

[0112] First, the "terminal device" receives emotional information from the user in the form of voice or text. This device uses the Google® Speech-to-Text API to convert voice input into text data. In addition, the user's mood and preferences, as emotional information, are processed as digital data.

[0113] Next, the "server" analyzes this information data using the Google Cloud Natural Language API. Based on the analyzed data, the "computational tool" uses OpenAI's GPT model to generate a large number of cocktail options based on the user's preferences.

[0114] The "display means" presents the generated options to the user and assists the user in selecting a specific cocktail. The selected information is then output to the user in an appropriate format.

[0115] After the user selects a cocktail, the server transmits mixing instructions to the automated cocktail machine via a "control system." The cocktail is then automatically mixed and served. Finally, user feedback is received, and the accumulated records are updated based on that feedback.

[0116] As a concrete example, a user might input "I'd like a refreshing cocktail." This prompt is parsed and processed using the Google Cloud Natural Language API and OpenAI's GPT model, and suggested cocktails include options such as "Mojito" and "Margarita." The cocktail selected by the user is automatically brewed and served quickly.

[0117] An example of a prompt to the generating AI model is: "Based on the user's preference information, please suggest the cocktail that best suits their current mood. Please also take into account past selection history and trends." In this way, the present invention realizes a cocktail service that is responsive to the individual preferences of the user.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device receives either user voice or text input. In the case of voice input, the Google Speech-to-Text API is used to convert the voice data into text data. In this case, the input is the user's speech, and the output is the transcribed information.

[0121] Step 2:

[0122] The server receives text data sent from the terminal and performs analysis using the Google Cloud Natural Language API. The input is text data, and the system processes it to extract user preferences and moods, outputting the extracted preference information as the analysis result.

[0123] Step 3:

[0124] Based on the analysis results, the server uses OpenAI's GPT model to generate cocktail options that suit the user's preferences. The input at this stage is the analyzed preference information, and past selection history and trend information are also taken into consideration, resulting in the output of multiple cocktail suggestions.

[0125] Step 4:

[0126] The server sends a list of cocktail options to the terminal, and the "display device" presents them to the user. The input is cocktail suggestions from the server, and the output is the options presented to the user.

[0127] Step 5:

[0128] The user selects one of their preferred cocktails from the presented options. In this step, the user's selection is the input, and the information of this selected cocktail is the output.

[0129] Step 6:

[0130] The server controls the automated cocktail machine to send mixing instructions based on the user's selection. The input here is information about the selected cocktail, and the output is an automated mixing instruction.

[0131] Step 7:

[0132] The device receives user feedback, sends it to the server, and updates the record. In this process, the input is user feedback information, and the output is the updated record data.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is an automated cocktail suggestion system that incorporates an emotion engine to recognize the user's emotions, thereby enabling personalized suggestions tailored to the user's emotional state. Specific embodiments of the system are described below.

[0135] When a user inputs their current mood or preferences via voice or text through the device, the device receives it and converts it into text using speech recognition technology. At this point, the emotion engine activates and detects the user's emotional state from their voice or text. For example, it analyzes emotions such as "happy" or "tired" from the tone and rhythm of the voice and the words used in the text.

[0136] The server analyzes the text data received from the user, along with the results of the emotion engine's analysis, to generate cocktail suggestions based on the user's preferences and emotions. An algorithm is executed that queries the user's past selection history and trend information to form multiple cocktail candidates that match their emotions.

[0137] The generated suggestions are presented to the user from the terminal, and the user can choose their desired cocktail from the presented options. The selected information is sent to the server, and instructions are sent to the cocktail machine via the mixing mechanism. As a result, the selected cocktail is automatically mixed and served.

[0138] Meanwhile, after the cocktail is served, users can input feedback via a terminal. This feedback is re-analyzed by the emotion engine, and new emotion data is stored in the database. This data is used to improve the accuracy of future suggestions and is used to adjust the selection criteria for suggested cocktails in real time.

[0139] For example, if a user enters "I want something to cheer me up today," the emotion engine detects a positive emotion from the user's input. Based on this, the server compares it with past selection history and popular trends to generate suggestions such as "a cocktail based on an energy drink." In this way, the present invention aims to provide an experience closely related to the user's emotions.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user inputs their mood or preferences via voice or text through the device. The device receives the voice data and, in the case of voice input, converts it into text data using speech recognition technology.

[0143] Step 2:

[0144] The device sends the received text data to the server. The emotion engine also analyzes the voice and text data to extract the user's emotional state.

[0145] Step 3:

[0146] The server analyzes the received text data to understand the user's preferences, and, taking into account the emotional information obtained by the emotion engine, generates multiple cocktail suggestions.

[0147] Step 4:

[0148] The server references the user's past selection history and current trend information to create a list of cocktail candidates best suited to their emotional state and preferences.

[0149] Step 5:

[0150] The server sends a list of generated cocktail candidates to the terminal, which then presents the list to the user visually or audibly.

[0151] Step 6:

[0152] The user selects their desired cocktail from the presented options. The terminal sends the selection information to the server.

[0153] Step 7:

[0154] The server generates instructions for the cocktail machine based on the user's selection and transmits those instructions via the mixing device.

[0155] Step 8:

[0156] The cocktail machine automatically mixes cocktails according to the server's instructions and serves them to the user.

[0157] Step 9:

[0158] After receiving their cocktail, users input feedback such as their satisfaction level and comments via a terminal. The terminal then sends this feedback to the server.

[0159] Step 10:

[0160] The server re-analyzes the received feedback using an emotion engine and updates the database. This updated data is used to improve the accuracy of future suggestions and adjust the suggestion criteria in real time.

[0161] (Example 2)

[0162] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0163] In today's world, systems that provide appropriate suggestions based on user emotions are rare, and providing highly accurate suggestions tailored to individual emotional states is difficult. Furthermore, conventional systems are limited to generating suggestions based on user selection history and trends, lacking the flexibility to consider user emotional states. This leads to a decline in the quality of the user experience and makes it difficult to increase satisfaction.

[0164] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0165] In this invention, the server includes data processing means for analyzing user emotional information and generating suggestions based thereon, generation means for optimizing by combining the user's past selection history and trend information, and data management means for analyzing feedback and adjusting the criteria for suggestions in real time. This enables optimal suggestions that respond to the diverse emotions of the user.

[0166] "Data processing means" refers to a device or process that receives voice or text input from a user and converts that data into an analyzable format as needed.

[0167] "Analysis means" refers to technologies and systems for identifying and detecting a user's emotional state from received text data.

[0168] "Generation means" refers to algorithms and devices that create multiple suggested options based on the user's emotional state, selection history, and trend information, and then provide the optimal suggestion.

[0169] A "presentation means" is a device or interface that provides the generated proposal to the user visually or audibly and accepts the user's selection.

[0170] "Blending instruction means" refers to a device or process that generates instructions for automatically blending a beverage based on a user's selection and transmits these instructions to a machine.

[0171] "Data management means" refers to a device that includes functions and analytical techniques for receiving user feedback, updating the database, and improving the accuracy of future suggestions.

[0172] This invention is an automated suggestion system that provides personalized cocktail suggestions based on the user's emotional state. Specific embodiments of the system are described below.

[0173] Terminal role:

[0174] The user inputs their emotional state and preferences via voice or text through the device. The device converts this input into text data using speech recognition software (e.g., a speech-to-text API). The converted data is then analyzed by an emotion engine to detect the user's emotional state.

[0175] Server role:

[0176] Based on the analysis results sent from the emotion engine, the server generates multiple cocktail suggestion candidates, taking into account the user's emotional state, past selection history, and trend information. Machine learning algorithms (e.g., AI model frameworks) are used for suggestion generation, and optimized suggestions are provided to the user.

[0177] Specific example:

[0178] For example, if a user enters "I want to refresh myself today," the emotion engine detects the emotion "refreshment" from this input. The server compares the analysis results with past selection history and current beverage trend information to generate suggestions such as "mint-based cocktails."

[0179] Example of a prompt:

[0180] "Please write a program that recognizes the user's emotions and generates cocktail suggestions based on those emotions."

[0181] Thus, the present invention aims to provide a more refined user experience through suggestions that respond to the user's emotions.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] Users input their emotional state and desires via voice or text through a device. This input is converted into text data using speech recognition software. The input mainly consists of the user's mood and image of the beverage they want, and the output is the converted text data.

[0185] Step 2:

[0186] The device sends text data to an emotion engine, which uses natural language processing technology to analyze the user's emotional state. Specifically, it analyzes words and phrases in the text and extracts emotional keywords such as "refreshed" and "relaxed." The input is the converted text data, and the output is the user's identified emotional state.

[0187] Step 3:

[0188] The server receives the analysis results from the emotion engine and generates cocktail suggestion candidates, taking into account the user's past selection history and trend information. The algorithm used is an AI model framework that executes database queries to obtain the necessary data. The inputs are emotion state, selection history, and trend information, and the output is a list of suggested cocktail candidates.

[0189] Step 4:

[0190] The terminal presents the user with cocktail suggestions obtained from the server. Specifically, it displays multiple suggested cocktails on the screen and prompts the user to make a selection visually or audibly. The input is the cocktail options sent from the server, and the output is the cocktail information selected by the user.

[0191] Step 5:

[0192] The server receives the user's selection and sends mixing instructions to the cocktail machine. Specifically, it generates data that specifies the quantities and order of each ingredient based on the selected cocktail's recipe. The input is the cocktail selected by the user, and the output is the specific mixing instructions sent to the cocktail machine.

[0193] Step 6:

[0194] After enjoying their cocktail, the user enters feedback into the device. This feedback is then analyzed again by the emotion engine to extract the user's impressions and areas for improvement. The input is the user's feedback, and the output is the analyzed impression data.

[0195] Step 7:

[0196] The server stores the analyzed feedback in a database and uses it to improve the accuracy of future suggestions. Specifically, it compares and analyzes past data and dynamically adjusts the algorithm settings. The input is the analyzed feedback data, and the output is updated database information.

[0197] (Application Example 2)

[0198] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0199] It is difficult to suggest the most suitable beverage based on the user's emotional state, and there is a lack of means to improve the accuracy of service provision based on user preferences. Furthermore, there is a need to utilize user feedback in real time to improve the accuracy of suggested options.

[0200] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0201] In this invention, the server includes communication means for receiving user input information and converting it into voice or text data, emotion analysis means for recognizing the emotional state from the analyzed data, and processing means for generating multiple suggestion candidates based on the analysis results and preferences. This makes it possible to provide personalized suggestions that are in line with the user's emotions and improve the quality of the service.

[0202] A "user" refers to an individual or group that uses the system, and is the entity that receives suggestions based on their emotional state and preferences.

[0203] "Communication means" refers to the means of receiving voice or text information from a user and incorporating it into the system as data.

[0204] "Emotion analysis means" refers to a method of identifying emotions using user voice and text data and performing analysis in order to make appropriate service suggestions.

[0205] A "processing means" is a means that has the function of generating multiple suggested options based on the results of sentiment analysis and the user's preferences.

[0206] A "suggestion" refers to a set of multiple service or product options created based on the user's emotional state and preferences.

[0207] A "storage method" refers to a means of saving user feedback and updating information resources.

[0208] The system for realizing this application includes a terminal that receives user input information and a server that performs analysis and makes suggestions. First, the user inputs their current mood and preferences via voice or text through a smartphone app. The terminal receives this information and, if voice input is received, converts it into text data using speech recognition software. This process uses general speech recognition technology.

[0209] Next, the server receives this text data and uses its sentiment analysis engine to recognize the user's emotional state. The sentiment analysis engine analyzes the vocabulary and context used in the text to determine emotions such as "positive," "negative," or "relaxed."

[0210] Next, the server uses the analysis results to run an algorithm that takes into account the user's past selection history and current trend information, generating multiple suggested options. By using well-known algorithms and machine learning models, it is possible to provide suggestions that best match the user's preferences.

[0211] The generated suggested cocktails are sent to the terminal and presented to the user. When the user selects their desired cocktail, that information is returned to the server, and automated instructions for mixing the cocktail are sent to the mixing device. As a result, the selected beverage is automatically mixed and served to the user.

[0212] After a beverage is served, users can provide feedback through the app. This feedback is re-analyzed on the server side, compared with the sentiment analysis results, and the information resources are updated. This makes it possible to continuously improve the accuracy of suggestions for future orders. For example, if a user inputs "I want something refreshing," the sentiment engine will detect "relaxation," and the server will suggest something like a "herbal tea-based cocktail" based on past data and trends.

[0213] An example of a generated AI model prompt is: "Identify the user's emotion from the text 'Something to cheer me up today' and suggest a suitable cocktail."

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The terminal receives input information from the user. This input is provided via a smartphone application, either as voice or text. In the case of voice input, the terminal uses voice recognition software to convert the voice into text data. This text data then becomes the input for the next processing step.

[0217] Step 2:

[0218] The server analyzes the text data received from the terminal. Using an emotion analysis engine, it evaluates the vocabulary and context within the text to determine the user's emotional state. In this process, emotional information such as positive, negative, and relaxed is output.

[0219] Step 3:

[0220] Based on the analyzed sentiment information, the server generates suggested options by referencing the user's past selection history and the latest trend information. By applying the algorithm, multiple beverage options that best match the user's current sentiment and preferences are generated and output to the next step.

[0221] Step 4:

[0222] The terminal presents the user with suggested cocktails from the server. Once the user selects their desired cocktail from the presented options, this selection information is sent back from the terminal to the server. This selection information then becomes the input for the next process.

[0223] Step 5:

[0224] The server receives the user's selection information and generates automatic mixing instructions. These instructions are sent to the cooking device, which automatically creates the selected beverage. This process ensures that the user receives the perfect cocktail.

[0225] Step 6:

[0226] The server receives feedback information from the user. The feedback is then analyzed again for sentiment, comparing the user's emotions with the accuracy of the suggestions, and updating the system's information resources. This improves the accuracy of future suggestions. Through this feedback process, the system continuously learns and improves.

[0227] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] This invention relates to a system that automates the process of assisting a user in selecting a cocktail, and the system comprises an interface means, an analysis means, a presentation means, a mixing means, and a storage means. The operation of this system can be described as follows.

[0244] When a user inputs information such as their mood or preferences via voice or text through the device, that input information is captured by the interface. In the case of voice input, the device uses speech recognition technology to convert it into text data.

[0245] The server analyzes text data acquired through the interface using natural language processing technology. The analysis accurately grasps the user's mood and preferences and compares them with past selection history and trend information. Based on this information, the server generates multiple cocktail suggestions that are best suited to the user.

[0246] The terminal displays cocktail suggestions sent from the server to the user, allowing the user to select from the suggested options. Once the user selects a cocktail, that information is sent to the mixing device, and the server sends precise instructions to the cocktail machine, which automatically mixes the selected cocktail.

[0247] Furthermore, feedback provided by users after the blending process is transmitted from the terminal to the server via a storage mechanism and stored in a database. This stored data is used to improve future suggestions and enable more personalized suggestions for users.

[0248] For example, if a user enters "I want a refreshing cocktail," the server analyzes this information and generates options such as "Mojito" or "Gin and Tonic" based on past selection history and trend information. If the user selects Mojito, that selection is quickly realized through the mixing process and served.

[0249] In this way, the present invention makes it possible to quickly provide a personalized cocktail experience for each user.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] Users input their mood, preferences, and allergy information via voice or text through their device. The device uses speech recognition technology to convert the voice input into text data.

[0253] Step 2:

[0254] The terminal sends the converted text data to the server. The server receives this data and begins natural language processing.

[0255] Step 3:

[0256] The server analyzes text data to extract user preferences and keywords. It also checks the user's past selection history by referring to past databases and queries for trend information.

[0257] Step 4:

[0258] Based on the extracted information, the server runs an algorithm to generate multiple cocktail suggestions and creates a list of candidates.

[0259] Step 5:

[0260] The server sends a list of potential cocktails to the terminal. The terminal then presents this list to the user, allowing them to make a selection.

[0261] Step 6:

[0262] The user selects their desired cocktail from the presented cocktail options. The terminal then sends this selection to the server.

[0263] Step 7:

[0264] The server generates instructions for mixing the selected cocktail and sends those instructions to the cocktail machine.

[0265] Step 8:

[0266] The cocktail machine automatically mixes and serves cocktails based on instructions.

[0267] Step 9:

[0268] After receiving their cocktail, the user enters feedback via a terminal. The terminal then sends this feedback to the server.

[0269] Step 10:

[0270] The server collects feedback and updates the database. This information is used to improve the accuracy of future suggestions.

[0271] (Example 1)

[0272] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] Choosing the optimal beverage based on a user's mood and preferences can be complicated by the sheer number of options and the diversity of individual tastes. Furthermore, providing personalized recommendations quickly while considering past selection history and trends is challenging. Moreover, effectively utilizing user feedback and continuously improving the quality of recommendations is essential.

[0274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0275] In this invention, the server includes a medium for receiving information entered by the user and converting voice data into text data, a technology for analyzing the user's preferences using natural language processing technology, and means for generating multiple suggestions using a generative AI model. This enables the rapid and appropriate suggestion of beverages based on the user's preferences.

[0276] A "user" is an individual who uses the system to select and evaluate beverages based on their preferences.

[0277] "Interface means" refers to a device or software that receives information input by a user and converts audio data into text data as needed.

[0278] "Natural language processing technology" is a technology for analyzing text data obtained from a user, understanding its content, and identifying the user's preferences.

[0279] "Generative AI model" is an artificial intelligence technology used to create multiple proposals, which generates optimal candidates based on the user's past selection history and trends.

[0280] "Presentation means" is a mechanism for notifying the user visually or audibly of the generated proposals.

[0281] "Blending means" is a device or process that automatically generates instructions for blending beverages based on the user's selection and transmits them to the device.

[0282] "Storage means" is a means for receiving feedback from the user, storing it in a database, and using it to improve future proposals.

[0283] "Past selection history" is a record of proposals previously selected by the user, which is information used as a reference when generating future proposal candidates.

[0284] "Trend information" is data indicating the current trends in the market and society, which is used as a reference in generating proposal candidates.

[0285] "Feedback" refers to the evaluation and opinions of the user regarding the proposed beverage, which is information used to improve the system.

[0286] This invention is a system for quickly proposing beverages that match the user's preferences and automating the blending process. The system mainly consists of a user terminal, a server, and an external blending device.

[0287] The user uses a device to input their mood and preferences for the day via voice or text. The device uses speech recognition technology (e.g., a common speech recognition API) for voice input, converting the voice into text data. This allows smartphones or dedicated devices to be used as the user interface.

[0288] Next, the device sends text data to the server. The server analyzes this data using natural language processing techniques (e.g., a general natural language processing library) to understand the user's mood and preferences. Based on this analysis, it refers to past selection history and trend information, and uses a generative AI model to generate suggested beverages that are best suited to the user.

[0289] The generated suggestions are displayed to the user via a terminal, and the user selects their desired beverage from among them. This selection information is transmitted via a server to an external mixing device. The server then issues instructions to the mixing device to accurately mix the selected beverage. As a result, the beverage is automatically mixed and served to the user.

[0290] Furthermore, after the beverage is served, the user sends feedback via their device. This feedback information is stored in a database and used to improve the accuracy of future suggestions, making it possible to provide users with a more personalized experience.

[0291] For example, if a user enters "I want a refreshing cocktail" into their device, the server uses this information and, based on the results of analysis using natural language processing technology, generates suggestions such as "Mojito" or "Gin and Tonic." If the user selects Mojito, the mixing machine automatically creates a Mojito based on that information. The generative AI model that underpins this process is continuously improved based on user feedback.

[0292] An example of a prompt to input into a generative AI model is: "Generate options to suggest the best cocktail based on the user's preferences. User's mood: Refreshing."

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The user inputs their mood and preferences for the day into the device. Input can be done via voice or text. In the case of voice input, the device uses speech recognition technology to convert the voice into text data. At this stage, the input is raw voice or text data, and the output is the converted text data.

[0296] Step 2:

[0297] The terminal sends text data obtained from the user to the server. The server receives this data and analyzes it using natural language processing techniques. Through this analysis, the user's mood and preferences are identified. The input is text data from the terminal, and the output is preference information extracted through the analysis.

[0298] Step 3:

[0299] The server uses the analysis results to reference the user's past selection history and trend information, and generates multiple suggested options using a generative AI model. The generative AI model uses prompt sentences to generate suggestions that match the user's preferences. Specifically, prompt sentences such as "User's mood: Refreshing" are used. The input is preference information and historical data, and the output is a list of suggested cocktails.

[0300] Step 4:

[0301] The terminal presents the proposed candidates received from the server to the user. The user selects the desired beverage from the presented options. The input at this stage is the proposed list from the server, and the output is the beverage information selected by the user.

[0302] Step 5:

[0303] Based on the user's selection, the server sends instructions for preparing the beverage to an external dispensing device. Specifically, it transmits commands to the device based on the selected beverage recipe. The input is the user's selection information, and the output is the command to the dispensing device.

[0304] Step 6:

[0305] After the beverage is provided, the user inputs feedback to the terminal. The terminal sends this feedback to the server, and the server accumulates the feedback information in the database. The input is the feedback from the user, and the output is the updated database information. This information is utilized for the next proposal.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] When a user selects a cocktail at a bar or restaurant, there is a need for an appropriate support system to quickly select a beverage suitable for their preferences and mood and automatically prepare it. In the conventional method, there are often difficulties in selection, and there is also the problem that it is difficult to make personalized proposals.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0310] In this invention, the server includes terminal means for receiving emotional information input by the user and converting it into information data, calculation means for generating a number of options based on the user's preferences, and display means for presenting the options to the user and outputting the selected information according to the user's choice. This enables the rapid and accurate provision of cocktails based on the individual user's preferences.

[0311] "Emotional information" refers to information related to the user's mood and preferences, and is data entered as voice or text.

[0312] "Terminal means" refers to devices or systems that receive input information from users and perform data conversion as needed.

[0313] "Information data" refers to digital data obtained by converting a user's emotional information into a specific format.

[0314] "Computational means" refers to systems or algorithms that perform processing to generate a large number of options based on user preferences.

[0315] "Options" refer to multiple suggested choices presented to the user, which are generated based on specific conditions.

[0316] "Display means" refers to devices or interfaces that provide users with options and further support the user's selection.

[0317] The following describes embodiments for carrying out the invention. This invention is a system that personalizes the user's cocktail selection experience and provides it quickly and accurately. This system is composed of multiple hardware and software components.

[0318] First, the "terminal device" receives emotional information from the user in the form of voice or text. This device uses the Google Speech-to-Text API to convert voice input into text data. In addition, the user's mood and preferences, as emotional information, are processed as digital data.

[0319] Next, the "server" analyzes this information data using the Google Cloud Natural Language API. Based on the analyzed data, the "computational tool" uses OpenAI's GPT model to generate a large number of cocktail options based on the user's preferences.

[0320] The "display means" presents the generated options to the user and assists the user in selecting a specific cocktail. The selected information is then output to the user in an appropriate format.

[0321] After the user selects a cocktail, the server transmits mixing instructions to the automated cocktail machine via a "control system." The cocktail is then automatically mixed and served. Finally, user feedback is received, and the accumulated records are updated based on that feedback.

[0322] As a concrete example, a user might input "I'd like a refreshing cocktail." This prompt is parsed and processed using the Google Cloud Natural Language API and OpenAI's GPT model, and suggested cocktails include options such as "Mojito" and "Margarita." The cocktail selected by the user is automatically brewed and served quickly.

[0323] An example of a prompt to the generating AI model is: "Based on the user's preference information, please suggest the cocktail that best suits their current mood. Please also take into account past selection history and trends." In this way, the present invention realizes a cocktail service that is responsive to the individual preferences of the user.

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The device receives either user voice or text input. In the case of voice input, the Google Speech-to-Text API is used to convert the voice data into text data. In this case, the input is the user's speech, and the output is the transcribed information.

[0327] Step 2:

[0328] The server receives text data sent from the terminal and performs analysis using the Google Cloud Natural Language API. The input is text data, and the system processes it to extract user preferences and moods, outputting the extracted preference information as the analysis result.

[0329] Step 3:

[0330] Based on the analysis results, the server uses OpenAI's GPT model to generate cocktail options that suit the user's preferences. The input at this stage is the analyzed preference information, and past selection history and trend information are also taken into consideration, resulting in the output of multiple cocktail suggestions.

[0331] Step 4:

[0332] The server sends a list of cocktail options to the terminal, and the "display device" presents them to the user. The input is cocktail suggestions from the server, and the output is the options presented to the user.

[0333] Step 5:

[0334] The user selects one of their preferred cocktails from the presented options. In this step, the user's selection is the input, and the information of this selected cocktail is the output.

[0335] Step 6:

[0336] The server controls the automated cocktail machine to send mixing instructions based on the user's selection. The input here is information about the selected cocktail, and the output is an automated mixing instruction.

[0337] Step 7:

[0338] The device receives user feedback, sends it to the server, and updates the record. In this process, the input is user feedback information, and the output is the updated record data.

[0339] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0340] This invention is an automated cocktail suggestion system that incorporates an emotion engine to recognize the user's emotions, thereby enabling personalized suggestions tailored to the user's emotional state. Specific embodiments of the system are described below.

[0341] When a user inputs their current mood or preferences via voice or text through the device, the device receives it and converts it into text using speech recognition technology. At this point, the emotion engine activates and detects the user's emotional state from their voice or text. For example, it analyzes emotions such as "happy" or "tired" from the tone and rhythm of the voice and the words used in the text.

[0342] The server analyzes the text data received from the user, along with the results of the emotion engine's analysis, to generate cocktail suggestions based on the user's preferences and emotions. An algorithm is executed that queries the user's past selection history and trend information to form multiple cocktail candidates that match their emotions.

[0343] The generated suggestions are presented to the user from the terminal, and the user can choose their desired cocktail from the presented options. The selected information is sent to the server, and instructions are sent to the cocktail machine via the mixing mechanism. As a result, the selected cocktail is automatically mixed and served.

[0344] Meanwhile, after the cocktail is served, users can input feedback via a terminal. This feedback is re-analyzed by the emotion engine, and new emotion data is stored in the database. This data is used to improve the accuracy of future suggestions and is used to adjust the selection criteria for suggested cocktails in real time.

[0345] For example, if a user enters "I want something to cheer me up today," the emotion engine detects a positive emotion from the user's input. Based on this, the server compares it with past selection history and popular trends to generate suggestions such as "a cocktail based on an energy drink." In this way, the present invention aims to provide an experience closely related to the user's emotions.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The user inputs their mood or preferences via voice or text through the device. The device receives the voice data and, in the case of voice input, converts it into text data using speech recognition technology.

[0349] Step 2:

[0350] The device sends the received text data to the server. The emotion engine also analyzes the voice and text data to extract the user's emotional state.

[0351] Step 3:

[0352] The server analyzes the received text data to understand the user's preferences, and, taking into account the emotional information obtained by the emotion engine, generates multiple cocktail suggestions.

[0353] Step 4:

[0354] The server references the user's past selection history and current trend information to create a list of cocktail candidates best suited to their emotional state and preferences.

[0355] Step 5:

[0356] The server sends a list of generated cocktail candidates to the terminal, which then presents the list to the user visually or audibly.

[0357] Step 6:

[0358] The user selects their desired cocktail from the presented options. The terminal sends the selection information to the server.

[0359] Step 7:

[0360] The server generates instructions for the cocktail machine based on the user's selection and transmits those instructions via the mixing device.

[0361] Step 8:

[0362] The cocktail machine automatically mixes cocktails according to the server's instructions and serves them to the user.

[0363] Step 9:

[0364] After receiving their cocktail, users input feedback such as their satisfaction level and comments via a terminal. The terminal then sends this feedback to the server.

[0365] Step 10:

[0366] The server re-analyzes the received feedback using an emotion engine and updates the database. This updated data is used to improve the accuracy of future suggestions and adjust the suggestion criteria in real time.

[0367] (Example 2)

[0368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0369] In today's world, systems that provide appropriate suggestions based on user emotions are rare, and providing highly accurate suggestions tailored to individual emotional states is difficult. Furthermore, conventional systems are limited to generating suggestions based on user selection history and trends, lacking the flexibility to consider user emotional states. This leads to a decline in the quality of the user experience and makes it difficult to increase satisfaction.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes data processing means for analyzing user emotional information and generating suggestions based thereon, generation means for optimizing by combining the user's past selection history and trend information, and data management means for analyzing feedback and adjusting the criteria for suggestions in real time. This enables optimal suggestions that respond to the diverse emotions of the user.

[0372] "Data processing means" refers to a device or process that receives voice or text input from a user and converts that data into an analyzable format as needed.

[0373] "Analysis means" refers to technologies and systems for identifying and detecting a user's emotional state from received text data.

[0374] "Generation means" refers to algorithms and devices that create multiple suggested options based on the user's emotional state, selection history, and trend information, and then provide the optimal suggestion.

[0375] A "presentation means" is a device or interface that provides the generated proposal to the user visually or audibly and accepts the user's selection.

[0376] "Blending instruction means" refers to a device or process that generates instructions for automatically blending a beverage based on a user's selection and transmits these instructions to a machine.

[0377] "Data management means" refers to a device that includes functions and analytical techniques for receiving user feedback, updating the database, and improving the accuracy of future suggestions.

[0378] This invention is an automated suggestion system that provides personalized cocktail suggestions based on the user's emotional state. Specific embodiments of the system are described below.

[0379] Terminal role:

[0380] The user inputs their emotional state and preferences via voice or text through the device. The device converts this input into text data using speech recognition software (e.g., a speech-to-text API). The converted data is then analyzed by an emotion engine to detect the user's emotional state.

[0381] Server role:

[0382] Based on the analysis results sent from the emotion engine, the server generates multiple cocktail suggestion candidates, taking into account the user's emotional state, past selection history, and trend information. Machine learning algorithms (e.g., AI model frameworks) are used for suggestion generation, and optimized suggestions are provided to the user.

[0383] Specific example:

[0384] For example, if a user enters "I want to refresh myself today," the emotion engine detects the emotion "refreshment" from this input. The server compares the analysis results with past selection history and current beverage trend information to generate suggestions such as "mint-based cocktails."

[0385] Example of a prompt:

[0386] "Please write a program that recognizes the user's emotions and generates cocktail suggestions based on those emotions."

[0387] Thus, the present invention aims to provide a more refined user experience through suggestions that respond to the user's emotions.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] Users input their emotional state and desires via voice or text through a device. This input is converted into text data using speech recognition software. The input mainly consists of the user's mood and image of the beverage they want, and the output is the converted text data.

[0391] Step 2:

[0392] The device sends text data to an emotion engine, which uses natural language processing technology to analyze the user's emotional state. Specifically, it analyzes words and phrases in the text and extracts emotional keywords such as "refreshed" and "relaxed." The input is the converted text data, and the output is the user's identified emotional state.

[0393] Step 3:

[0394] The server receives the analysis results from the emotion engine and generates cocktail suggestion candidates, taking into account the user's past selection history and trend information. The algorithm used is an AI model framework that executes database queries to obtain the necessary data. The inputs are emotion state, selection history, and trend information, and the output is a list of suggested cocktail candidates.

[0395] Step 4:

[0396] The terminal presents the user with cocktail suggestions obtained from the server. Specifically, it displays multiple suggested cocktails on the screen and prompts the user to make a selection visually or audibly. The input is the cocktail options sent from the server, and the output is the cocktail information selected by the user.

[0397] Step 5:

[0398] The server receives the user's selection and sends mixing instructions to the cocktail machine. Specifically, it generates data that specifies the quantities and order of each ingredient based on the selected cocktail's recipe. The input is the cocktail selected by the user, and the output is the specific mixing instructions sent to the cocktail machine.

[0399] Step 6:

[0400] After enjoying their cocktail, the user enters feedback into the device. This feedback is then analyzed again by the emotion engine to extract the user's impressions and areas for improvement. The input is the user's feedback, and the output is the analyzed impression data.

[0401] Step 7:

[0402] The server stores the analyzed feedback in a database and uses it to improve the accuracy of future suggestions. Specifically, it compares and analyzes past data and dynamically adjusts the algorithm settings. The input is the analyzed feedback data, and the output is updated database information.

[0403] (Application Example 2)

[0404] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0405] It is difficult to suggest the most suitable beverage based on the user's emotional state, and there is a lack of means to improve the accuracy of service provision based on user preferences. Furthermore, there is a need to utilize user feedback in real time to improve the accuracy of suggested options.

[0406] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0407] In this invention, the server includes communication means for receiving user input information and converting it into voice or text data, emotion analysis means for recognizing the emotional state from the analyzed data, and processing means for generating multiple suggestion candidates based on the analysis results and preferences. This makes it possible to provide personalized suggestions that are in line with the user's emotions and improve the quality of the service.

[0408] A "user" refers to an individual or group that uses the system, and is the entity that receives suggestions based on their emotional state and preferences.

[0409] "Communication means" refers to the means of receiving voice or text information from a user and incorporating it into the system as data.

[0410] "Emotion analysis means" refers to a method of identifying emotions using user voice and text data and performing analysis in order to make appropriate service suggestions.

[0411] A "processing means" is a means that has the function of generating multiple suggested options based on the results of sentiment analysis and the user's preferences.

[0412] A "suggestion" refers to a set of multiple service or product options created based on the user's emotional state and preferences.

[0413] A "storage method" refers to a means of saving user feedback and updating information resources.

[0414] The system for realizing this application includes a terminal that receives user input information and a server that performs analysis and makes suggestions. First, the user inputs their current mood and preferences via voice or text through a smartphone app. The terminal receives this information and, if voice input is received, converts it into text data using speech recognition software. This process uses general speech recognition technology.

[0415] Next, the server receives this text data and uses its sentiment analysis engine to recognize the user's emotional state. The sentiment analysis engine analyzes the vocabulary and context used in the text to determine emotions such as "positive," "negative," or "relaxed."

[0416] Next, the server uses the analysis results to run an algorithm that takes into account the user's past selection history and current trend information, generating multiple suggested options. By using well-known algorithms and machine learning models, it is possible to provide suggestions that best match the user's preferences.

[0417] The generated suggested cocktails are sent to the terminal and presented to the user. When the user selects their desired cocktail, that information is returned to the server, and automated instructions for mixing the cocktail are sent to the mixing device. As a result, the selected beverage is automatically mixed and served to the user.

[0418] After a beverage is served, users can provide feedback through the app. This feedback is re-analyzed on the server side, compared with the sentiment analysis results, and the information resources are updated. This makes it possible to continuously improve the accuracy of suggestions for future orders. For example, if a user inputs "I want something refreshing," the sentiment engine will detect "relaxation," and the server will suggest something like a "herbal tea-based cocktail" based on past data and trends.

[0419] An example of a generated AI model prompt is: "Identify the user's emotion from the text 'Something to cheer me up today' and suggest a suitable cocktail."

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The terminal receives input information from the user. This input is provided via a smartphone application, either as voice or text. In the case of voice input, the terminal uses voice recognition software to convert the voice into text data. This text data then becomes the input for the next processing step.

[0423] Step 2:

[0424] The server analyzes the text data received from the terminal. Using an emotion analysis engine, it evaluates the vocabulary and context within the text to determine the user's emotional state. In this process, emotional information such as positive, negative, and relaxed is output.

[0425] Step 3:

[0426] Based on the analyzed sentiment information, the server generates suggested options by referencing the user's past selection history and the latest trend information. By applying the algorithm, multiple beverage options that best match the user's current sentiment and preferences are generated and output to the next step.

[0427] Step 4:

[0428] The terminal presents the user with suggested cocktails from the server. Once the user selects their desired cocktail from the presented options, this selection information is sent back from the terminal to the server. This selection information then becomes the input for the next process.

[0429] Step 5:

[0430] The server receives the user's selection information and generates automatic mixing instructions. These instructions are sent to the cooking device, which automatically creates the selected beverage. This process ensures that the user receives the perfect cocktail.

[0431] Step 6:

[0432] The server receives feedback information from the user. The feedback is then analyzed again for sentiment, comparing the user's emotions with the accuracy of the suggestions, and updating the system's information resources. This improves the accuracy of future suggestions. Through this feedback process, the system continuously learns and improves.

[0433] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0434] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0439] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0441] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0443] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0444] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0445] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0446] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0448] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0449] This invention relates to a system that automates the process of assisting a user in selecting a cocktail, and the system comprises an interface means, an analysis means, a presentation means, a mixing means, and a storage means. The operation of this system can be described as follows.

[0450] When a user inputs information such as their mood or preferences via voice or text through the device, that input information is captured by the interface. In the case of voice input, the device uses speech recognition technology to convert it into text data.

[0451] The server analyzes text data acquired through the interface using natural language processing technology. The analysis accurately grasps the user's mood and preferences and compares them with past selection history and trend information. Based on this information, the server generates multiple cocktail suggestions that are best suited to the user.

[0452] The terminal displays cocktail suggestions sent from the server to the user, allowing the user to select from the suggested options. Once the user selects a cocktail, that information is sent to the mixing device, and the server sends precise instructions to the cocktail machine, which automatically mixes the selected cocktail.

[0453] Furthermore, feedback provided by users after the blending process is transmitted from the terminal to the server via a storage mechanism and stored in a database. This stored data is used to improve future suggestions and enable more personalized suggestions for users.

[0454] For example, if a user enters "I want a refreshing cocktail," the server analyzes this information and generates options such as "Mojito" or "Gin and Tonic" based on past selection history and trend information. If the user selects Mojito, that selection is quickly realized through the mixing process and served.

[0455] In this way, the present invention makes it possible to quickly provide a personalized cocktail experience for each user.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] Users input their mood, preferences, and allergy information via voice or text through their device. The device uses speech recognition technology to convert the voice input into text data.

[0459] Step 2:

[0460] The terminal sends the converted text data to the server. The server receives this data and begins natural language processing.

[0461] Step 3:

[0462] The server analyzes text data to extract user preferences and keywords. It also checks the user's past selection history by referring to past databases and queries for trend information.

[0463] Step 4:

[0464] Based on the extracted information, the server runs an algorithm to generate multiple cocktail suggestions and creates a list of candidates.

[0465] Step 5:

[0466] The server sends a list of potential cocktails to the terminal. The terminal then presents this list to the user, allowing them to make a selection.

[0467] Step 6:

[0468] The user selects their desired cocktail from the presented cocktail options. The terminal then sends this selection to the server.

[0469] Step 7:

[0470] The server generates instructions for mixing the selected cocktail and sends those instructions to the cocktail machine.

[0471] Step 8:

[0472] The cocktail machine automatically mixes and serves cocktails based on instructions.

[0473] Step 9:

[0474] After receiving their cocktail, the user enters feedback via a terminal. The terminal then sends this feedback to the server.

[0475] Step 10:

[0476] The server collects feedback and updates the database. This information is used to improve the accuracy of future suggestions.

[0477] (Example 1)

[0478] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0479] Choosing the optimal beverage based on a user's mood and preferences can be complicated by the sheer number of options and the diversity of individual tastes. Furthermore, providing personalized recommendations quickly while considering past selection history and trends is challenging. Moreover, effectively utilizing user feedback and continuously improving the quality of recommendations is essential.

[0480] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0481] In this invention, the server includes a medium for receiving information entered by the user and converting voice data into text data, a technology for analyzing the user's preferences using natural language processing technology, and means for generating multiple suggestions using a generative AI model. This enables the rapid and appropriate suggestion of beverages based on the user's preferences.

[0482] A "user" is an individual who uses the system to select and evaluate beverages based on their preferences.

[0483] "Interface means" refers to a device or software that receives information input by a user and converts audio data into text data as needed.

[0484] "Natural language processing technology" is a technique that analyzes text data obtained from users, understands its content, and identifies the user's preferences.

[0485] A "generative AI model" is an artificial intelligence technology used to create multiple suggestions, generating the optimal candidates based on the user's past selection history and trends.

[0486] A "presentation means" is a mechanism for visually or audibly informing the user of the generated proposal.

[0487] "Blending means" refers to a device or process that automatically generates instructions for blending a beverage based on the user's selection and transmits them to a device.

[0488] A "storage method" refers to a means of receiving user feedback, storing it in a database, and using it to improve future proposals.

[0489] "Past selection history" refers to a record of suggestions previously selected by the user, and is used as reference when generating future suggestion candidates.

[0490] "Trend information" refers to data that indicates current trends in the market and society, and is used as a reference when generating potential proposals.

[0491] "Feedback" refers to user evaluations and opinions on suggested beverages, and is information that can be used to improve the system.

[0492] This invention is a system for quickly suggesting beverages tailored to user preferences and automating the blending process. The system mainly consists of a user terminal, a server, and external blending equipment.

[0493] The user uses a device to input their mood and preferences for the day via voice or text. The device uses speech recognition technology (e.g., a common speech recognition API) for voice input, converting the voice into text data. This allows smartphones or dedicated devices to be used as the user interface.

[0494] Next, the device sends text data to the server. The server analyzes this data using natural language processing techniques (e.g., a general natural language processing library) to understand the user's mood and preferences. Based on this analysis, it refers to past selection history and trend information, and uses a generative AI model to generate suggested beverages that are best suited to the user.

[0495] The generated suggestions are displayed to the user via a terminal, and the user selects their desired beverage from among them. This selection information is transmitted via a server to an external mixing device. The server then issues instructions to the mixing device to accurately mix the selected beverage. As a result, the beverage is automatically mixed and served to the user.

[0496] Furthermore, after the beverage is served, the user sends feedback via their device. This feedback information is stored in a database and used to improve the accuracy of future suggestions, making it possible to provide users with a more personalized experience.

[0497] For example, if a user enters "I want a refreshing cocktail" into their device, the server uses this information and, based on the results of analysis using natural language processing technology, generates suggestions such as "Mojito" or "Gin and Tonic." If the user selects Mojito, the mixing machine automatically creates a Mojito based on that information. The generative AI model that underpins this process is continuously improved based on user feedback.

[0498] An example of a prompt to input into a generative AI model is: "Generate options to suggest the best cocktail based on the user's preferences. User's mood: Refreshing."

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] The user inputs their mood and preferences for the day into the device. Input can be done via voice or text. In the case of voice input, the device uses speech recognition technology to convert the voice into text data. At this stage, the input is raw voice or text data, and the output is the converted text data.

[0502] Step 2:

[0503] The terminal sends text data obtained from the user to the server. The server receives this data and analyzes it using natural language processing techniques. Through this analysis, the user's mood and preferences are identified. The input is text data from the terminal, and the output is preference information extracted through the analysis.

[0504] Step 3:

[0505] The server uses the analysis results to reference the user's past selection history and trend information, and generates multiple suggested options using a generative AI model. The generative AI model uses prompt sentences to generate suggestions that match the user's preferences. Specifically, prompt sentences such as "User's mood: Refreshing" are used. The input is preference information and historical data, and the output is a list of suggested cocktails.

[0506] Step 4:

[0507] The terminal presents the user with a list of suggested beverages received from the server. The user selects their desired beverage from the presented options. At this stage, the input is the list of suggestions from the server, and the output is the beverage information selected by the user.

[0508] Step 5:

[0509] The server transmits instructions for preparing beverages to an external mixing device based on the user's selection. Specifically, it transmits commands to the device based on the selected beverage recipe. The input is the user's selection information, and the output is the commands to the mixing device.

[0510] Step 6:

[0511] After being served their beverage, the user enters feedback into a terminal. The terminal sends this feedback to a server, which stores the feedback information in a database. The input is the user's feedback, and the output is the updated database information. This information is used to inform future suggestions.

[0512] (Application Example 1)

[0513] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] When users choose a cocktail at a bar or restaurant, there is a need for an appropriate support system that can quickly select a beverage that suits their preferences and mood, and automatically mix it for them. Traditional methods often present challenges, such as making difficult choices and providing personalized suggestions.

[0515] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0516] In this invention, the server includes terminal means for receiving emotional information input by the user and converting it into information data, calculation means for generating a number of options based on the user's preferences, and display means for presenting the options to the user and outputting the selected information according to the user's choice. This enables the rapid and accurate provision of cocktails based on the individual user's preferences.

[0517] "Emotional information" refers to information related to the user's mood and preferences, and is data entered as voice or text.

[0518] "Terminal means" refers to devices or systems that receive input information from users and perform data conversion as needed.

[0519] "Information data" refers to digital data obtained by converting a user's emotional information into a specific format.

[0520] "Computational means" refers to systems or algorithms that perform processing to generate a large number of options based on user preferences.

[0521] "Options" refer to multiple suggested choices presented to the user, which are generated based on specific conditions.

[0522] "Display means" refers to devices or interfaces that provide users with options and further support the user's selection.

[0523] The following describes embodiments for carrying out the invention. This invention is a system that personalizes the user's cocktail selection experience and provides it quickly and accurately. This system is composed of multiple hardware and software components.

[0524] First, the "terminal device" receives emotional information from the user in the form of voice or text. This device uses the Google Speech-to-Text API to convert voice input into text data. In addition, the user's mood and preferences, as emotional information, are processed as digital data.

[0525] Next, the "server" analyzes this information data using the Google Cloud Natural Language API. Based on the analyzed data, the "computational tool" uses OpenAI's GPT model to generate a large number of cocktail options based on the user's preferences.

[0526] The "display means" presents the generated options to the user and assists the user in selecting a specific cocktail. The selected information is then output to the user in an appropriate format.

[0527] After the user selects a cocktail, the server transmits mixing instructions to the automated cocktail machine via a "control system." The cocktail is then automatically mixed and served. Finally, user feedback is received, and the accumulated records are updated based on that feedback.

[0528] As a concrete example, a user might input "I'd like a refreshing cocktail." This prompt is parsed and processed using the Google Cloud Natural Language API and OpenAI's GPT model, and suggested cocktails include options such as "Mojito" and "Margarita." The cocktail selected by the user is automatically brewed and served quickly.

[0529] An example of a prompt to the generating AI model is: "Based on the user's preference information, please suggest the cocktail that best suits their current mood. Please also take into account past selection history and trends." In this way, the present invention realizes a cocktail service that is responsive to the individual preferences of the user.

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The device receives either user voice or text input. In the case of voice input, the Google Speech-to-Text API is used to convert the voice data into text data. In this case, the input is the user's speech, and the output is the transcribed information.

[0533] Step 2:

[0534] The server receives text data sent from the terminal and performs analysis using the Google Cloud Natural Language API. The input is text data, and the system processes it to extract user preferences and moods, outputting the extracted preference information as the analysis result.

[0535] Step 3:

[0536] Based on the analysis results, the server uses OpenAI's GPT model to generate cocktail options that suit the user's preferences. The input at this stage is the analyzed preference information, and past selection history and trend information are also taken into consideration, resulting in the output of multiple cocktail suggestions.

[0537] Step 4:

[0538] The server sends a list of cocktail options to the terminal, and the "display device" presents them to the user. The input is cocktail suggestions from the server, and the output is the options presented to the user.

[0539] Step 5:

[0540] The user selects one of their preferred cocktails from the presented options. In this step, the user's selection is the input, and the information of this selected cocktail is the output.

[0541] Step 6:

[0542] The server controls the automated cocktail machine to send mixing instructions based on the user's selection. The input here is information about the selected cocktail, and the output is an automated mixing instruction.

[0543] Step 7:

[0544] The device receives user feedback, sends it to the server, and updates the record. In this process, the input is user feedback information, and the output is the updated record data.

[0545] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0546] This invention is an automated cocktail suggestion system that incorporates an emotion engine to recognize the user's emotions, thereby enabling personalized suggestions tailored to the user's emotional state. Specific embodiments of the system are described below.

[0547] When a user inputs their current mood or preferences via voice or text through the device, the device receives it and converts it into text using speech recognition technology. At this point, the emotion engine activates and detects the user's emotional state from their voice or text. For example, it analyzes emotions such as "happy" or "tired" from the tone and rhythm of the voice and the words used in the text.

[0548] The server analyzes the text data received from the user, along with the results of the emotion engine's analysis, to generate cocktail suggestions based on the user's preferences and emotions. An algorithm is executed that queries the user's past selection history and trend information to form multiple cocktail candidates that match their emotions.

[0549] The generated suggestions are presented to the user from the terminal, and the user can choose their desired cocktail from the presented options. The selected information is sent to the server, and instructions are sent to the cocktail machine via the mixing mechanism. As a result, the selected cocktail is automatically mixed and served.

[0550] Meanwhile, after the cocktail is served, users can input feedback via a terminal. This feedback is re-analyzed by the emotion engine, and new emotion data is stored in the database. This data is used to improve the accuracy of future suggestions and is used to adjust the selection criteria for suggested cocktails in real time.

[0551] For example, if a user enters "I want something to cheer me up today," the emotion engine detects a positive emotion from the user's input. Based on this, the server compares it with past selection history and popular trends to generate suggestions such as "a cocktail based on an energy drink." In this way, the present invention aims to provide an experience closely related to the user's emotions.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The user inputs their mood or preferences via voice or text through the device. The device receives the voice data and, in the case of voice input, converts it into text data using speech recognition technology.

[0555] Step 2:

[0556] The device sends the received text data to the server. The emotion engine also analyzes the voice and text data to extract the user's emotional state.

[0557] Step 3:

[0558] The server analyzes the received text data to understand the user's preferences, and, taking into account the emotional information obtained by the emotion engine, generates multiple cocktail suggestions.

[0559] Step 4:

[0560] The server references the user's past selection history and current trend information to create a list of cocktail candidates best suited to their emotional state and preferences.

[0561] Step 5:

[0562] The server sends a list of generated cocktail candidates to the terminal, which then presents the list to the user visually or audibly.

[0563] Step 6:

[0564] The user selects their desired cocktail from the presented options. The terminal sends the selection information to the server.

[0565] Step 7:

[0566] The server generates instructions for the cocktail machine based on the user's selection and transmits those instructions via the mixing device.

[0567] Step 8:

[0568] The cocktail machine automatically mixes cocktails according to the server's instructions and serves them to the user.

[0569] Step 9:

[0570] After receiving their cocktail, users input feedback such as their satisfaction level and comments via a terminal. The terminal then sends this feedback to the server.

[0571] Step 10:

[0572] The server re-analyzes the received feedback using an emotion engine and updates the database. This updated data is used to improve the accuracy of future suggestions and adjust the suggestion criteria in real time.

[0573] (Example 2)

[0574] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0575] In today's world, systems that provide appropriate suggestions based on user emotions are rare, and providing highly accurate suggestions tailored to individual emotional states is difficult. Furthermore, conventional systems are limited to generating suggestions based on user selection history and trends, lacking the flexibility to consider user emotional states. This leads to a decline in the quality of the user experience and makes it difficult to increase satisfaction.

[0576] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0577] In this invention, the server includes data processing means for analyzing user emotional information and generating suggestions based thereon, generation means for optimizing by combining the user's past selection history and trend information, and data management means for analyzing feedback and adjusting the criteria for suggestions in real time. This enables optimal suggestions that respond to the diverse emotions of the user.

[0578] "Data processing means" refers to a device or process that receives voice or text input from a user and converts that data into an analyzable format as needed.

[0579] "Analysis means" refers to technologies and systems for identifying and detecting a user's emotional state from received text data.

[0580] "Generation means" refers to algorithms and devices that create multiple suggested options based on the user's emotional state, selection history, and trend information, and then provide the optimal suggestion.

[0581] A "presentation means" is a device or interface that provides the generated proposal to the user visually or audibly and accepts the user's selection.

[0582] "Blending instruction means" refers to a device or process that generates instructions for automatically blending a beverage based on a user's selection and transmits these instructions to a machine.

[0583] "Data management means" refers to a device that includes functions and analytical techniques for receiving user feedback, updating the database, and improving the accuracy of future suggestions.

[0584] This invention is an automated suggestion system that provides personalized cocktail suggestions based on the user's emotional state. Specific embodiments of the system are described below.

[0585] Terminal role:

[0586] The user inputs their emotional state and preferences via voice or text through the device. The device converts this input into text data using speech recognition software (e.g., a speech-to-text API). The converted data is then analyzed by an emotion engine to detect the user's emotional state.

[0587] Server role:

[0588] Based on the analysis results sent from the emotion engine, the server generates multiple cocktail suggestion candidates, taking into account the user's emotional state, past selection history, and trend information. Machine learning algorithms (e.g., AI model frameworks) are used for suggestion generation, and optimized suggestions are provided to the user.

[0589] Specific example:

[0590] For example, if a user enters "I want to refresh myself today," the emotion engine detects the emotion "refreshment" from this input. The server compares the analysis results with past selection history and current beverage trend information to generate suggestions such as "mint-based cocktails."

[0591] Example of a prompt:

[0592] "Please write a program that recognizes the user's emotions and generates cocktail suggestions based on those emotions."

[0593] Thus, the present invention aims to provide a more refined user experience through suggestions that respond to the user's emotions.

[0594] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0595] Step 1:

[0596] Users input their emotional state and desires via voice or text through a device. This input is converted into text data using speech recognition software. The input mainly consists of the user's mood and image of the beverage they want, and the output is the converted text data.

[0597] Step 2:

[0598] The device sends text data to an emotion engine, which uses natural language processing technology to analyze the user's emotional state. Specifically, it analyzes words and phrases in the text and extracts emotional keywords such as "refreshed" and "relaxed." The input is the converted text data, and the output is the user's identified emotional state.

[0599] Step 3:

[0600] The server receives the analysis results from the emotion engine and generates cocktail suggestion candidates, taking into account the user's past selection history and trend information. The algorithm used is an AI model framework that executes database queries to obtain the necessary data. The inputs are emotion state, selection history, and trend information, and the output is a list of suggested cocktail candidates.

[0601] Step 4:

[0602] The terminal presents the user with cocktail suggestions obtained from the server. Specifically, it displays multiple suggested cocktails on the screen and prompts the user to make a selection visually or audibly. The input is the cocktail options sent from the server, and the output is the cocktail information selected by the user.

[0603] Step 5:

[0604] The server receives the user's selection and sends mixing instructions to the cocktail machine. Specifically, it generates data that specifies the quantities and order of each ingredient based on the selected cocktail's recipe. The input is the cocktail selected by the user, and the output is the specific mixing instructions sent to the cocktail machine.

[0605] Step 6:

[0606] After enjoying their cocktail, the user enters feedback into the device. This feedback is then analyzed again by the emotion engine to extract the user's impressions and areas for improvement. The input is the user's feedback, and the output is the analyzed impression data.

[0607] Step 7:

[0608] The server stores the analyzed feedback in a database and uses it to improve the accuracy of future suggestions. Specifically, it compares and analyzes past data and dynamically adjusts the algorithm settings. The input is the analyzed feedback data, and the output is updated database information.

[0609] (Application Example 2)

[0610] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0611] It is difficult to suggest the most suitable beverage based on the user's emotional state, and there is a lack of means to improve the accuracy of service provision based on user preferences. Furthermore, there is a need to utilize user feedback in real time to improve the accuracy of suggested options.

[0612] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0613] In this invention, the server includes communication means for receiving user input information and converting it into voice or text data, emotion analysis means for recognizing the emotional state from the analyzed data, and processing means for generating multiple suggestion candidates based on the analysis results and preferences. This makes it possible to provide personalized suggestions that are in line with the user's emotions and improve the quality of the service.

[0614] A "user" refers to an individual or group that uses the system, and is the entity that receives suggestions based on their emotional state and preferences.

[0615] "Communication means" refers to the means of receiving voice or text information from a user and incorporating it into the system as data.

[0616] "Emotion analysis means" refers to a method of identifying emotions using user voice and text data and performing analysis in order to make appropriate service suggestions.

[0617] A "processing means" is a means that has the function of generating multiple suggested options based on the results of sentiment analysis and the user's preferences.

[0618] A "suggestion" refers to a set of multiple service or product options created based on the user's emotional state and preferences.

[0619] A "storage method" refers to a means of saving user feedback and updating information resources.

[0620] The system for realizing this application includes a terminal that receives user input information and a server that performs analysis and makes suggestions. First, the user inputs their current mood and preferences via voice or text through a smartphone app. The terminal receives this information and, if voice input is received, converts it into text data using speech recognition software. This process uses general speech recognition technology.

[0621] Next, the server receives this text data and uses its sentiment analysis engine to recognize the user's emotional state. The sentiment analysis engine analyzes the vocabulary and context used in the text to determine emotions such as "positive," "negative," or "relaxed."

[0622] Next, the server uses the analysis results to run an algorithm that takes into account the user's past selection history and current trend information, generating multiple suggested options. By using well-known algorithms and machine learning models, it is possible to provide suggestions that best match the user's preferences.

[0623] The generated suggested cocktails are sent to the terminal and presented to the user. When the user selects their desired cocktail, that information is returned to the server, and automated instructions for mixing the cocktail are sent to the mixing device. As a result, the selected beverage is automatically mixed and served to the user.

[0624] After a beverage is served, users can provide feedback through the app. This feedback is re-analyzed on the server side, compared with the sentiment analysis results, and the information resources are updated. This makes it possible to continuously improve the accuracy of suggestions for future orders. For example, if a user inputs "I want something refreshing," the sentiment engine will detect "relaxation," and the server will suggest something like a "herbal tea-based cocktail" based on past data and trends.

[0625] An example of a generated AI model prompt is: "Identify the user's emotion from the text 'Something to cheer me up today' and suggest a suitable cocktail."

[0626] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0627] Step 1:

[0628] The terminal receives input information from the user. This input is provided via a smartphone application, either as voice or text. In the case of voice input, the terminal uses voice recognition software to convert the voice into text data. This text data then becomes the input for the next processing step.

[0629] Step 2:

[0630] The server analyzes the text data received from the terminal. Using an emotion analysis engine, it evaluates the vocabulary and context within the text to determine the user's emotional state. In this process, emotional information such as positive, negative, and relaxed is output.

[0631] Step 3:

[0632] Based on the analyzed sentiment information, the server generates suggested options by referencing the user's past selection history and the latest trend information. By applying the algorithm, multiple beverage options that best match the user's current sentiment and preferences are generated and output to the next step.

[0633] Step 4:

[0634] The terminal presents the user with suggested cocktails from the server. Once the user selects their desired cocktail from the presented options, this selection information is sent back from the terminal to the server. This selection information then becomes the input for the next process.

[0635] Step 5:

[0636] The server receives the user's selection information and generates automatic mixing instructions. These instructions are sent to the cooking device, which automatically creates the selected beverage. This process ensures that the user receives the perfect cocktail.

[0637] Step 6:

[0638] The server receives feedback information from the user. The feedback is then analyzed again for sentiment, comparing the user's emotions with the accuracy of the suggestions, and updating the system's information resources. This improves the accuracy of future suggestions. Through this feedback process, the system continuously learns and improves.

[0639] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0640] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0641] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0642] [Fourth Embodiment]

[0643] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0644] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0645] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0646] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0647] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0648] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0649] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0650] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0651] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0652] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0653] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0654] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0655] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] This invention relates to a system that automates the process of assisting a user in selecting a cocktail, and the system comprises an interface means, an analysis means, a presentation means, a mixing means, and a storage means. The operation of this system can be described as follows.

[0657] When a user inputs information such as their mood or preferences via voice or text through the device, that input information is captured by the interface. In the case of voice input, the device uses speech recognition technology to convert it into text data.

[0658] The server analyzes text data acquired through the interface using natural language processing technology. The analysis accurately grasps the user's mood and preferences and compares them with past selection history and trend information. Based on this information, the server generates multiple cocktail suggestions that are best suited to the user.

[0659] The terminal displays cocktail suggestions sent from the server to the user, allowing the user to select from the suggested options. Once the user selects a cocktail, that information is sent to the mixing device, and the server sends precise instructions to the cocktail machine, which automatically mixes the selected cocktail.

[0660] Furthermore, feedback provided by users after the blending process is transmitted from the terminal to the server via a storage mechanism and stored in a database. This stored data is used to improve future suggestions and enable more personalized suggestions for users.

[0661] For example, if a user enters "I want a refreshing cocktail," the server analyzes this information and generates options such as "Mojito" or "Gin and Tonic" based on past selection history and trend information. If the user selects Mojito, that selection is quickly realized through the mixing process and served.

[0662] In this way, the present invention makes it possible to quickly provide a personalized cocktail experience for each user.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] Users input their mood, preferences, and allergy information via voice or text through their device. The device uses speech recognition technology to convert the voice input into text data.

[0666] Step 2:

[0667] The terminal sends the converted text data to the server. The server receives this data and begins natural language processing.

[0668] Step 3:

[0669] The server analyzes text data to extract user preferences and keywords. It also checks the user's past selection history by referring to past databases and queries for trend information.

[0670] Step 4:

[0671] Based on the extracted information, the server runs an algorithm to generate multiple cocktail suggestions and creates a list of candidates.

[0672] Step 5:

[0673] The server sends a list of potential cocktails to the terminal. The terminal then presents this list to the user, allowing them to make a selection.

[0674] Step 6:

[0675] The user selects their desired cocktail from the presented cocktail options. The terminal then sends this selection to the server.

[0676] Step 7:

[0677] The server generates instructions for mixing the selected cocktail and sends those instructions to the cocktail machine.

[0678] Step 8:

[0679] The cocktail machine automatically mixes and serves cocktails based on instructions.

[0680] Step 9:

[0681] After receiving their cocktail, the user enters feedback via a terminal. The terminal then sends this feedback to the server.

[0682] Step 10:

[0683] The server collects feedback and updates the database. This information is used to improve the accuracy of future suggestions.

[0684] (Example 1)

[0685] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] Choosing the optimal beverage based on a user's mood and preferences can be complicated by the sheer number of options and the diversity of individual tastes. Furthermore, providing personalized recommendations quickly while considering past selection history and trends is challenging. Moreover, effectively utilizing user feedback and continuously improving the quality of recommendations is essential.

[0687] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0688] In this invention, the server includes a medium for receiving information entered by the user and converting voice data into text data, a technology for analyzing the user's preferences using natural language processing technology, and means for generating multiple suggestions using a generative AI model. This enables the rapid and appropriate suggestion of beverages based on the user's preferences.

[0689] A "user" is an individual who uses the system to select and evaluate beverages based on their preferences.

[0690] "Interface means" refers to a device or software that receives information input by a user and converts audio data into text data as needed.

[0691] "Natural language processing technology" is a technique that analyzes text data obtained from users, understands its content, and identifies the user's preferences.

[0692] A "generative AI model" is an artificial intelligence technology used to create multiple suggestions, generating the optimal candidates based on the user's past selection history and trends.

[0693] A "presentation means" is a mechanism for visually or audibly informing the user of the generated proposal.

[0694] "Blending means" refers to a device or process that automatically generates instructions for blending a beverage based on the user's selection and transmits them to a device.

[0695] A "storage method" refers to a means of receiving user feedback, storing it in a database, and using it to improve future proposals.

[0696] "Past selection history" refers to a record of suggestions previously selected by the user, and is used as reference when generating future suggestion candidates.

[0697] "Trend information" refers to data that indicates current trends in the market and society, and is used as a reference when generating potential proposals.

[0698] "Feedback" refers to user evaluations and opinions on suggested beverages, and is information that can be used to improve the system.

[0699] This invention is a system for quickly suggesting beverages tailored to user preferences and automating the blending process. The system mainly consists of a user terminal, a server, and external blending equipment.

[0700] The user uses a device to input their mood and preferences for the day via voice or text. The device uses speech recognition technology (e.g., a common speech recognition API) for voice input, converting the voice into text data. This allows smartphones or dedicated devices to be used as the user interface.

[0701] Next, the device sends text data to the server. The server analyzes this data using natural language processing techniques (e.g., a general natural language processing library) to understand the user's mood and preferences. Based on this analysis, it refers to past selection history and trend information, and uses a generative AI model to generate suggested beverages that are best suited to the user.

[0702] The generated suggestions are displayed to the user via a terminal, and the user selects their desired beverage from among them. This selection information is transmitted via a server to an external mixing device. The server then issues instructions to the mixing device to accurately mix the selected beverage. As a result, the beverage is automatically mixed and served to the user.

[0703] Furthermore, after the beverage is served, the user sends feedback via their device. This feedback information is stored in a database and used to improve the accuracy of future suggestions, making it possible to provide users with a more personalized experience.

[0704] For example, if a user enters "I want a refreshing cocktail" into their device, the server uses this information and, based on the results of analysis using natural language processing technology, generates suggestions such as "Mojito" or "Gin and Tonic." If the user selects Mojito, the mixing machine automatically creates a Mojito based on that information. The generative AI model that underpins this process is continuously improved based on user feedback.

[0705] An example of a prompt to input into a generative AI model is: "Generate options to suggest the best cocktail based on the user's preferences. User's mood: Refreshing."

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] The user inputs their mood and preferences for the day into the device. Input can be done via voice or text. In the case of voice input, the device uses speech recognition technology to convert the voice into text data. At this stage, the input is raw voice or text data, and the output is the converted text data.

[0709] Step 2:

[0710] The terminal sends text data obtained from the user to the server. The server receives this data and analyzes it using natural language processing techniques. Through this analysis, the user's mood and preferences are identified. The input is text data from the terminal, and the output is preference information extracted through the analysis.

[0711] Step 3:

[0712] The server uses the analysis results to reference the user's past selection history and trend information, and generates multiple suggested options using a generative AI model. The generative AI model uses prompt sentences to generate suggestions that match the user's preferences. Specifically, prompt sentences such as "User's mood: Refreshing" are used. The input is preference information and historical data, and the output is a list of suggested cocktails.

[0713] Step 4:

[0714] The terminal presents the user with a list of suggested beverages received from the server. The user selects their desired beverage from the presented options. At this stage, the input is the list of suggestions from the server, and the output is the beverage information selected by the user.

[0715] Step 5:

[0716] The server transmits instructions for preparing beverages to an external mixing device based on the user's selection. Specifically, it transmits commands to the device based on the selected beverage recipe. The input is the user's selection information, and the output is the commands to the mixing device.

[0717] Step 6:

[0718] After being served their beverage, the user enters feedback into a terminal. The terminal sends this feedback to a server, which stores the feedback information in a database. The input is the user's feedback, and the output is the updated database information. This information is used to inform future suggestions.

[0719] (Application Example 1)

[0720] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0721] When users choose a cocktail at a bar or restaurant, there is a need for an appropriate support system that can quickly select a beverage that suits their preferences and mood, and automatically mix it for them. Traditional methods often present challenges, such as making difficult choices and providing personalized suggestions.

[0722] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0723] In this invention, the server includes terminal means for receiving emotional information input by the user and converting it into information data, calculation means for generating a number of options based on the user's preferences, and display means for presenting the options to the user and outputting the selected information according to the user's choice. This enables the rapid and accurate provision of cocktails based on the individual user's preferences.

[0724] "Emotional information" refers to information related to the user's mood and preferences, and is data entered as voice or text.

[0725] "Terminal means" refers to devices or systems that receive input information from users and perform data conversion as needed.

[0726] "Information data" refers to digital data obtained by converting a user's emotional information into a specific format.

[0727] "Computational means" refers to systems or algorithms that perform processing to generate a large number of options based on user preferences.

[0728] "Options" refer to multiple suggested choices presented to the user, which are generated based on specific conditions.

[0729] "Display means" refers to devices or interfaces that provide users with options and further support the user's selection.

[0730] The following describes embodiments for carrying out the invention. This invention is a system that personalizes the user's cocktail selection experience and provides it quickly and accurately. This system is composed of multiple hardware and software components.

[0731] First, the "terminal device" receives emotional information from the user in the form of voice or text. This device uses the Google Speech-to-Text API to convert voice input into text data. In addition, the user's mood and preferences, as emotional information, are processed as digital data.

[0732] Next, the "server" analyzes this information data using the Google Cloud Natural Language API. Based on the analyzed data, the "computational tool" uses OpenAI's GPT model to generate a large number of cocktail options based on the user's preferences.

[0733] The "display means" presents the generated options to the user and assists the user in selecting a specific cocktail. The selected information is then output to the user in an appropriate format.

[0734] After the user selects a cocktail, the server transmits mixing instructions to the automated cocktail machine via a "control system." The cocktail is then automatically mixed and served. Finally, user feedback is received, and the accumulated records are updated based on that feedback.

[0735] As a concrete example, a user might input "I'd like a refreshing cocktail." This prompt is parsed and processed using the Google Cloud Natural Language API and OpenAI's GPT model, and suggested cocktails include options such as "Mojito" and "Margarita." The cocktail selected by the user is automatically brewed and served quickly.

[0736] An example of a prompt to the generating AI model is: "Based on the user's preference information, please suggest the cocktail that best suits their current mood. Please also take into account past selection history and trends." In this way, the present invention realizes a cocktail service that is responsive to the individual preferences of the user.

[0737] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0738] Step 1:

[0739] The device receives either user voice or text input. In the case of voice input, the Google Speech-to-Text API is used to convert the voice data into text data. In this case, the input is the user's speech, and the output is the transcribed information.

[0740] Step 2:

[0741] The server receives text data sent from the terminal and performs analysis using the Google Cloud Natural Language API. The input is text data, and the system processes it to extract user preferences and moods, outputting the extracted preference information as the analysis result.

[0742] Step 3:

[0743] Based on the analysis results, the server uses OpenAI's GPT model to generate cocktail options that suit the user's preferences. The input at this stage is the analyzed preference information, and past selection history and trend information are also taken into consideration, resulting in the output of multiple cocktail suggestions.

[0744] Step 4:

[0745] The server sends a list of cocktail options to the terminal, and the "display device" presents them to the user. The input is cocktail suggestions from the server, and the output is the options presented to the user.

[0746] Step 5:

[0747] The user selects one of their preferred cocktails from the presented options. In this step, the user's selection is the input, and the information of this selected cocktail is the output.

[0748] Step 6:

[0749] The server controls the automated cocktail machine to send mixing instructions based on the user's selection. The input here is information about the selected cocktail, and the output is an automated mixing instruction.

[0750] Step 7:

[0751] The device receives user feedback, sends it to the server, and updates the record. In this process, the input is user feedback information, and the output is the updated record data.

[0752] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0753] This invention is an automated cocktail suggestion system that incorporates an emotion engine to recognize the user's emotions, thereby enabling personalized suggestions tailored to the user's emotional state. Specific embodiments of the system are described below.

[0754] When a user inputs their current mood or preferences via voice or text through the device, the device receives it and converts it into text using speech recognition technology. At this point, the emotion engine activates and detects the user's emotional state from their voice or text. For example, it analyzes emotions such as "happy" or "tired" from the tone and rhythm of the voice and the words used in the text.

[0755] The server analyzes the text data received from the user, along with the results of the emotion engine's analysis, to generate cocktail suggestions based on the user's preferences and emotions. An algorithm is executed that queries the user's past selection history and trend information to form multiple cocktail candidates that match their emotions.

[0756] The generated suggestions are presented to the user from the terminal, and the user can choose their desired cocktail from the presented options. The selected information is sent to the server, and instructions are sent to the cocktail machine via the mixing mechanism. As a result, the selected cocktail is automatically mixed and served.

[0757] Meanwhile, after the cocktail is served, users can input feedback via a terminal. This feedback is re-analyzed by the emotion engine, and new emotion data is stored in the database. This data is used to improve the accuracy of future suggestions and is used to adjust the selection criteria for suggested cocktails in real time.

[0758] For example, if a user enters "I want something to cheer me up today," the emotion engine detects a positive emotion from the user's input. Based on this, the server compares it with past selection history and popular trends to generate suggestions such as "a cocktail based on an energy drink." In this way, the present invention aims to provide an experience closely related to the user's emotions.

[0759] The following describes the processing flow.

[0760] Step 1:

[0761] The user inputs their mood or preferences via voice or text through the device. The device receives the voice data and, in the case of voice input, converts it into text data using speech recognition technology.

[0762] Step 2:

[0763] The device sends the received text data to the server. The emotion engine also analyzes the voice and text data to extract the user's emotional state.

[0764] Step 3:

[0765] The server analyzes the received text data to understand the user's preferences, and, taking into account the emotional information obtained by the emotion engine, generates multiple cocktail suggestions.

[0766] Step 4:

[0767] The server references the user's past selection history and current trend information to create a list of cocktail candidates best suited to their emotional state and preferences.

[0768] Step 5:

[0769] The server sends a list of generated cocktail candidates to the terminal, which then presents the list to the user visually or audibly.

[0770] Step 6:

[0771] The user selects their desired cocktail from the presented options. The terminal sends the selection information to the server.

[0772] Step 7:

[0773] The server generates instructions for the cocktail machine based on the user's selection and transmits those instructions via the mixing device.

[0774] Step 8:

[0775] The cocktail machine automatically mixes cocktails according to the server's instructions and serves them to the user.

[0776] Step 9:

[0777] After receiving their cocktail, users input feedback such as their satisfaction level and comments via a terminal. The terminal then sends this feedback to the server.

[0778] Step 10:

[0779] The server re-analyzes the received feedback using an emotion engine and updates the database. This updated data is used to improve the accuracy of future suggestions and adjust the suggestion criteria in real time.

[0780] (Example 2)

[0781] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] In today's world, systems that provide appropriate suggestions based on user emotions are rare, and providing highly accurate suggestions tailored to individual emotional states is difficult. Furthermore, conventional systems are limited to generating suggestions based on user selection history and trends, lacking the flexibility to consider user emotional states. This leads to a decline in the quality of the user experience and makes it difficult to increase satisfaction.

[0783] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0784] In this invention, the server includes data processing means for analyzing user emotional information and generating suggestions based thereon, generation means for optimizing by combining the user's past selection history and trend information, and data management means for analyzing feedback and adjusting the criteria for suggestions in real time. This enables optimal suggestions that respond to the diverse emotions of the user.

[0785] "Data processing means" refers to a device or process that receives voice or text input from a user and converts that data into an analyzable format as needed.

[0786] "Analysis means" refers to technologies and systems for identifying and detecting a user's emotional state from received text data.

[0787] "Generation means" refers to algorithms and devices that create multiple suggested options based on the user's emotional state, selection history, and trend information, and then provide the optimal suggestion.

[0788] A "presentation means" is a device or interface that provides the generated proposal to the user visually or audibly and accepts the user's selection.

[0789] "Blending instruction means" refers to a device or process that generates instructions for automatically blending a beverage based on a user's selection and transmits these instructions to a machine.

[0790] "Data management means" refers to a device that includes functions and analytical techniques for receiving user feedback, updating the database, and improving the accuracy of future suggestions.

[0791] This invention is an automated suggestion system that provides personalized cocktail suggestions based on the user's emotional state. Specific embodiments of the system are described below.

[0792] Terminal role:

[0793] The user inputs their emotional state and preferences via voice or text through the device. The device converts this input into text data using speech recognition software (e.g., a speech-to-text API). The converted data is then analyzed by an emotion engine to detect the user's emotional state.

[0794] Server role:

[0795] Based on the analysis results sent from the emotion engine, the server generates multiple cocktail suggestion candidates, taking into account the user's emotional state, past selection history, and trend information. Machine learning algorithms (e.g., AI model frameworks) are used for suggestion generation, and optimized suggestions are provided to the user.

[0796] Specific example:

[0797] For example, if a user enters "I want to refresh myself today," the emotion engine detects the emotion "refreshment" from this input. The server compares the analysis results with past selection history and current beverage trend information to generate suggestions such as "mint-based cocktails."

[0798] Example of a prompt:

[0799] "Please write a program that recognizes the user's emotions and generates cocktail suggestions based on those emotions."

[0800] Thus, the present invention aims to provide a more refined user experience through suggestions that respond to the user's emotions.

[0801] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0802] Step 1:

[0803] Users input their emotional state and desires via voice or text through a device. This input is converted into text data using speech recognition software. The input mainly consists of the user's mood and image of the beverage they want, and the output is the converted text data.

[0804] Step 2:

[0805] The device sends text data to an emotion engine, which uses natural language processing technology to analyze the user's emotional state. Specifically, it analyzes words and phrases in the text and extracts emotional keywords such as "refreshed" and "relaxed." The input is the converted text data, and the output is the user's identified emotional state.

[0806] Step 3:

[0807] The server receives the analysis results from the emotion engine and generates cocktail suggestion candidates, taking into account the user's past selection history and trend information. The algorithm used is an AI model framework that executes database queries to obtain the necessary data. The inputs are emotion state, selection history, and trend information, and the output is a list of suggested cocktail candidates.

[0808] Step 4:

[0809] The terminal presents the user with cocktail suggestions obtained from the server. Specifically, it displays multiple suggested cocktails on the screen and prompts the user to make a selection visually or audibly. The input is the cocktail options sent from the server, and the output is the cocktail information selected by the user.

[0810] Step 5:

[0811] The server receives the user's selection and sends mixing instructions to the cocktail machine. Specifically, it generates data that specifies the quantities and order of each ingredient based on the selected cocktail's recipe. The input is the cocktail selected by the user, and the output is the specific mixing instructions sent to the cocktail machine.

[0812] Step 6:

[0813] After enjoying their cocktail, the user enters feedback into the device. This feedback is then analyzed again by the emotion engine to extract the user's impressions and areas for improvement. The input is the user's feedback, and the output is the analyzed impression data.

[0814] Step 7:

[0815] The server stores the analyzed feedback in a database and uses it to improve the accuracy of future suggestions. Specifically, it compares and analyzes past data and dynamically adjusts the algorithm settings. The input is the analyzed feedback data, and the output is updated database information.

[0816] (Application Example 2)

[0817] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0818] It is difficult to suggest the most suitable beverage based on the user's emotional state, and there is a lack of means to improve the accuracy of service provision based on user preferences. Furthermore, there is a need to utilize user feedback in real time to improve the accuracy of suggested options.

[0819] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0820] In this invention, the server includes communication means for receiving user input information and converting it into voice or text data, emotion analysis means for recognizing the emotional state from the analyzed data, and processing means for generating multiple suggestion candidates based on the analysis results and preferences. This makes it possible to provide personalized suggestions that are in line with the user's emotions and improve the quality of the service.

[0821] A "user" refers to an individual or group that uses the system, and is the entity that receives suggestions based on their emotional state and preferences.

[0822] "Communication means" refers to the means of receiving voice or text information from a user and incorporating it into the system as data.

[0823] "Emotion analysis means" refers to a method of identifying emotions using user voice and text data and performing analysis in order to make appropriate service suggestions.

[0824] A "processing means" is a means that has the function of generating multiple suggested options based on the results of sentiment analysis and the user's preferences.

[0825] A "suggestion" refers to a set of multiple service or product options created based on the user's emotional state and preferences.

[0826] A "storage method" refers to a means of saving user feedback and updating information resources.

[0827] The system for realizing this application includes a terminal that receives user input information and a server that performs analysis and makes suggestions. First, the user inputs their current mood and preferences via voice or text through a smartphone app. The terminal receives this information and, if voice input is received, converts it into text data using speech recognition software. This process uses general speech recognition technology.

[0828] Next, the server receives this text data and uses its sentiment analysis engine to recognize the user's emotional state. The sentiment analysis engine analyzes the vocabulary and context used in the text to determine emotions such as "positive," "negative," or "relaxed."

[0829] Next, the server uses the analysis results to run an algorithm that takes into account the user's past selection history and current trend information, generating multiple suggested options. By using well-known algorithms and machine learning models, it is possible to provide suggestions that best match the user's preferences.

[0830] The generated suggested cocktails are sent to the terminal and presented to the user. When the user selects their desired cocktail, that information is returned to the server, and automated instructions for mixing the cocktail are sent to the mixing device. As a result, the selected beverage is automatically mixed and served to the user.

[0831] After a beverage is served, users can provide feedback through the app. This feedback is re-analyzed on the server side, compared with the sentiment analysis results, and the information resources are updated. This makes it possible to continuously improve the accuracy of suggestions for future orders. For example, if a user inputs "I want something refreshing," the sentiment engine will detect "relaxation," and the server will suggest something like a "herbal tea-based cocktail" based on past data and trends.

[0832] An example of a generated AI model prompt is: "Identify the user's emotion from the text 'Something to cheer me up today' and suggest a suitable cocktail."

[0833] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0834] Step 1:

[0835] The terminal receives input information from the user. This input is provided via a smartphone application, either as voice or text. In the case of voice input, the terminal uses voice recognition software to convert the voice into text data. This text data then becomes the input for the next processing step.

[0836] Step 2:

[0837] The server analyzes the text data received from the terminal. Using an emotion analysis engine, it evaluates the vocabulary and context within the text to determine the user's emotional state. In this process, emotional information such as positive, negative, and relaxed is output.

[0838] Step 3:

[0839] Based on the analyzed sentiment information, the server generates suggested options by referencing the user's past selection history and the latest trend information. By applying the algorithm, multiple beverage options that best match the user's current sentiment and preferences are generated and output to the next step.

[0840] Step 4:

[0841] The terminal presents the user with suggested cocktails from the server. Once the user selects their desired cocktail from the presented options, this selection information is sent back from the terminal to the server. This selection information then becomes the input for the next process.

[0842] Step 5:

[0843] The server receives the user's selection information and generates automatic mixing instructions. These instructions are sent to the cooking device, which automatically creates the selected beverage. This process ensures that the user receives the perfect cocktail.

[0844] Step 6:

[0845] The server receives feedback information from the user. The feedback is then analyzed again for sentiment, comparing the user's emotions with the accuracy of the suggestions, and updating the system's information resources. This improves the accuracy of future suggestions. Through this feedback process, the system continuously learns and improves.

[0846] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0847] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0848] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0849] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0850] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0851] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0852] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0853] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0854] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0855] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0856] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0857] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0858] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0859] 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.

[0860] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0861] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0862] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0863] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0864] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0865] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0866] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0867] The following is further disclosed regarding the embodiments described above.

[0868] (Claim 1)

[0869] An interface means that receives information entered by the user and converts it into text data,

[0870] This text data is analyzed and a processing means is used to generate multiple suggested candidates based on the user's preferences.

[0871] A presentation means that presents proposed options to the user and outputs the selected information according to the user's choice,

[0872] A mixing means that automatically generates instructions for mixing beverages based on user selection and transmits them to the device,

[0873] A means of receiving user feedback and updating the accumulated database,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, which uses an algorithm that takes into account the user's past selection history and trend information when generating candidate suggestions.

[0877] (Claim 3)

[0878] The system according to claim 1, which analyzes user feedback and adjusts the selection criteria for proposed candidates in real time.

[0879] "Example 1"

[0880] (Claim 1)

[0881] A medium that receives information entered by the user and converts the audio data into text data,

[0882] This technology processes this text data and uses natural language processing techniques to analyze user preferences.

[0883] A means of using a generative AI model to generate multiple proposals,

[0884] A means of informing the user of the generated suggestions and displaying selected information based on the user's choices,

[0885] A device that automatically generates instructions to create a beverage based on the user's selection and sends them to the device,

[0886] A means of improving the database that collects and stores user feedback,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which uses a calculation method that takes into account the user's past selection history and trend information when generating suggestions.

[0890] (Claim 3)

[0891] The system according to claim 1, which processes user feedback and immediately adjusts the selection criteria for proposals.

[0892] "Application Example 1"

[0893] (Claim 1)

[0894] A terminal means that receives emotional information entered by a user and converts it into information data,

[0895] This information data is analyzed and a computation means is used to generate a large number of options based on the user's preferences.

[0896] A display means that presents options to the user and outputs the selected information according to the user's choice,

[0897] A control means that automatically generates instructions for mixing beverages based on the user's selection and transmits them to the device,

[0898] A recording method that receives user evaluations and updates accumulated records,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, which uses a method that takes into account the user's past selection history and trend information when generating options.

[0902] (Claim 3)

[0903] The system according to claim 1, which analyzes user evaluations and sequentially adjusts the selection criteria for options.

[0904] "Example 2 of combining an emotion engine"

[0905] (Claim 1)

[0906] A data processing means that receives emotional information entered by a user and converts it into text data,

[0907] This text data is analyzed to detect the user's emotional state, and

[0908] A generation method that generates multiple suggested candidates based on the user's emotional state and optimizes them by considering past selection history and trend information,

[0909] A presentation means that presents the generated candidate suggestions to the user and outputs the selected information according to the user's choice,

[0910] A mixing instruction means that transmits instructions to the device to automatically mix the beverage based on the user's selection,

[0911] A data management system that receives and analyzes user feedback and updates the database to improve the accuracy of suggestions,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein an algorithm that takes into account the user's emotional state is applied to the generated candidate suggestions.

[0915] (Claim 3)

[0916] The system according to claim 1, which analyzes user feedback and adjusts the selection criteria for suggested candidates in real time according to the user's emotional state.

[0917] "Application example 2 when combining with an emotional engine"

[0918] (Claim 1)

[0919] A communication means that receives information entered by a user and converts it into voice or text data,

[0920] This includes an emotion analysis means that analyzes this audio or text data to recognize the user's emotional state,

[0921] A processing means that generates multiple suggested candidates according to the user's preferences based on the analysis results,

[0922] A presentation means that presents proposed options to the user and outputs the selected information according to the user's choice,

[0923] A mixing means that automatically generates instructions for mixing beverages based on the user's selection and transmits them to the device,

[0924] A means of receiving user feedback and updating accumulated information resources,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, which uses an algorithm that improves the accuracy of suggestions in real time by taking into account the user's past selection history and trend information when generating suggested candidates.

[0928] (Claim 3)

[0929] The system according to claim 1, which analyzes user feedback, adjusts the criteria for selecting candidate suggestions in real time, and links them with the results of sentiment analysis. [Explanation of Symbols]

[0930] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An interface means that receives information entered by the user and converts it into text data, This text data is analyzed and a processing means is used to generate multiple suggested candidates based on the user's preferences. A presentation means that presents proposed options to the user and outputs the selected information according to the user's choice, A mixing means that automatically generates instructions for mixing beverages based on user selection and transmits them to the device, A means of receiving user feedback and updating the accumulated database, A system that includes this.

2. The system according to claim 1, which uses an algorithm that takes into account the user's past selection history and trend information when generating candidate suggestions.

3. The system according to claim 1, which analyzes user feedback and adjusts the selection criteria for proposed candidates in real time.

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