system

Abstract

We provide the system. [Solution] An information processing device for inputting user preference information, An analysis device that analyzes and patterns the preference information, A selection device that selects the optimal beverage based on the pattern, A generating device that generates a list of recommended beverages selected, A system including an ordering device that places orders based on the recommended list.

Description

Technical Field

[0004] , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is executed 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 modern times, there are many options for selecting beverages with strong palatability, which is cumbersome for users. Also, since individual preferences continue to change, there is a problem that it is difficult to make an optimal choice according to the situation. Furthermore, by considering the combination of foods related to beverages, it is required to provide a valuable purchasing experience for users.

Means for Solving the Problems

[0005] The present invention includes an information processing device that inputs and analyzes user preference information, and an analysis device that performs pattern generation based on the preference information. Furthermore, the system includes a selection device that selects the optimal beverage and a generation device that generates a recommendation list based on that selection, thereby solving the above problems. In addition, by including a device that suggests related food products, a more comprehensive suggestion can be provided. By including a device for placing orders and a device for communicating with sellers, efficient transactions can be realized.

[0006] An "information processing device" is a device that receives data input from users and processes it appropriately.

[0007] An "analysis device" is a device that analyzes data based on input preference information and identifies important patterns.

[0008] A "selection device" is a device used to select the optimal beverage based on the analysis results.

[0009] A "generation device" is a device that organizes information related to selected beverages and creates a recommendation list to present to users.

[0010] A "suggestion device" is a device that provides additional information, such as food items, related to the selected beverage.

[0011] An "ordering device" is a device used to place orders for beverages and related products based on the user's selection.

[0012] A "communication device" is a device that transmits order information to the seller and facilitates transactions between the parties. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of 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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 an 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 an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, the labeled 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), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

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

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0028] 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.

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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for selecting highly palatable beverages according to the individual preferences of users and providing an optimal purchasing experience. This system is mainly implemented by the following components: an information processing device, an analysis device, a selection device, a generation device, a suggestion device, an ordering device, and a communication device.

[0035] The user uses a terminal to input their preferences. For example, the user might input that they like acidic coffee. This information is sent to the server and temporarily stored by the information processing device.

[0036] The server's analysis system analyzes preference information received from users. Using past data and information from other users, the analysis system identifies the user's preference patterns. This analysis highlights the flavors and characteristics that the user prefers.

[0037] Next, the server's selection device selects the most suitable beverage based on this analysis result. The selection device refers to beverage information in the database and identifies the beverage that best matches the user's preferences. For example, acidic Ethiopian coffee may be selected as a candidate.

[0038] The server's generating device creates a recommendation list for the user, centered around the selected beverage. This list includes detailed information about the coffee, relevant reviews, pricing, and pairing suggestions (e.g., it pairs well with chocolate).

[0039] Based on this information, the user selects their desired beverage via the terminal. Once the selection is complete, the terminal sends instructions from the ordering device to the server.

[0040] The server uses an ordering device to place orders for selected beverages with the vendor. A communication device exchanges information with the vendor to support the smooth execution of orders. Furthermore, a suggestion device recommends related food items and additional beverages.

[0041] In this way, the present invention is a system that enables appropriate beverage selection tailored to individual preferences and provides a fulfilling purchasing experience. Users can make the optimal choice based on their preferences, and the system supports that choice, making the beverage purchasing process more comfortable.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users use a device to input information about their preferences. Specifically, they select or enter information such as the characteristics of their favorite flavors (e.g., sourness or sweetness), the names of specific beverages they have enjoyed in the past, allergies, and disliked ingredients.

[0045] Step 2:

[0046] The terminal sends the entered preference information to the server. The transmitted data is temporarily held in the server's information processing device.

[0047] Step 3:

[0048] The server's analysis device begins processing the received preference information. Using an AI algorithm, it identifies preference trends and, if necessary, extracts patterns by referencing data from other users with similar preferences.

[0049] Step 4:

[0050] The server's selection device chooses the optimal beverage based on the analysis results. It uses a scoring system to determine the beverage that best matches the user's preferences from the beverage database and lists the candidates.

[0051] Step 5:

[0052] The server's generator produces a recommendation list that includes the selected beverages. The list includes detailed information about each beverage (e.g., flavor, price, origin), reviews, and suggestions for related foods (e.g., suitable cheeses or sweets for pairing).

[0053] Step 6:

[0054] Users view a list of recommendations on their device. They examine the list and select beverages that match their preferences. They also consider accompanying suggestions to make an overall decision on which products to purchase.

[0055] Step 7:

[0056] The terminal creates an order instruction based on the user's selection and sends it to the server. This instruction includes details of the selected beverage and delivery information.

[0057] Step 8:

[0058] The server's ordering device processes orders with the seller based on the received instructions. The communication device contacts the seller to ensure the order is executed correctly. During this process, data exchange with relevant agents is also performed as needed.

[0059] Through the steps described above, this system efficiently selects and orders beverages according to the user's preferences.

[0060] (Example 1)

[0061] 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."

[0062] Traditional beverage recommendation systems had problems such as failing to appropriately select beverages that matched users' preferences or having a cumbersome purchasing process. As a result, users were not able to have a satisfactory purchasing experience, and the convenience of the system itself was reduced.

[0063] 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.

[0064] In this invention, the server includes terminal means for receiving user preference information, analysis means for analyzing this information and identifying patterns, beverage selection means based on the analysis results, and recommendation list generation means including details of the selected beverages and pairing suggestions. This enables beverage selection tailored to the user's individual preferences and smooth ordering.

[0065] A "terminal device" is an electronic device used by users to input their preference information and transmit it to the system.

[0066] An "analysis device" is a device that receives user preference information, compares it with past data, and identifies patterns.

[0067] A "selection method" is a device that has the function of selecting the most suitable beverage from a database based on the analyzed preference patterns.

[0068] A "generation means" is a device that has the function of creating a recommendation list that includes detailed information about the selected beverage and pairing suggestions.

[0069] An "ordering device" is a device that has the function of facilitating the purchase of beverages selected by the user.

[0070] "Communication means" refers to devices used to transmit information about selected and ordered beverages to sales businesses and to facilitate necessary information exchange.

[0071] A "selection method" is a device that has the function of allowing users to identify their desired beverage from a recommendation list and transmit that information to the system.

[0072] A "suggestion device" is a device that has the function of suggesting food products or additional beverages related to the selected beverage.

[0073] This invention is a system that selects beverages according to the individual preferences of users and provides an optimal purchasing experience. The system consists of an information processing device, an analysis device, a selection device, a generation device, a suggestion device, an ordering device, and a communication device.

[0074] The user inputs their preferences using a terminal. Specifically, they input information such as their preference for sour flavors and send this information to the server via the terminal interface. The server temporarily stores this information in its information processing device.

[0075] The server's analysis system uses a proprietary AI model to analyze user preference information. By referencing past data and the preferences of other users, it identifies preference patterns, effectively highlighting the user's preferred tastes.

[0076] Based on the analysis results, the server's selection device chooses the optimal beverage from the database. The selected beverages are compiled into a recommendation list by the generating device, which includes detailed information about the beverage, its price, and pairing suggestions (for example, that a specific type of chocolate would be a good match).

[0077] The user receives this recommendation list via a terminal and selects a beverage that suits their preferences. Order information for the selected beverage is then sent to the server, and the ordering device places the order with the vendor. The communication device is responsible for exchanging the necessary information with the vendor to fulfill the order.

[0078] The suggestion device automatically generates and presents to the user suggestions for food pairings and additional beverages that complement a drink. For example, it can suggest, "Dark chocolate pairs well with this coffee."

[0079] As a concrete example, a prompt using a generative AI model might be: "Please recommend a new coffee brand with a characteristic acidity that the user prefers. Also, please suggest suitable pairings." By inputting this prompt into the generative AI model, appropriate recommendations are made.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses a terminal to input their preferences. For example, they might write, "I like acidic coffee." This input information is converted into a digital format on the terminal and sent to the server. The entered preference information is formatted appropriately, a recipient address is set, and it is delivered to the server via the network.

[0083] Step 2:

[0084] The server uses an information processing device to temporarily store the received preference information. The input here is the user's preference information, which is stored in the server's memory. The data is then structured and converted into a format for analysis.

[0085] Step 3:

[0086] The server's analysis system uses an AI model to analyze the received preference information. The input is preference patterns, and the output is the identification of user-specific preference patterns. The AI ​​model references past databases and the preferences of similar users, and extracts trends using data mining techniques.

[0087] Step 4:

[0088] The server selection device selects the optimal beverage that matches the user's preferences based on the analysis results. The input is the analyzed preference pattern, and the output is a list of beverages extracted from the database. Here, filtering and ranking are performed based on the selection criteria.

[0089] Step 5:

[0090] The server's generation device creates a recommendation list based on the selected beverages. This step compiles the beverage details, price, reviews, and pairing suggestions into a list. The input is the selected beverage information, and the output is the recommendation list presented to the user. The generated list is laid out in a visually appealing format.

[0091] Step 6:

[0092] The user reviews a list of recommendations provided on the terminal and selects their desired beverage. The input is the recommendation list, and the output is the user's selection information. Here, the interface operation when selecting from the list is important, as the selection is accurately recorded.

[0093] Step 7:

[0094] The terminal receives user selections and sends order instructions to the server. Input is user selection information, and output is order information sent to the server. The information is organized into necessary variables and reformatted to ensure accurate data delivery to the server.

[0095] Step 8:

[0096] The server uses an ordering device to place an order for the selected beverage with the vendor. At this stage, communication takes place with the vendor using a communication method. Input is order information, and output is order confirmation. The process includes inventory check and delivery date setting.

[0097] (Application Example 1)

[0098] 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."

[0099] There is a need to improve user satisfaction by providing beverage selection based on preferences and a smooth purchasing process. However, existing systems have not been smooth in the entire process from inputting preference information to beverage selection and purchase, and there have been problems such as the extra hassle involved in payment in particular. Furthermore, the suggestion of related food products and smooth communication with suppliers are also important requirements, but these are also issues that have not been adequately resolved.

[0100] 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.

[0101] In this invention, the server includes data processing means for inputting user preference information, information analysis means for analyzing and patternizing the preference information, and payment processing means for performing electronic payment for the selected beverage. This allows the user to consistently perform everything from selecting a beverage that suits their preferences to making an electronic payment.

[0102] "Data processing means" refers to means that receive preference information entered by the user and process that information to store and manage it in an appropriate format.

[0103] "Information analysis means" refers to means used to analyze input preference information and identify the user's preference patterns by comparing it with past data.

[0104] "Selection method" refers to a method for selecting the most suitable beverage from a database based on user preference patterns obtained through analysis.

[0105] "Information generation means" refers to means for creating a recommendation list that includes detailed information about the selected beverages.

[0106] "Information processing means" refers to the means for placing necessary orders based on the generated recommendation list.

[0107] "Payment processing means" refers to the means for smoothly facilitating electronic payments for selected beverages.

[0108] An "information suggestion device" is a means of suggesting food products related to a selected beverage.

[0109] "Information exchange means" refers to the means of exchanging information and communicating with suppliers regarding beverages for which an order has been placed.

[0110] The system that realizes this invention recommends appropriate beverages based on preference information entered by the user via a terminal such as a smartphone, and further enables electronic payment for the selected beverage. The specific operation of this system is described below.

[0111] The server receives preference information sent from the user through a smartphone application. The server is provided through an application built with a mobile app development framework such as React Native. The user inputs information about their preferences (e.g., prefers acidic coffee), and this information is sent to the server.

[0112] The server processes data using Node.js and stores the information in a MongoDB database. This data analysis system compares the user's preferences with past data and other users' preference patterns to select the most suitable beverage for them. The selected beverages are then generated as a recommendation list by the information generation system and displayed on the user's terminal along with detailed information and related reviews.

[0113] Furthermore, for beverages selected by the user, electronic payment is immediately processed using payment methods such as the Stripe API. In addition, related food products are suggested through an information suggestion device, and smooth information management is facilitated with suppliers through information exchange means.

[0114] As a concrete example, a user traveling with a friend might use a smartphone app to be recommended a special seasonal coffee only available locally, and complete the payment on the spot, making the trip even more enjoyable. The prompt for this application states, "Generate a scenario in which the user preference-based beverage selection system recommends a limited-edition beverage at the travel destination."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user inputs preference information using a device. This information is received through a smartphone interface, and specific preference data, such as "I like acidic coffee," is generated. This data is then sent to the server as input.

[0118] Step 2:

[0119] The server processes the received preference information using Node.js and stores it in a MongoDB database for analysis. This stored preference data is used as input for data analysis. The information analysis tool compares this data with historical data and data from other users to extract the user's preference patterns. This process outputs analysis results that are useful for the user's beverage selection.

[0120] Step 3:

[0121] Based on the analyzed preference patterns, the server uses selection methods to identify relevant beverages from the database. This process selects the beverage that best matches the user's preferences as a candidate. The selected beverage information is output and used as input for the next step.

[0122] Step 4:

[0123] The server generates a recommendation list using an information generation mechanism. This list includes detailed information, prices, reviews, and related suggestions for the selected beverages. The generated list is sent to the user's terminal and presented to the user as output.

[0124] Step 5:

[0125] The user selects a beverage from a recommendation list, and that information is sent to the server. The selected beverage information is then used as input to proceed to the payment processing flow.

[0126] Step 6:

[0127] The server uses payment processing methods such as the Stripe API to execute electronic payments for the selected beverages. Payment information and status are generated and notified to the user's device after the transaction is complete.

[0128] Step 7:

[0129] The server uses an information suggestion device to suggest additional foods related to the selected beverage. The suggested food information is generated and provided to the user as output.

[0130] Step 8:

[0131] The server uses information exchange mechanisms to communicate with suppliers and confirm details about the ordered beverages. The server processes the communication to determine whether or not the beverages can be supplied, and provides necessary feedback to both the supplier and the user as output.

[0132] 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.

[0133] This invention relates to a system that selects beverages by integrating user preference information and emotional information. This system consists of an information processing device, an analysis device, a selection device, a generation device, an ordering device, a communication device, and an emotional engine.

[0134] Users can input preference information through the device. This information includes preferred flavors, previously enjoyed beverages, and ingredients to avoid. The device also incorporates an emotion sensor that detects the user's emotional state in real time from their facial expressions and voice, and transmits this data to the emotion engine.

[0135] The server receives preference and emotional information transmitted from the terminal. The analysis device matches and analyzes the preference information with emotional data from the emotional engine to identify the user's current mood and long-term preference patterns. This analysis forms the basis for highly personalized beverage selection.

[0136] Based on the analysis results, the server's selection device chooses the optimal beverage. Emotional information plays a crucial role in this selection process; for example, it can suggest a specific herbal tea when the user is in a calm mood, or an energy drink when they are seeking vitality.

[0137] The server's generation system creates a recommendation list centered around the selected beverages and also suggests related food items. This list includes detailed information and reviews, as well as selection reasons tailored to the user's emotional state.

[0138] The user reviews the recommendation list via the terminal and selects the beverage that best suits their mood and preferences. After selection, the terminal generates an order instruction and sends it to the server.

[0139] The server's ordering device places orders with sellers according to received instructions. The communication device communicates with sellers to ensure smooth transactions and utilizes a feedback loop, including sentiment information, to improve the accuracy of future recommendations.

[0140] In this way, a system is built that enables highly accurate beverage selection and purchasing experiences that reflect users' preferences and emotions. Users can improve their daily consumption experience by receiving beverage suggestions that suit their individual mood and situation.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users input their preferences using a device. This includes their taste preferences, past favorite beverages, and ingredients they want to avoid. Furthermore, an emotion sensor built into the device collects emotional data through the user's facial expressions and voice.

[0144] Step 2:

[0145] The terminal collects preference information and emotional data and sends it to the server. The information processing device temporarily stores this data.

[0146] Step 3:

[0147] The server's analysis system analyzes preference and emotional information received from users. Using machine learning algorithms, it identifies patterns and changes in preferences based on the user's emotional state, and identifies their current mood and future preferences.

[0148] Step 4:

[0149] Based on the analysis results, the server's selection device chooses the optimal beverage. For example, if the user is relaxed, it might select a beverage like chamomile tea. If the user needs energy, it might suggest espresso.

[0150] Step 5:

[0151] The server's generation system creates a recommendation list centered around the selected beverages. The list includes each beverage's flavor, price, past reviews, and selection criteria based on user sentiment. It also includes suggested food pairings (e.g., whether it pairs well with a particular chocolate).

[0152] Step 6:

[0153] The user reviews the recommendation list on the device. Considering the provided information and their own emotional state, they select the most suitable beverage. Once the selection is complete, the device automatically generates an order instruction.

[0154] Step 7:

[0155] The order instructions generated by the terminal are sent to the ordering device on the server. Based on these instructions, the ordering device sends the order to the seller and makes any necessary adjustments.

[0156] Step 8:

[0157] The server's communication equipment works in conjunction with sellers to manage order processing and ensure smooth operation. Simultaneously, incorporating transaction feedback into sentiment analysis improves the accuracy of future selections.

[0158] Through these steps, it becomes possible to provide a highly accurate beverage selection and purchasing experience that reflects the user's emotions and preferences.

[0159] (Example 2)

[0160] 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 will be referred to as the "terminal."

[0161] In modern society, it is important to select products that meet the preferences and emotions of users, but conventional systems have difficulty in adequately reflecting the diverse needs and emotional states of users. Furthermore, there is a need to develop a commercial transaction system that allows users to purchase selected products quickly and easily.

[0162] 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.

[0163] In this invention, the server includes a user interface means for inputting user preference data, a data analysis means for analyzing the preference data and emotional data to identify the user's mood and long-term preference patterns, and a selection means using an algorithm for selecting beverages based on the analysis results. This makes it possible to select products that reflect the user's preferences and emotions.

[0164] "User interface means" refers to devices or software used by users to input preference data into a system, and includes input devices such as touchscreens and keyboards.

[0165] "Data analysis means" refers to a device or process that receives user preference data and emotional data, analyzes them, and identifies the user's mood and long-term preference patterns.

[0166] "Selection method" refers to an algorithm or process for selecting a beverage suitable for the user based on the analysis results obtained from data analysis methods.

[0167] "Data generation means" refers to a device or program that has the function of creating a recommendation list based on information about selected beverages.

[0168] A "means of commercial transaction" refers to a device or system that has the function of facilitating the order process based on a generated list of recommendations and conducting transactions with the seller.

[0169] A "data suggestion means" refers to a device or algorithm that has the function of suggesting food products related to a selected beverage.

[0170] "Data communication means" refers to a device or protocol used to communicate and exchange information with the seller regarding the ordered goods.

[0171] This invention is a system that selects beverages based on the user's preferences and emotions, and mainly consists of a server, a terminal, and a user. Specific embodiments for implementing this invention are described below.

[0172] The server uses SciKit-Learn, a Python library, as its data analysis tool. Preference and sentiment data are analyzed through algorithms to identify the user's mood and long-term preference patterns. Based on these analysis results, a generative AI model is used to select the most suitable beverage for the user. The beverage selection process uses the prompt "Please suggest the best beverage for this mood."

[0173] The terminal is equipped with a touchscreen and emotion sensor as user interface means, and acquires preference data and real-time emotion data from the user. This data is encrypted and sent to a server for analysis. The terminal also presents the user with a list of recommendations generated by a data generation means and proceeds with the ordering process for the selected beverage.

[0174] Users specify their preferences in detail through the system, or receive beverage suggestions based on emotional information extracted by an emotion sensor. For example, if a user feels like "I want to relax today," the system recommends chamomile tea. Once the user selects a beverage, the terminal uses data communication to send the order information to a server for commercial transactions, ensuring smooth communication with the seller.

[0175] This allows users to receive appropriate product suggestions tailored to their preferences and emotions, enabling them to complete purchases effectively and quickly. This system is implemented using specific software such as Python, SciKit-Learn, and generative AI models.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] Users input preference data through the device. This input includes preferred flavors, previously selected beverages, and ingredients they wish to avoid. An emotion sensor built into the device acquires the user's emotion data in real time, detecting their current emotional state from their facial expressions and voice. This input data is temporarily stored in the device's local storage.

[0179] Step 2:

[0180] The terminal sends the acquired preference and emotion data to the server. This transmission is secure using the SSL encryption protocol. The server stores the received data in an analysis database. The output of this step is the analysis dataset on the server.

[0181] Step 3:

[0182] The server's data analysis method uses the SciKit-Learn library to analyze received preference and sentiment data. Clustering and classification algorithms are used in the data processing to identify the user's mood and long-term preference patterns. The analysis results are output as recommendation data.

[0183] Step 4:

[0184] The server selection method involves using a generative AI model based on the analysis results to select the optimal beverage according to the prompt message "Please suggest the best beverage for this mood." The generative AI model generates a list of beverages, which the server then formats into a detailed recommendation list. This list is then output.

[0185] Step 5:

[0186] The server's data generation mechanism sends a formatted recommendation list to the terminal. This list also includes food suggestions related to the selected beverage. The terminal displays the received recommendation list in its user interface and presents the suggestions to the user.

[0187] Step 6:

[0188] The user reviews the recommendation list displayed on the terminal and selects their favorite beverage. This selection is entered, and the terminal generates an order instruction. The order instruction is sent to the server, and the commercial transaction process begins.

[0189] Step 7:

[0190] The server's transaction mechanism communicates with the seller based on the order instructions. This communication uses a RESTful API protocol to ensure that orders are processed accurately. A confirmation message from the seller is returned to the server, and the user is notified of the final completion of the transaction.

[0191] This allows users to experience the selection of the optimal beverage based on their preferences and emotions, along with a smooth purchasing process.

[0192] (Application Example 2)

[0193] 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 device 14 will be referred to as the "terminal."

[0194] Conventional beverage selection systems typically based their recommendations on user preferences, but they failed to consider the user's emotional state, resulting in inadequate recommendations for specific emotional conditions. Therefore, the challenge lies in providing more personalized beverage selections tailored to individual users, along with the associated consumption experience.

[0195] 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.

[0196] In this invention, the server includes information processing means for inputting user preference information, analysis means for analyzing and patternizing the preference information, and selection means for selecting the optimal beverage based on the pattern and the user's emotional information. This enables more personalized beverage selection that reflects the user's emotional state.

[0197] "Information processing means" refers to a device or program for inputting and managing user preference information.

[0198] "Analysis means" refers to a device or program that has the function of analyzing input preference information and extracting specific preference patterns.

[0199] "Selection means" refers to a device or program for determining the optimal beverage based on analyzed patterns and user emotional information.

[0200] "Generation means" refers to a device or program created to provide users with a list of selected beverages.

[0201] "Ordering method" refers to a device or program for arranging necessary beverages based on the generated recommendation list.

[0202] An "automatic preparation means" is a device or program for preparing or serving ordered beverages in a manner that suits the user's emotional state.

[0203] "Suggestion means" refers to a device or program for suggesting additional ingredients or food products related to the selected beverage.

[0204] "Communication means" refers to a device or program that facilitates the exchange of information with the supplier.

[0205] In order to implement this invention, it is necessary to coordinate various information processing devices, analysis devices, selection devices, generation devices, ordering devices, automatic preparation devices, proposal devices, and communication devices.

[0206] First, the terminal acquires preference information from the user. In this process, the user inputs information such as their preferences, past drinking history, and ingredients they wish to avoid, through a smart device or interface. This information is then aggregated by an information processing system on the server.

[0207] Next, the server uses analysis tools to analyze the user's emotional information, which is collected separately from the acquired preference information. This analysis uses existing emotion analysis AI modules on data collected through facial recognition cameras and voice analysis microphones. Microsoft® Azure® emotion recognition APIs are one example. The analyzed data is used to determine the user's current emotional state.

[0208] Next, based on these analysis results, a selection process is initiated to determine the optimal beverage. This selection process comprehensively considers preference and emotional information to select the most appropriate beverage for the user.

[0209] Furthermore, the system generates a list of recommended beverages selected by the generation method, and this list includes detailed information, reviews, and reasons for selection. Based on this list of recommendations, users select the beverage that interests them most.

[0210] After selection, the ordering mechanism functions to reserve the selected beverage and place an order with the vending system. The automated preparation mechanism adjusts and serves the selected beverage appropriately according to the user's mood.

[0211] Furthermore, by using the suggestion method, related ingredients that pair well with the selected beverage can also be suggested, resulting in a richer consumer experience.

[0212] Finally, we will use communication methods to facilitate smooth information exchange with suppliers. The feedback obtained during this process will be used to improve the accuracy of future proposals.

[0213] As a concrete example, an analysis of a user's work-related fatigue results in the selection of herbal tea as a relaxing beverage, and preparations for serving it are made based on the emotional analysis. An example of a prompt sentence to be input into the generating AI model is, "Evaluate the stress level from the facial expressions and voice detected by the camera, and explain the reason for selecting a relaxing beverage."

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

[0215] Step 1:

[0216] The terminal collects preference information from the user. It obtains data such as preferred tastes, previously enjoyed beverages, and ingredients to avoid, which the user enters through their smart device. At this stage, the terminal receives the information using an input interface and sends it to the server. This information functions as a dataset necessary for subsequent selections.

[0217] Step 2:

[0218] The server receives preference information and emotional data acquired in real time from the terminal. Emotional data is collected from the user's facial expressions and voice tone using the facial recognition camera and voice analysis microphone built into the terminal. The received data is input into an emotional analysis AI module to determine the user's current emotional state. This analysis process outputs emotional state data.

[0219] Step 3:

[0220] Based on the analysis results, the server selects the most suitable beverage using a selection method. Referring to the analyzed preference patterns and emotional state, for example, if the user is seeking calmness, it selects herbal tea. Here, the input preference information and emotional information are matched by an algorithm, and as a result, the most suitable beverage for the user is output.

[0221] Step 4:

[0222] The server generates a recommendation list based on the selected beverage. This list includes detailed information, reviews, and selection reasons tailored to the user's emotional state. The generator compiles this information and outputs it as a list to the terminal. This allows the user to view detailed information and assists in making purchasing decisions.

[0223] Step 5:

[0224] The user selects a beverage from a list of recommendations and sends an order to the server via the terminal. The information of the selected beverage is entered into the ordering system, and an order is placed with the automated vending system. At this stage, order details are output in preparation for the actual procurement of the beverage, and the process proceeds to the next stage.

[0225] Step 6:

[0226] The automated preparation system activates, cooking or serving the selected beverage in a manner tailored to the user's emotional state. Information regarding the beverage preparation is input, and settings such as extraction time and temperature are adjusted to maximize relaxation. This completes the optimal serving experience for the user.

[0227] Step 7:

[0228] The server's communication system exchanges information with suppliers regarding the selected beverage. It receives feedback and delivery information from suppliers and updates the database to improve future suggestions and order accuracy. This process enhances overall system efficiency and user experience.

[0229] Throughout these steps, a highly personalized beverage delivery system is achieved, based on the user's emotional state and preferences.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention is a system for selecting highly palatable beverages according to the individual preferences of users and providing an optimal purchasing experience. This system is mainly implemented by the following components: an information processing device, an analysis device, a selection device, a generation device, a suggestion device, an ordering device, and a communication device.

[0247] The user uses a terminal to input their preferences. For example, the user might input that they like acidic coffee. This information is sent to the server and temporarily stored by the information processing device.

[0248] The server's analysis system analyzes preference information received from users. Using past data and information from other users, the analysis system identifies the user's preference patterns. This analysis highlights the flavors and characteristics that the user prefers.

[0249] Next, the server's selection device selects the most suitable beverage based on this analysis result. The selection device refers to beverage information in the database and identifies the beverage that best matches the user's preferences. For example, acidic Ethiopian coffee may be selected as a candidate.

[0250] The server's generating device creates a recommendation list for the user, centered around the selected beverage. This list includes detailed information about the coffee, relevant reviews, pricing, and pairing suggestions (e.g., it pairs well with chocolate).

[0251] Based on this information, the user selects their desired beverage via the terminal. Once the selection is complete, the terminal sends instructions from the ordering device to the server.

[0252] The server uses an ordering device to place orders for selected beverages with the vendor. A communication device exchanges information with the vendor to support the smooth execution of orders. Furthermore, a suggestion device recommends related food items and additional beverages.

[0253] In this way, the present invention is a system that enables appropriate beverage selection tailored to individual preferences and provides a fulfilling purchasing experience. Users can make the optimal choice based on their preferences, and the system supports that choice, making the beverage purchasing process more comfortable.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] Users use a device to input information about their preferences. Specifically, they select or enter information such as the characteristics of their favorite flavors (e.g., sourness or sweetness), the names of specific beverages they have enjoyed in the past, allergies, and disliked ingredients.

[0257] Step 2:

[0258] The terminal sends the entered preference information to the server. The transmitted data is temporarily held in the server's information processing device.

[0259] Step 3:

[0260] The server's analysis device begins processing the received preference information. Using an AI algorithm, it identifies preference trends and, if necessary, extracts patterns by referencing data from other users with similar preferences.

[0261] Step 4:

[0262] The server's selection device chooses the optimal beverage based on the analysis results. It uses a scoring system to determine the beverage that best matches the user's preferences from the beverage database and lists the candidates.

[0263] Step 5:

[0264] The server's generator produces a recommendation list that includes the selected beverages. The list includes detailed information about each beverage (e.g., flavor, price, origin), reviews, and suggestions for related foods (e.g., suitable cheeses or sweets for pairing).

[0265] Step 6:

[0266] Users view a list of recommendations on their device. They examine the list and select beverages that match their preferences. They also consider accompanying suggestions to make an overall decision on which products to purchase.

[0267] Step 7:

[0268] The terminal creates an order instruction based on the user's selection and sends it to the server. This instruction includes details of the selected beverage and delivery information.

[0269] Step 8:

[0270] The server's ordering device processes orders with the seller based on the received instructions. The communication device contacts the seller to ensure the order is executed correctly. During this process, data exchange with relevant agents is also performed as needed.

[0271] Through the steps described above, this system efficiently selects and orders beverages according to the user's preferences.

[0272] (Example 1)

[0273] 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."

[0274] Traditional beverage recommendation systems had problems such as failing to appropriately select beverages that matched users' preferences or having a cumbersome purchasing process. As a result, users were not able to have a satisfactory purchasing experience, and the convenience of the system itself was reduced.

[0275] 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.

[0276] In this invention, the server includes terminal means for receiving user preference information, analysis means for analyzing this information and identifying patterns, beverage selection means based on the analysis results, and recommendation list generation means including details of the selected beverages and pairing suggestions. This enables beverage selection tailored to the user's individual preferences and smooth ordering.

[0277] A "terminal device" is an electronic device used by users to input their preference information and transmit it to the system.

[0278] The "analysis means" is a device that has the function of receiving the preference information of the user and identifying patterns by comparing it with past data.

[0279] The "selection means" is a device that has the function of selecting the optimal beverage from within the database based on the analyzed preference pattern.

[0280] The "generation means" is a device that has the function of creating a recommendation list including detailed information about the selected beverage and pairing suggestions.

[0281] The "ordering means" is a device that has the function of smoothly performing the purchase of the beverage selected by the user.

[0282] The "communication means" is a device that conveys the information of the selected and ordered beverages to the sales business operator and conducts the necessary information exchange.

[0283] The "selection means" is a device that has the function of the user identifying the desired beverage from the recommendation list and transmitting the information to the system.

[0284] The "proposal means" is a device that has the function of proposing foods and additional beverages related to the selected beverage.

[0285] This invention is a system that selects beverages according to the individual preferences of the user and provides an optimal purchase experience. The system consists of an information processing device, an analysis device, a selection device, a generation device, a proposal device, an ordering device, and a communication device.

[0286] The user uses the terminal to input their preference information. Specifically, information such as preferring sourness is input and transmitted to the server via the terminal interface. The server temporarily holds this information in the information processing device.

[0287] The server's analysis system uses a proprietary AI model to analyze user preference information. By referencing past data and the preferences of other users, it identifies preference patterns, effectively highlighting the user's preferred tastes.

[0288] Based on the analysis results, the server's selection device chooses the optimal beverage from the database. The selected beverages are compiled into a recommendation list by the generating device, which includes detailed information about the beverage, its price, and pairing suggestions (for example, that a specific type of chocolate would be a good match).

[0289] The user receives this recommendation list via a terminal and selects a beverage that suits their preferences. Order information for the selected beverage is then sent to the server, and the ordering device places the order with the vendor. The communication device is responsible for exchanging the necessary information with the vendor to fulfill the order.

[0290] The suggestion device automatically generates and presents to the user suggestions for food pairings and additional beverages that complement a drink. For example, it can suggest, "Dark chocolate pairs well with this coffee."

[0291] As a concrete example, a prompt using a generative AI model might be: "Please recommend a new coffee brand with a characteristic acidity that the user prefers. Also, please suggest suitable pairings." By inputting this prompt into the generative AI model, appropriate recommendations are made.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The user uses a terminal to input their preferences. For example, they might write, "I like acidic coffee." This input information is converted into a digital format on the terminal and sent to the server. The entered preference information is formatted appropriately, a recipient address is set, and it is delivered to the server via the network.

[0295] Step 2:

[0296] The server uses an information processing device to temporarily store the received preference information. The input here is the user's preference information, which is stored in the server's memory. The data is then structured and converted into a format for analysis.

[0297] Step 3:

[0298] The server's analysis system uses an AI model to analyze the received preference information. The input is preference patterns, and the output is the identification of user-specific preference patterns. The AI ​​model references past databases and the preferences of similar users, and extracts trends using data mining techniques.

[0299] Step 4:

[0300] The server selection device selects the optimal beverage that matches the user's preferences based on the analysis results. The input is the analyzed preference pattern, and the output is a list of beverages extracted from the database. Here, filtering and ranking are performed based on the selection criteria.

[0301] Step 5:

[0302] The server's generation device creates a recommendation list based on the selected beverages. This step compiles the beverage details, price, reviews, and pairing suggestions into a list. The input is the selected beverage information, and the output is the recommendation list presented to the user. The generated list is laid out in a visually appealing format.

[0303] Step 6:

[0304] The user checks the recommended list provided by the terminal and selects the desired beverage. The input is the recommended list, and the output is the user's selection information. Here, the interface operation when selecting from the list is important, and the selected content is accurately recorded.

[0305] Step 7:

[0306] The terminal receives the user's selection and sends an order instruction to the server. The input is the user's selection information, and the output is the order information to the server. The information is organized as necessary variables and reformatted so that the data is accurately passed to the server.

[0307] Step 8:

[0308] The server uses an ordering device to execute an order for the selected beverage to the seller. At this stage, communication means are used to communicate with the seller. The input is the order information, and the output is an order confirmation. The processing includes inventory confirmation and delivery date setting.

[0309] (Application Example 1)

[0310] 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".

[0311] It is required to provide a smooth purchase process for beverage selection based on preferences and improve user satisfaction. However, in the existing systems, the series of processes from the input of preference information to beverage selection and purchase has not been smooth, and there has been a problem that it takes extra effort especially in payment. Also, although the proposal of related foods and smooth communication with suppliers are important requirements, these are also issues that have not been fully resolved.

[0312] 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.

[0313] In this invention, the server includes data processing means for inputting user preference information, information analysis means for analyzing and patternizing the preference information, and payment processing means for performing electronic payment for the selected beverage. This allows the user to consistently perform everything from selecting a beverage that suits their preferences to making an electronic payment.

[0314] "Data processing means" refers to means that receive preference information entered by the user and process that information to store and manage it in an appropriate format.

[0315] "Information analysis means" refers to means used to analyze input preference information and identify the user's preference patterns by comparing it with past data.

[0316] "Selection method" refers to a method for selecting the most suitable beverage from a database based on user preference patterns obtained through analysis.

[0317] "Information generation means" refers to means for creating a recommendation list that includes detailed information about the selected beverages.

[0318] "Information processing means" refers to the means for placing necessary orders based on the generated recommendation list.

[0319] "Payment processing means" refers to the means for smoothly facilitating electronic payments for selected beverages.

[0320] An "information suggestion device" is a means of suggesting food products related to a selected beverage.

[0321] "Information exchange means" refers to the means of exchanging information and communicating with suppliers regarding beverages for which an order has been placed.

[0322] The system that realizes this invention recommends appropriate beverages based on preference information entered by the user via a terminal such as a smartphone, and further enables electronic payment for the selected beverage. The specific operation of this system is described below.

[0323] The server receives preference information sent from the user through a smartphone application. The server is provided through an application built with a mobile app development framework such as React Native. The user inputs information about their preferences (e.g., prefers acidic coffee), and this information is sent to the server.

[0324] The server processes data using Node.js and stores the information in a MongoDB database. This data analysis system compares the user's preferences with past data and other users' preference patterns to select the most suitable beverage for them. The selected beverages are then generated as a recommendation list by the information generation system and displayed on the user's terminal along with detailed information and related reviews.

[0325] Furthermore, for beverages selected by the user, electronic payment is immediately processed using payment methods such as the Stripe API. In addition, related food products are suggested through an information suggestion device, and smooth information management is facilitated with suppliers through information exchange means.

[0326] As a concrete example, a user traveling with a friend might use a smartphone app to be recommended a special seasonal coffee only available locally, and complete the payment on the spot, making the trip even more enjoyable. The prompt for this application states, "Generate a scenario in which the user preference-based beverage selection system recommends a limited-edition beverage at the travel destination."

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The user inputs preference information using a device. This information is received through a smartphone interface, and specific preference data, such as "I like acidic coffee," is generated. This data is then sent to the server as input.

[0330] Step 2:

[0331] The server processes the received preference information using Node.js and stores it in a MongoDB database for analysis. This stored preference data is used as input for data analysis. The information analysis tool compares this data with historical data and data from other users to extract the user's preference patterns. This process outputs analysis results that are useful for the user's beverage selection.

[0332] Step 3:

[0333] Based on the analyzed preference patterns, the server uses selection methods to identify relevant beverages from the database. This process selects the beverage that best matches the user's preferences as a candidate. The selected beverage information is output and used as input for the next step.

[0334] Step 4:

[0335] The server generates a recommendation list using an information generation mechanism. This list includes detailed information, prices, reviews, and related suggestions for the selected beverages. The generated list is sent to the user's terminal and presented to the user as output.

[0336] Step 5:

[0337] The user selects a beverage from a recommendation list, and that information is sent to the server. The selected beverage information is then used as input to proceed to the payment processing flow.

[0338] Step 6:

[0339] The server uses payment processing methods such as the Stripe API to execute electronic payments for the selected beverages. Payment information and status are generated and notified to the user's device after the transaction is complete.

[0340] Step 7:

[0341] The server uses an information suggestion device to suggest additional foods related to the selected beverage. The suggested food information is generated and provided to the user as output.

[0342] Step 8:

[0343] The server uses information exchange mechanisms to communicate with suppliers and confirm details about the ordered beverages. The server processes the communication to determine whether or not the beverages can be supplied, and provides necessary feedback to both the supplier and the user as output.

[0344] 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.

[0345] This invention relates to a system that selects beverages by integrating user preference information and emotional information. This system consists of an information processing device, an analysis device, a selection device, a generation device, an ordering device, a communication device, and an emotional engine.

[0346] Users can input preference information through the device. This information includes preferred flavors, previously enjoyed beverages, and ingredients to avoid. The device also incorporates an emotion sensor that detects the user's emotional state in real time from their facial expressions and voice, and transmits this data to the emotion engine.

[0347] The server receives preference and emotional information transmitted from the terminal. The analysis device matches and analyzes the preference information with emotional data from the emotional engine to identify the user's current mood and long-term preference patterns. This analysis forms the basis for highly personalized beverage selection.

[0348] Based on the analysis results, the server's selection device chooses the optimal beverage. Emotional information plays a crucial role in this selection process; for example, it can suggest a specific herbal tea when the user is in a calm mood, or an energy drink when they are seeking vitality.

[0349] The server's generation system creates a recommendation list centered around the selected beverages and also suggests related food items. This list includes detailed information and reviews, as well as selection reasons tailored to the user's emotional state.

[0350] The user reviews the recommendation list via the terminal and selects the beverage that best suits their mood and preferences. After selection, the terminal generates an order instruction and sends it to the server.

[0351] The server's ordering device places orders with sellers according to received instructions. The communication device communicates with sellers to ensure smooth transactions and utilizes a feedback loop, including sentiment information, to improve the accuracy of future recommendations.

[0352] In this way, a system is built that enables highly accurate beverage selection and purchasing experiences that reflect users' preferences and emotions. Users can improve their daily consumption experience by receiving beverage suggestions that suit their individual mood and situation.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] Users input their preferences using a device. This includes their taste preferences, past favorite beverages, and ingredients they want to avoid. Furthermore, an emotion sensor built into the device collects emotional data through the user's facial expressions and voice.

[0356] Step 2:

[0357] The terminal collects preference information and emotional data and sends it to the server. The information processing device temporarily stores this data.

[0358] Step 3:

[0359] The server's analysis system analyzes preference and emotional information received from users. Using machine learning algorithms, it identifies patterns and changes in preferences based on the user's emotional state, and identifies their current mood and future preferences.

[0360] Step 4:

[0361] Based on the analysis results, the server's selection device chooses the optimal beverage. For example, if the user is relaxed, it might select a beverage like chamomile tea. If the user needs energy, it might suggest espresso.

[0362] Step 5:

[0363] The server's generation system creates a recommendation list centered around the selected beverages. The list includes each beverage's flavor, price, past reviews, and selection criteria based on user sentiment. It also includes suggested food pairings (e.g., whether it pairs well with a particular chocolate).

[0364] Step 6:

[0365] The user reviews the recommendation list on the device. Considering the provided information and their own emotional state, they select the most suitable beverage. Once the selection is complete, the device automatically generates an order instruction.

[0366] Step 7:

[0367] The order instructions generated by the terminal are sent to the ordering device on the server. Based on these instructions, the ordering device sends the order to the seller and makes any necessary adjustments.

[0368] Step 8:

[0369] The server's communication equipment works in conjunction with sellers to manage order processing and ensure smooth operation. Simultaneously, incorporating transaction feedback into sentiment analysis improves the accuracy of future selections.

[0370] Through these steps, it becomes possible to provide a highly accurate beverage selection and purchasing experience that reflects the user's emotions and preferences.

[0371] (Example 2)

[0372] 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".

[0373] In modern society, it is important to select products that meet the preferences and emotions of users, but conventional systems have difficulty in adequately reflecting the diverse needs and emotional states of users. Furthermore, there is a need to develop a commercial transaction system that allows users to purchase selected products quickly and easily.

[0374] 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.

[0375] In this invention, the server includes a user interface means for inputting user preference data, a data analysis means for analyzing the preference data and emotional data to identify the user's mood and long-term preference patterns, and a selection means using an algorithm for selecting beverages based on the analysis results. This makes it possible to select products that reflect the user's preferences and emotions.

[0376] "User interface means" refers to devices or software used by users to input preference data into a system, and includes input devices such as touchscreens and keyboards.

[0377] "Data analysis means" refers to a device or process that receives user preference data and emotional data, analyzes them, and identifies the user's mood and long-term preference patterns.

[0378] "Selection method" refers to an algorithm or process for selecting a beverage suitable for the user based on the analysis results obtained from data analysis methods.

[0379] "Data generation means" refers to a device or program that has the function of creating a recommendation list based on information about selected beverages.

[0380] A "means of commercial transaction" refers to a device or system that has the function of facilitating the order process based on a generated list of recommendations and conducting transactions with the seller.

[0381] A "data suggestion means" refers to a device or algorithm that has the function of suggesting food products related to a selected beverage.

[0382] "Data communication means" refers to a device or protocol used to communicate and exchange information with the seller regarding the ordered goods.

[0383] This invention is a system that selects beverages based on the user's preferences and emotions, and mainly consists of a server, a terminal, and a user. Specific embodiments for implementing this invention are described below.

[0384] The server uses SciKit-Learn, a Python library, as its data analysis tool. Preference and sentiment data are analyzed through algorithms to identify the user's mood and long-term preference patterns. Based on these analysis results, a generative AI model is used to select the most suitable beverage for the user. The beverage selection process uses the prompt "Please suggest the best beverage for this mood."

[0385] The terminal is equipped with a touchscreen and emotion sensor as user interface means, and acquires preference data and real-time emotion data from the user. This data is encrypted and sent to a server for analysis. The terminal also presents the user with a list of recommendations generated by a data generation means and proceeds with the ordering process for the selected beverage.

[0386] Users specify their preferences in detail through the system, or receive beverage suggestions based on emotional information extracted by an emotion sensor. For example, if a user feels like "I want to relax today," the system recommends chamomile tea. Once the user selects a beverage, the terminal uses data communication to send the order information to a server for commercial transactions, ensuring smooth communication with the seller.

[0387] This allows users to receive appropriate product suggestions tailored to their preferences and emotions, enabling them to complete purchases effectively and quickly. This system is implemented using specific software such as Python, SciKit-Learn, and generative AI models.

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

[0389] Step 1:

[0390] Users input preference data through the device. This input includes preferred flavors, previously selected beverages, and ingredients they wish to avoid. An emotion sensor built into the device acquires the user's emotion data in real time, detecting their current emotional state from their facial expressions and voice. This input data is temporarily stored in the device's local storage.

[0391] Step 2:

[0392] The terminal sends the acquired preference and emotion data to the server. This transmission is secure using the SSL encryption protocol. The server stores the received data in an analysis database. The output of this step is the analysis dataset on the server.

[0393] Step 3:

[0394] The server's data analysis method uses the SciKit-Learn library to analyze received preference and sentiment data. Clustering and classification algorithms are used in the data processing to identify the user's mood and long-term preference patterns. The analysis results are output as recommendation data.

[0395] Step 4:

[0396] The server selection method involves using a generative AI model based on the analysis results to select the optimal beverage according to the prompt message "Please suggest the best beverage for this mood." The generative AI model generates a list of beverages, which the server then formats into a detailed recommendation list. This list is then output.

[0397] Step 5:

[0398] The server's data generation mechanism sends a formatted recommendation list to the terminal. This list also includes food suggestions related to the selected beverage. The terminal displays the received recommendation list in its user interface and presents the suggestions to the user.

[0399] Step 6:

[0400] The user reviews the recommendation list displayed on the terminal and selects their favorite beverage. This selection is entered, and the terminal generates an order instruction. The order instruction is sent to the server, and the commercial transaction process begins.

[0401] Step 7:

[0402] The server's transaction mechanism communicates with the seller based on the order instructions. This communication uses a RESTful API protocol to ensure that orders are processed accurately. A confirmation message from the seller is returned to the server, and the user is notified of the final completion of the transaction.

[0403] This allows users to experience the selection of the optimal beverage based on their preferences and emotions, along with a smooth purchasing process.

[0404] (Application Example 2)

[0405] 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."

[0406] Conventional beverage selection systems typically based their recommendations on user preferences, but they failed to consider the user's emotional state, resulting in inadequate recommendations for specific emotional conditions. Therefore, the challenge lies in providing more personalized beverage selections tailored to individual users, along with the associated consumption experience.

[0407] 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.

[0408] In this invention, the server includes information processing means for inputting user preference information, analysis means for analyzing and patternizing the preference information, and selection means for selecting the optimal beverage based on the pattern and the user's emotional information. This enables more personalized beverage selection that reflects the user's emotional state.

[0409] "Information processing means" refers to a device or program for inputting and managing user preference information.

[0410] "Analysis means" refers to a device or program that has the function of analyzing input preference information and extracting specific preference patterns.

[0411] "Selection means" refers to a device or program for determining the optimal beverage based on analyzed patterns and user emotional information.

[0412] "Generation means" refers to a device or program created to provide users with a list of selected beverages.

[0413] "Ordering method" refers to a device or program for arranging necessary beverages based on the generated recommendation list.

[0414] An "automatic preparation means" is a device or program for preparing or serving ordered beverages in a manner that suits the user's emotional state.

[0415] "Suggestion means" refers to a device or program for suggesting additional ingredients or food products related to the selected beverage.

[0416] "Communication means" refers to a device or program that facilitates the exchange of information with the supplier.

[0417] In order to implement this invention, it is necessary to coordinate various information processing devices, analysis devices, selection devices, generation devices, ordering devices, automatic preparation devices, proposal devices, and communication devices.

[0418] First, the terminal acquires preference information from the user. In this process, the user inputs information such as their preferences, past drinking history, and ingredients they wish to avoid, through a smart device or interface. This information is then aggregated by an information processing system on the server.

[0419] Next, the server uses analysis tools to analyze the user's emotional information, which is collected separately from the acquired preference information. This analysis uses existing emotion analysis AI modules on data collected through facial recognition cameras and voice analysis microphones. Microsoft Azure's emotion recognition API is one example. The analyzed data is used to determine the user's current emotional state.

[0420] Next, based on these analysis results, a selection process is initiated to determine the optimal beverage. This selection process comprehensively considers preference and emotional information to select the most appropriate beverage for the user.

[0421] Furthermore, the system generates a list of recommended beverages selected by the generation method, and this list includes detailed information, reviews, and reasons for selection. Based on this list of recommendations, users select the beverage that interests them most.

[0422] After selection, the ordering mechanism functions to reserve the selected beverage and place an order with the vending system. The automated preparation mechanism adjusts and serves the selected beverage appropriately according to the user's mood.

[0423] Furthermore, by using the suggestion method, related ingredients that pair well with the selected beverage can also be suggested, resulting in a richer consumer experience.

[0424] Finally, we will use communication methods to facilitate smooth information exchange with suppliers. The feedback obtained during this process will be used to improve the accuracy of future proposals.

[0425] As a concrete example, an analysis of a user's work-related fatigue results in the selection of herbal tea as a relaxing beverage, and preparations for serving it are made based on the emotional analysis. An example of a prompt sentence to be input into the generating AI model is, "Evaluate the stress level from the facial expressions and voice detected by the camera, and explain the reason for selecting a relaxing beverage."

[0426] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0427] Step 1:

[0428] The terminal collects preference information from the user. It obtains data such as preferred tastes, previously enjoyed beverages, and ingredients to avoid, which the user enters through their smart device. At this stage, the terminal receives the information using an input interface and sends it to the server. This information functions as a dataset necessary for subsequent selections.

[0429] Step 2:

[0430] The server receives preference information and emotional data acquired in real time from the terminal. Emotional data is collected from the user's facial expressions and voice tone using the facial recognition camera and voice analysis microphone built into the terminal. The received data is input into an emotional analysis AI module to determine the user's current emotional state. This analysis process outputs emotional state data.

[0431] Step 3:

[0432] Based on the analysis results, the server selects the most suitable beverage using a selection method. Referring to the analyzed preference patterns and emotional state, for example, if the user is seeking calmness, it selects herbal tea. Here, the input preference information and emotional information are matched by an algorithm, and as a result, the most suitable beverage for the user is output.

[0433] Step 4:

[0434] The server generates a recommendation list based on the selected beverage. This list includes detailed information, reviews, and selection reasons tailored to the user's emotional state. The generator compiles this information and outputs it as a list to the terminal. This allows the user to view detailed information and assists in making purchasing decisions.

[0435] Step 5:

[0436] The user selects a beverage from a list of recommendations and sends an order to the server via the terminal. The information of the selected beverage is entered into the ordering system, and an order is placed with the automated vending system. At this stage, order details are output in preparation for the actual procurement of the beverage, and the process proceeds to the next stage.

[0437] Step 6:

[0438] The automated preparation system activates, cooking or serving the selected beverage in a manner tailored to the user's emotional state. Information regarding the beverage preparation is input, and settings such as extraction time and temperature are adjusted to maximize relaxation. This completes the optimal serving experience for the user.

[0439] Step 7:

[0440] The server's communication system exchanges information with suppliers regarding the selected beverage. It receives feedback and delivery information from suppliers and updates the database to improve future suggestions and order accuracy. This process enhances overall system efficiency and user experience.

[0441] Throughout these steps, a highly personalized beverage delivery system is achieved, based on the user's emotional state and preferences.

[0442] 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.

[0443] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0444] 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.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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".

[0458] This invention is a system for selecting highly palatable beverages according to the individual preferences of users and providing an optimal purchasing experience. This system is mainly implemented by the following components: an information processing device, an analysis device, a selection device, a generation device, a suggestion device, an ordering device, and a communication device.

[0459] The user uses a terminal to input their preferences. For example, the user might input that they like acidic coffee. This information is sent to the server and temporarily stored by the information processing device.

[0460] The server's analysis system analyzes preference information received from users. Using past data and information from other users, the analysis system identifies the user's preference patterns. This analysis highlights the flavors and characteristics that the user prefers.

[0461] Next, the server's selection device selects the most suitable beverage based on this analysis result. The selection device refers to beverage information in the database and identifies the beverage that best matches the user's preferences. For example, acidic Ethiopian coffee may be selected as a candidate.

[0462] The server's generating device creates a recommendation list for the user, centered around the selected beverage. This list includes detailed information about the coffee, relevant reviews, pricing, and pairing suggestions (e.g., it pairs well with chocolate).

[0463] Based on this information, the user selects their desired beverage via the terminal. Once the selection is complete, the terminal sends instructions from the ordering device to the server.

[0464] The server uses an ordering device to place orders for selected beverages with the vendor. A communication device exchanges information with the vendor to support the smooth execution of orders. Furthermore, a suggestion device recommends related food items and additional beverages.

[0465] In this way, the present invention is a system that enables appropriate beverage selection tailored to individual preferences and provides a fulfilling purchasing experience. Users can make the optimal choice based on their preferences, and the system supports that choice, making the beverage purchasing process more comfortable.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] Users use a device to input information about their preferences. Specifically, they select or enter information such as the characteristics of their favorite flavors (e.g., sourness or sweetness), the names of specific beverages they have enjoyed in the past, allergies, and disliked ingredients.

[0469] Step 2:

[0470] The terminal sends the entered preference information to the server. The transmitted data is temporarily held in the server's information processing device.

[0471] Step 3:

[0472] The server's analysis device begins processing the received preference information. Using an AI algorithm, it identifies preference trends and, if necessary, extracts patterns by referencing data from other users with similar preferences.

[0473] Step 4:

[0474] The server's selection device chooses the optimal beverage based on the analysis results. It uses a scoring system to determine the beverage that best matches the user's preferences from the beverage database and lists the candidates.

[0475] Step 5:

[0476] The server's generator produces a recommendation list that includes the selected beverages. The list includes detailed information about each beverage (e.g., flavor, price, origin), reviews, and suggestions for related foods (e.g., suitable cheeses or sweets for pairing).

[0477] Step 6:

[0478] Users view a list of recommendations on their device. They examine the list and select beverages that match their preferences. They also consider accompanying suggestions to make an overall decision on which products to purchase.

[0479] Step 7:

[0480] The terminal creates an order instruction based on the user's selection and sends it to the server. This instruction includes details of the selected beverage and delivery information.

[0481] Step 8:

[0482] The server's ordering device processes orders with the seller based on the received instructions. The communication device contacts the seller to ensure the order is executed correctly. During this process, data exchange with relevant agents is also performed as needed.

[0483] Through the steps described above, this system efficiently selects and orders beverages according to the user's preferences.

[0484] (Example 1)

[0485] 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."

[0486] Traditional beverage recommendation systems had problems such as failing to appropriately select beverages that matched users' preferences or having a cumbersome purchasing process. As a result, users were not able to have a satisfactory purchasing experience, and the convenience of the system itself was reduced.

[0487] 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.

[0488] In this invention, the server includes terminal means for receiving user preference information, analysis means for analyzing this information and identifying patterns, beverage selection means based on the analysis results, and recommendation list generation means including details of the selected beverages and pairing suggestions. This enables beverage selection tailored to the user's individual preferences and smooth ordering.

[0489] A "terminal device" is an electronic device used by users to input their preference information and transmit it to the system.

[0490] An "analysis device" is a device that receives user preference information, compares it with past data, and identifies patterns.

[0491] A "selection method" is a device that has the function of selecting the most suitable beverage from a database based on the analyzed preference patterns.

[0492] A "generation means" is a device that has the function of creating a recommendation list that includes detailed information about the selected beverage and pairing suggestions.

[0493] An "ordering device" is a device that has the function of facilitating the purchase of beverages selected by the user.

[0494] "Communication means" refers to devices used to transmit information about selected and ordered beverages to sales businesses and to facilitate necessary information exchange.

[0495] A "selection method" is a device that has the function of allowing users to identify their desired beverage from a recommendation list and transmit that information to the system.

[0496] A "suggestion device" is a device that has the function of suggesting food products or additional beverages related to the selected beverage.

[0497] This invention is a system that selects beverages according to the individual preferences of users and provides an optimal purchasing experience. The system consists of an information processing device, an analysis device, a selection device, a generation device, a suggestion device, an ordering device, and a communication device.

[0498] The user inputs their preferences using a terminal. Specifically, they input information such as their preference for sour flavors and send this information to the server via the terminal interface. The server temporarily stores this information in its information processing device.

[0499] The server's analysis system uses a proprietary AI model to analyze user preference information. By referencing past data and the preferences of other users, it identifies preference patterns, effectively highlighting the user's preferred tastes.

[0500] Based on the analysis results, the server's selection device chooses the optimal beverage from the database. The selected beverages are compiled into a recommendation list by the generating device, which includes detailed information about the beverage, its price, and pairing suggestions (for example, that a specific type of chocolate would be a good match).

[0501] The user receives this recommendation list via a terminal and selects a beverage that suits their preferences. Order information for the selected beverage is then sent to the server, and the ordering device places the order with the vendor. The communication device is responsible for exchanging the necessary information with the vendor to fulfill the order.

[0502] The suggestion device automatically generates and presents to the user suggestions for food pairings and additional beverages that complement a drink. For example, it can suggest, "Dark chocolate pairs well with this coffee."

[0503] As a concrete example, a prompt using a generative AI model might be: "Please recommend a new coffee brand with a characteristic acidity that the user prefers. Also, please suggest suitable pairings." By inputting this prompt into the generative AI model, appropriate recommendations are made.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] The user uses a terminal to input their preferences. For example, they might write, "I like acidic coffee." This input information is converted into a digital format on the terminal and sent to the server. The entered preference information is formatted appropriately, a recipient address is set, and it is delivered to the server via the network.

[0507] Step 2:

[0508] The server uses an information processing device to temporarily store the received preference information. The input here is the user's preference information, which is stored in the server's memory. The data is then structured and converted into a format for analysis.

[0509] Step 3:

[0510] The server's analysis system uses an AI model to analyze the received preference information. The input is preference patterns, and the output is the identification of user-specific preference patterns. The AI ​​model references past databases and the preferences of similar users, and extracts trends using data mining techniques.

[0511] Step 4:

[0512] The server selection device selects the optimal beverage that matches the user's preferences based on the analysis results. The input is the analyzed preference pattern, and the output is a list of beverages extracted from the database. Here, filtering and ranking are performed based on the selection criteria.

[0513] Step 5:

[0514] The server's generation device creates a recommendation list based on the selected beverages. This step compiles the beverage details, price, reviews, and pairing suggestions into a list. The input is the selected beverage information, and the output is the recommendation list presented to the user. The generated list is laid out in a visually appealing format.

[0515] Step 6:

[0516] The user reviews a list of recommendations provided on the terminal and selects their desired beverage. The input is the recommendation list, and the output is the user's selection information. Here, the interface operation when selecting from the list is important, as the selection is accurately recorded.

[0517] Step 7:

[0518] The terminal receives user selections and sends order instructions to the server. Input is user selection information, and output is order information sent to the server. The information is organized into necessary variables and reformatted to ensure accurate data delivery to the server.

[0519] Step 8:

[0520] The server uses an ordering device to place an order for the selected beverage with the vendor. At this stage, communication takes place with the vendor using a communication method. Input is order information, and output is order confirmation. The process includes inventory check and delivery date setting.

[0521] (Application Example 1)

[0522] 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."

[0523] There is a need to improve user satisfaction by providing beverage selection based on preferences and a smooth purchasing process. However, existing systems have not been smooth in the entire process from inputting preference information to beverage selection and purchase, and there have been problems such as the extra hassle involved in payment in particular. Furthermore, the suggestion of related food products and smooth communication with suppliers are also important requirements, but these are also issues that have not been adequately resolved.

[0524] 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.

[0525] In this invention, the server includes data processing means for inputting user preference information, information analysis means for analyzing and patternizing the preference information, and payment processing means for performing electronic payment for the selected beverage. This allows the user to consistently perform everything from selecting a beverage that suits their preferences to making an electronic payment.

[0526] "Data processing means" refers to means that receive preference information entered by the user and process that information to store and manage it in an appropriate format.

[0527] "Information analysis means" refers to means used to analyze input preference information and identify the user's preference patterns by comparing it with past data.

[0528] "Selection method" refers to a method for selecting the most suitable beverage from a database based on user preference patterns obtained through analysis.

[0529] "Information generation means" refers to means for creating a recommendation list that includes detailed information about the selected beverages.

[0530] "Information processing means" refers to the means for placing necessary orders based on the generated recommendation list.

[0531] "Payment processing means" refers to the means for smoothly facilitating electronic payments for selected beverages.

[0532] An "information suggestion device" is a means of suggesting food products related to a selected beverage.

[0533] "Information exchange means" refers to the means of exchanging information and communicating with suppliers regarding beverages for which an order has been placed.

[0534] The system that realizes this invention recommends appropriate beverages based on preference information entered by the user via a terminal such as a smartphone, and further enables electronic payment for the selected beverage. The specific operation of this system is described below.

[0535] The server receives preference information sent from the user through a smartphone application. The server is provided through an application built with a mobile app development framework such as React Native. The user inputs information about their preferences (e.g., prefers acidic coffee), and this information is sent to the server.

[0536] The server processes data using Node.js and stores the information in a MongoDB database. This data analysis system compares the user's preferences with past data and other users' preference patterns to select the most suitable beverage for them. The selected beverages are then generated as a recommendation list by the information generation system and displayed on the user's terminal along with detailed information and related reviews.

[0537] Furthermore, for beverages selected by the user, electronic payment is immediately processed using payment methods such as the Stripe API. In addition, related food products are suggested through an information suggestion device, and smooth information management is facilitated with suppliers through information exchange means.

[0538] As a concrete example, a user traveling with a friend might use a smartphone app to be recommended a special seasonal coffee only available locally, and complete the payment on the spot, making the trip even more enjoyable. The prompt for this application states, "Generate a scenario in which the user preference-based beverage selection system recommends a limited-edition beverage at the travel destination."

[0539] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0540] Step 1:

[0541] The user inputs preference information using a device. This information is received through a smartphone interface, and specific preference data, such as "I like acidic coffee," is generated. This data is then sent to the server as input.

[0542] Step 2:

[0543] The server processes the received preference information using Node.js and stores it in a MongoDB database for analysis. This stored preference data is used as input for data analysis. The information analysis tool compares this data with historical data and data from other users to extract the user's preference patterns. This process outputs analysis results that are useful for the user's beverage selection.

[0544] Step 3:

[0545] Based on the analyzed preference patterns, the server uses selection methods to identify relevant beverages from the database. This process selects the beverage that best matches the user's preferences as a candidate. The selected beverage information is output and used as input for the next step.

[0546] Step 4:

[0547] The server generates a recommendation list using an information generation mechanism. This list includes detailed information, prices, reviews, and related suggestions for the selected beverages. The generated list is sent to the user's terminal and presented to the user as output.

[0548] Step 5:

[0549] The user selects a beverage from a recommendation list, and that information is sent to the server. The selected beverage information is then used as input to proceed to the payment processing flow.

[0550] Step 6:

[0551] The server uses payment processing methods such as the Stripe API to execute electronic payments for the selected beverages. Payment information and status are generated and notified to the user's device after the transaction is complete.

[0552] Step 7:

[0553] The server uses an information suggestion device to suggest additional foods related to the selected beverage. The suggested food information is generated and provided to the user as output.

[0554] Step 8:

[0555] The server uses information exchange mechanisms to communicate with suppliers and confirm details about the ordered beverages. The server processes the communication to determine whether or not the beverages can be supplied, and provides necessary feedback to both the supplier and the user as output.

[0556] 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.

[0557] This invention relates to a system that selects beverages by integrating user preference information and emotional information. This system consists of an information processing device, an analysis device, a selection device, a generation device, an ordering device, a communication device, and an emotional engine.

[0558] Users can input preference information through the device. This information includes preferred flavors, previously enjoyed beverages, and ingredients to avoid. The device also incorporates an emotion sensor that detects the user's emotional state in real time from their facial expressions and voice, and transmits this data to the emotion engine.

[0559] The server receives preference and emotional information transmitted from the terminal. The analysis device matches and analyzes the preference information with emotional data from the emotional engine to identify the user's current mood and long-term preference patterns. This analysis forms the basis for highly personalized beverage selection.

[0560] Based on the analysis results, the server's selection device chooses the optimal beverage. Emotional information plays a crucial role in this selection process; for example, it can suggest a specific herbal tea when the user is in a calm mood, or an energy drink when they are seeking vitality.

[0561] The server's generation system creates a recommendation list centered around the selected beverages and also suggests related food items. This list includes detailed information and reviews, as well as selection reasons tailored to the user's emotional state.

[0562] The user reviews the recommendation list via the terminal and selects the beverage that best suits their mood and preferences. After selection, the terminal generates an order instruction and sends it to the server.

[0563] The server's ordering device places orders with sellers according to received instructions. The communication device communicates with sellers to ensure smooth transactions and utilizes a feedback loop, including sentiment information, to improve the accuracy of future recommendations.

[0564] In this way, a system is built that enables highly accurate beverage selection and purchasing experiences that reflect users' preferences and emotions. Users can improve their daily consumption experience by receiving beverage suggestions that suit their individual mood and situation.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] Users input their preferences using a device. This includes their taste preferences, past favorite beverages, and ingredients they want to avoid. Furthermore, an emotion sensor built into the device collects emotional data through the user's facial expressions and voice.

[0568] Step 2:

[0569] The terminal collects preference information and emotional data and sends it to the server. The information processing device temporarily stores this data.

[0570] Step 3:

[0571] The server's analysis system analyzes preference and emotional information received from users. Using machine learning algorithms, it identifies patterns and changes in preferences based on the user's emotional state, and identifies their current mood and future preferences.

[0572] Step 4:

[0573] Based on the analysis results, the server's selection device chooses the optimal beverage. For example, if the user is relaxed, it might select a beverage like chamomile tea. If the user needs energy, it might suggest espresso.

[0574] Step 5:

[0575] The server's generation system creates a recommendation list centered around the selected beverages. The list includes each beverage's flavor, price, past reviews, and selection criteria based on user sentiment. It also includes suggested food pairings (e.g., whether it pairs well with a particular chocolate).

[0576] Step 6:

[0577] The user reviews the recommendation list on the device. Considering the provided information and their own emotional state, they select the most suitable beverage. Once the selection is complete, the device automatically generates an order instruction.

[0578] Step 7:

[0579] The order instructions generated by the terminal are sent to the ordering device on the server. Based on these instructions, the ordering device sends the order to the seller and makes any necessary adjustments.

[0580] Step 8:

[0581] The server's communication equipment works in conjunction with sellers to manage order processing and ensure smooth operation. Simultaneously, incorporating transaction feedback into sentiment analysis improves the accuracy of future selections.

[0582] Through these steps, it becomes possible to provide a highly accurate beverage selection and purchasing experience that reflects the user's emotions and preferences.

[0583] (Example 2)

[0584] 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."

[0585] In modern society, it is important to select products that meet the preferences and emotions of users, but conventional systems have difficulty in adequately reflecting the diverse needs and emotional states of users. Furthermore, there is a need to develop a commercial transaction system that allows users to purchase selected products quickly and easily.

[0586] 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.

[0587] In this invention, the server includes a user interface means for inputting user preference data, a data analysis means for analyzing the preference data and emotional data to identify the user's mood and long-term preference patterns, and a selection means using an algorithm for selecting beverages based on the analysis results. This makes it possible to select products that reflect the user's preferences and emotions.

[0588] "User interface means" refers to devices or software used by users to input preference data into a system, and includes input devices such as touchscreens and keyboards.

[0589] "Data analysis means" refers to a device or process that receives user preference data and emotional data, analyzes them, and identifies the user's mood and long-term preference patterns.

[0590] "Selection method" refers to an algorithm or process for selecting a beverage suitable for the user based on the analysis results obtained from data analysis methods.

[0591] "Data generation means" refers to a device or program that has the function of creating a recommendation list based on information about selected beverages.

[0592] A "means of commercial transaction" refers to a device or system that has the function of facilitating the order process based on a generated list of recommendations and conducting transactions with the seller.

[0593] A "data suggestion means" refers to a device or algorithm that has the function of suggesting food products related to a selected beverage.

[0594] "Data communication means" refers to a device or protocol used to communicate and exchange information with the seller regarding the ordered goods.

[0595] This invention is a system that selects beverages based on the user's preferences and emotions, and mainly consists of a server, a terminal, and a user. Specific embodiments for implementing this invention are described below.

[0596] The server uses SciKit-Learn, a Python library, as its data analysis tool. Preference and sentiment data are analyzed through algorithms to identify the user's mood and long-term preference patterns. Based on these analysis results, a generative AI model is used to select the most suitable beverage for the user. The beverage selection process uses the prompt "Please suggest the best beverage for this mood."

[0597] The terminal is equipped with a touchscreen and emotion sensor as user interface means, and acquires preference data and real-time emotion data from the user. This data is encrypted and sent to a server for analysis. The terminal also presents the user with a list of recommendations generated by a data generation means and proceeds with the ordering process for the selected beverage.

[0598] Users specify their preferences in detail through the system, or receive beverage suggestions based on emotional information extracted by an emotion sensor. For example, if a user feels like "I want to relax today," the system recommends chamomile tea. Once the user selects a beverage, the terminal uses data communication to send the order information to a server for commercial transactions, ensuring smooth communication with the seller.

[0599] This allows users to receive appropriate product suggestions tailored to their preferences and emotions, enabling them to complete purchases effectively and quickly. This system is implemented using specific software such as Python, SciKit-Learn, and generative AI models.

[0600] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0601] Step 1:

[0602] Users input preference data through the device. This input includes preferred flavors, previously selected beverages, and ingredients they wish to avoid. An emotion sensor built into the device acquires the user's emotion data in real time, detecting their current emotional state from their facial expressions and voice. This input data is temporarily stored in the device's local storage.

[0603] Step 2:

[0604] The terminal sends the acquired preference and emotion data to the server. This transmission is secure using the SSL encryption protocol. The server stores the received data in an analysis database. The output of this step is the analysis dataset on the server.

[0605] Step 3:

[0606] The server's data analysis method uses the SciKit-Learn library to analyze received preference and sentiment data. Clustering and classification algorithms are used in the data processing to identify the user's mood and long-term preference patterns. The analysis results are output as recommendation data.

[0607] Step 4:

[0608] The server selection method involves using a generative AI model based on the analysis results to select the optimal beverage according to the prompt message "Please suggest the best beverage for this mood." The generative AI model generates a list of beverages, which the server then formats into a detailed recommendation list. This list is then output.

[0609] Step 5:

[0610] The server's data generation mechanism sends a formatted recommendation list to the terminal. This list also includes food suggestions related to the selected beverage. The terminal displays the received recommendation list in its user interface and presents the suggestions to the user.

[0611] Step 6:

[0612] The user reviews the recommendation list displayed on the terminal and selects their favorite beverage. This selection is entered, and the terminal generates an order instruction. The order instruction is sent to the server, and the commercial transaction process begins.

[0613] Step 7:

[0614] The server's transaction mechanism communicates with the seller based on the order instructions. This communication uses a RESTful API protocol to ensure that orders are processed accurately. A confirmation message from the seller is returned to the server, and the user is notified of the final completion of the transaction.

[0615] This allows users to experience the selection of the optimal beverage based on their preferences and emotions, along with a smooth purchasing process.

[0616] (Application Example 2)

[0617] 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."

[0618] Conventional beverage selection systems typically based their recommendations on user preferences, but they failed to consider the user's emotional state, resulting in inadequate recommendations for specific emotional conditions. Therefore, the challenge lies in providing more personalized beverage selections tailored to individual users, along with the associated consumption experience.

[0619] 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.

[0620] In this invention, the server includes information processing means for inputting user preference information, analysis means for analyzing and patternizing the preference information, and selection means for selecting the optimal beverage based on the pattern and the user's emotional information. This enables more personalized beverage selection that reflects the user's emotional state.

[0621] "Information processing means" refers to a device or program for inputting and managing user preference information.

[0622] "Analysis means" refers to a device or program that has the function of analyzing input preference information and extracting specific preference patterns.

[0623] "Selection means" refers to a device or program for determining the optimal beverage based on analyzed patterns and user emotional information.

[0624] "Generation means" refers to a device or program created to provide users with a list of selected beverages.

[0625] "Ordering method" refers to a device or program for arranging necessary beverages based on the generated recommendation list.

[0626] An "automatic preparation means" is a device or program for preparing or serving ordered beverages in a manner that suits the user's emotional state.

[0627] "Suggestion means" refers to a device or program for suggesting additional ingredients or food products related to the selected beverage.

[0628] "Communication means" refers to a device or program that facilitates the exchange of information with the supplier.

[0629] In order to implement this invention, it is necessary to coordinate various information processing devices, analysis devices, selection devices, generation devices, ordering devices, automatic preparation devices, proposal devices, and communication devices.

[0630] First, the terminal acquires preference information from the user. In this process, the user inputs information such as their preferences, past drinking history, and ingredients they wish to avoid, through a smart device or interface. This information is then aggregated by an information processing system on the server.

[0631] Next, the server uses analysis tools to analyze the user's emotional information, which is collected separately from the acquired preference information. This analysis uses existing emotion analysis AI modules on data collected through facial recognition cameras and voice analysis microphones. Microsoft Azure's emotion recognition API is one example. The analyzed data is used to determine the user's current emotional state.

[0632] Next, based on these analysis results, a selection process is initiated to determine the optimal beverage. This selection process comprehensively considers preference and emotional information to select the most appropriate beverage for the user.

[0633] Furthermore, the system generates a list of recommended beverages selected by the generation method, and this list includes detailed information, reviews, and reasons for selection. Based on this list of recommendations, users select the beverage that interests them most.

[0634] After selection, the ordering mechanism functions to reserve the selected beverage and place an order with the vending system. The automated preparation mechanism adjusts and serves the selected beverage appropriately according to the user's mood.

[0635] Furthermore, by using the suggestion method, related ingredients that pair well with the selected beverage can also be suggested, resulting in a richer consumer experience.

[0636] Finally, we will use communication methods to facilitate smooth information exchange with suppliers. The feedback obtained during this process will be used to improve the accuracy of future proposals.

[0637] As a concrete example, an analysis of a user's work-related fatigue results in the selection of herbal tea as a relaxing beverage, and preparations for serving it are made based on the emotional analysis. An example of a prompt sentence to be input into the generating AI model is, "Evaluate the stress level from the facial expressions and voice detected by the camera, and explain the reason for selecting a relaxing beverage."

[0638] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0639] Step 1:

[0640] The terminal collects preference information from the user. It obtains data such as preferred tastes, previously enjoyed beverages, and ingredients to avoid, which the user enters through their smart device. At this stage, the terminal receives the information using an input interface and sends it to the server. This information functions as a dataset necessary for subsequent selections.

[0641] Step 2:

[0642] The server receives preference information and emotional data acquired in real time from the terminal. Emotional data is collected from the user's facial expressions and voice tone using the facial recognition camera and voice analysis microphone built into the terminal. The received data is input into an emotional analysis AI module to determine the user's current emotional state. This analysis process outputs emotional state data.

[0643] Step 3:

[0644] Based on the analysis results, the server selects the most suitable beverage using a selection method. Referring to the analyzed preference patterns and emotional state, for example, if the user is seeking calmness, it selects herbal tea. Here, the input preference information and emotional information are matched by an algorithm, and as a result, the most suitable beverage for the user is output.

[0645] Step 4:

[0646] The server generates a recommendation list based on the selected beverage. This list includes detailed information, reviews, and selection reasons tailored to the user's emotional state. The generator compiles this information and outputs it as a list to the terminal. This allows the user to view detailed information and assists in making purchasing decisions.

[0647] Step 5:

[0648] The user selects a beverage from a list of recommendations and sends an order to the server via the terminal. The information of the selected beverage is entered into the ordering system, and an order is placed with the automated vending system. At this stage, order details are output in preparation for the actual procurement of the beverage, and the process proceeds to the next stage.

[0649] Step 6:

[0650] The automated preparation system activates, cooking or serving the selected beverage in a manner tailored to the user's emotional state. Information regarding the beverage preparation is input, and settings such as extraction time and temperature are adjusted to maximize relaxation. This completes the optimal serving experience for the user.

[0651] Step 7:

[0652] The server's communication system exchanges information with suppliers regarding the selected beverage. It receives feedback and delivery information from suppliers and updates the database to improve future suggestions and order accuracy. This process enhances overall system efficiency and user experience.

[0653] Throughout these steps, a highly personalized beverage delivery system is achieved, based on the user's emotional state and preferences.

[0654] 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.

[0655] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0656] 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.

[0657] [Fourth Embodiment]

[0658] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0659] 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.

[0660] 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).

[0661] 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.

[0662] 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.

[0663] 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).

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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".

[0671] This invention is a system for selecting highly palatable beverages according to the individual preferences of users and providing an optimal purchasing experience. This system is mainly implemented by the following components: an information processing device, an analysis device, a selection device, a generation device, a suggestion device, an ordering device, and a communication device.

[0672] The user uses a terminal to input their preferences. For example, the user might input that they like acidic coffee. This information is sent to the server and temporarily stored by the information processing device.

[0673] The server's analysis system analyzes preference information received from users. Using past data and information from other users, the analysis system identifies the user's preference patterns. This analysis highlights the flavors and characteristics that the user prefers.

[0674] Next, the server's selection device selects the most suitable beverage based on this analysis result. The selection device refers to beverage information in the database and identifies the beverage that best matches the user's preferences. For example, acidic Ethiopian coffee may be selected as a candidate.

[0675] The server's generating device creates a recommendation list for the user, centered around the selected beverage. This list includes detailed information about the coffee, relevant reviews, pricing, and pairing suggestions (e.g., it pairs well with chocolate).

[0676] Based on this information, the user selects their desired beverage via the terminal. Once the selection is complete, the terminal sends instructions from the ordering device to the server.

[0677] The server uses an ordering device to place orders for selected beverages with the vendor. A communication device exchanges information with the vendor to support the smooth execution of orders. Furthermore, a suggestion device recommends related food items and additional beverages.

[0678] In this way, the present invention is a system that enables appropriate beverage selection tailored to individual preferences and provides a fulfilling purchasing experience. Users can make the optimal choice based on their preferences, and the system supports that choice, making the beverage purchasing process more comfortable.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] Users use a device to input information about their preferences. Specifically, they select or enter information such as the characteristics of their favorite flavors (e.g., sourness or sweetness), the names of specific beverages they have enjoyed in the past, allergies, and disliked ingredients.

[0682] Step 2:

[0683] The terminal sends the entered preference information to the server. The transmitted data is temporarily held in the server's information processing device.

[0684] Step 3:

[0685] The server's analysis device begins processing the received preference information. Using an AI algorithm, it identifies preference trends and, if necessary, extracts patterns by referencing data from other users with similar preferences.

[0686] Step 4:

[0687] The server's selection device chooses the optimal beverage based on the analysis results. It uses a scoring system to determine the beverage that best matches the user's preferences from the beverage database and lists the candidates.

[0688] Step 5:

[0689] The server's generator produces a recommendation list that includes the selected beverages. The list includes detailed information about each beverage (e.g., flavor, price, origin), reviews, and suggestions for related foods (e.g., suitable cheeses or sweets for pairing).

[0690] Step 6:

[0691] Users view a list of recommendations on their device. They examine the list and select beverages that match their preferences. They also consider accompanying suggestions to make an overall decision on which products to purchase.

[0692] Step 7:

[0693] The terminal creates an order instruction based on the user's selection and sends it to the server. This instruction includes details of the selected beverage and delivery information.

[0694] Step 8:

[0695] The server's ordering device processes orders with the seller based on the received instructions. The communication device contacts the seller to ensure the order is executed correctly. During this process, data exchange with relevant agents is also performed as needed.

[0696] Through the steps described above, this system efficiently selects and orders beverages according to the user's preferences.

[0697] (Example 1)

[0698] 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".

[0699] Traditional beverage recommendation systems had problems such as failing to appropriately select beverages that matched users' preferences or having a cumbersome purchasing process. As a result, users were not able to have a satisfactory purchasing experience, and the convenience of the system itself was reduced.

[0700] 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.

[0701] In this invention, the server includes terminal means for receiving user preference information, analysis means for analyzing this information and identifying patterns, beverage selection means based on the analysis results, and recommendation list generation means including details of the selected beverages and pairing suggestions. This enables beverage selection tailored to the user's individual preferences and smooth ordering.

[0702] A "terminal device" is an electronic device used by users to input their preference information and transmit it to the system.

[0703] An "analysis device" is a device that receives user preference information, compares it with past data, and identifies patterns.

[0704] A "selection method" is a device that has the function of selecting the most suitable beverage from a database based on the analyzed preference patterns.

[0705] A "generation means" is a device that has the function of creating a recommendation list that includes detailed information about the selected beverage and pairing suggestions.

[0706] An "ordering device" is a device that has the function of facilitating the purchase of beverages selected by the user.

[0707] "Communication means" refers to devices used to transmit information about selected and ordered beverages to sales businesses and to facilitate necessary information exchange.

[0708] A "selection method" is a device that has the function of allowing users to identify their desired beverage from a recommendation list and transmit that information to the system.

[0709] A "suggestion device" is a device that has the function of suggesting food products or additional beverages related to the selected beverage.

[0710] This invention is a system that selects beverages according to the individual preferences of users and provides an optimal purchasing experience. The system consists of an information processing device, an analysis device, a selection device, a generation device, a suggestion device, an ordering device, and a communication device.

[0711] The user inputs their preferences using a terminal. Specifically, they input information such as their preference for sour flavors and send this information to the server via the terminal interface. The server temporarily stores this information in its information processing device.

[0712] The server's analysis system uses a proprietary AI model to analyze user preference information. By referencing past data and the preferences of other users, it identifies preference patterns, effectively highlighting the user's preferred tastes.

[0713] Based on the analysis results, the server's selection device chooses the optimal beverage from the database. The selected beverages are compiled into a recommendation list by the generating device, which includes detailed information about the beverage, its price, and pairing suggestions (for example, that a specific type of chocolate would be a good match).

[0714] The user receives this recommendation list via a terminal and selects a beverage that suits their preferences. Order information for the selected beverage is then sent to the server, and the ordering device places the order with the vendor. The communication device is responsible for exchanging the necessary information with the vendor to fulfill the order.

[0715] The suggestion device automatically generates and presents to the user suggestions for food pairings and additional beverages that complement a drink. For example, it can suggest, "Dark chocolate pairs well with this coffee."

[0716] As a concrete example, a prompt using a generative AI model might be: "Please recommend a new coffee brand with a characteristic acidity that the user prefers. Also, please suggest suitable pairings." By inputting this prompt into the generative AI model, appropriate recommendations are made.

[0717] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0718] Step 1:

[0719] The user uses a terminal to input their preferences. For example, they might write, "I like acidic coffee." This input information is converted into a digital format on the terminal and sent to the server. The entered preference information is formatted appropriately, a recipient address is set, and it is delivered to the server via the network.

[0720] Step 2:

[0721] The server uses an information processing device to temporarily store the received preference information. The input here is the user's preference information, which is stored in the server's memory. The data is then structured and converted into a format for analysis.

[0722] Step 3:

[0723] The server's analysis system uses an AI model to analyze the received preference information. The input is preference patterns, and the output is the identification of user-specific preference patterns. The AI ​​model references past databases and the preferences of similar users, and extracts trends using data mining techniques.

[0724] Step 4:

[0725] The server selection device selects the optimal beverage that matches the user's preferences based on the analysis results. The input is the analyzed preference pattern, and the output is a list of beverages extracted from the database. Here, filtering and ranking are performed based on the selection criteria.

[0726] Step 5:

[0727] The server's generation device creates a recommendation list based on the selected beverages. This step compiles the beverage details, price, reviews, and pairing suggestions into a list. The input is the selected beverage information, and the output is the recommendation list presented to the user. The generated list is laid out in a visually appealing format.

[0728] Step 6:

[0729] The user reviews a list of recommendations provided on the terminal and selects their desired beverage. The input is the recommendation list, and the output is the user's selection information. Here, the interface operation when selecting from the list is important, as the selection is accurately recorded.

[0730] Step 7:

[0731] The terminal receives user selections and sends order instructions to the server. Input is user selection information, and output is order information sent to the server. The information is organized into necessary variables and reformatted to ensure accurate data delivery to the server.

[0732] Step 8:

[0733] The server uses an ordering device to place an order for the selected beverage with the vendor. At this stage, communication takes place with the vendor using a communication method. Input is order information, and output is order confirmation. The process includes inventory check and delivery date setting.

[0734] (Application Example 1)

[0735] 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".

[0736] There is a need to improve user satisfaction by providing beverage selection based on preferences and a smooth purchasing process. However, existing systems have not been smooth in the entire process from inputting preference information to beverage selection and purchase, and there have been problems such as the extra hassle involved in payment in particular. Furthermore, the suggestion of related food products and smooth communication with suppliers are also important requirements, but these are also issues that have not been adequately resolved.

[0737] 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.

[0738] In this invention, the server includes data processing means for inputting user preference information, information analysis means for analyzing and patternizing the preference information, and payment processing means for performing electronic payment for the selected beverage. This allows the user to consistently perform everything from selecting a beverage that suits their preferences to making an electronic payment.

[0739] "Data processing means" refers to means that receive preference information entered by the user and process that information to store and manage it in an appropriate format.

[0740] "Information analysis means" refers to means used to analyze input preference information and identify the user's preference patterns by comparing it with past data.

[0741] "Selection method" refers to a method for selecting the most suitable beverage from a database based on user preference patterns obtained through analysis.

[0742] "Information generation means" refers to means for creating a recommendation list that includes detailed information about the selected beverages.

[0743] "Information processing means" refers to the means for placing necessary orders based on the generated recommendation list.

[0744] "Payment processing means" refers to the means for smoothly facilitating electronic payments for selected beverages.

[0745] An "information suggestion device" is a means of suggesting food products related to a selected beverage.

[0746] "Information exchange means" refers to the means of exchanging information and communicating with suppliers regarding beverages for which an order has been placed.

[0747] The system that realizes this invention recommends appropriate beverages based on preference information entered by the user via a terminal such as a smartphone, and further enables electronic payment for the selected beverage. The specific operation of this system is described below.

[0748] The server receives preference information sent from the user through a smartphone application. The server is provided through an application built with a mobile app development framework such as React Native. The user inputs information about their preferences (e.g., prefers acidic coffee), and this information is sent to the server.

[0749] The server processes data using Node.js and stores the information in a MongoDB database. This data analysis system compares the user's preferences with past data and other users' preference patterns to select the most suitable beverage for them. The selected beverages are then generated as a recommendation list by the information generation system and displayed on the user's terminal along with detailed information and related reviews.

[0750] Furthermore, for beverages selected by the user, electronic payment is immediately processed using payment methods such as the Stripe API. In addition, related food products are suggested through an information suggestion device, and smooth information management is facilitated with suppliers through information exchange means.

[0751] As a concrete example, a user traveling with a friend might use a smartphone app to be recommended a special seasonal coffee only available locally, and complete the payment on the spot, making the trip even more enjoyable. The prompt for this application states, "Generate a scenario in which the user preference-based beverage selection system recommends a limited-edition beverage at the travel destination."

[0752] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0753] Step 1:

[0754] The user inputs preference information using a device. This information is received through a smartphone interface, and specific preference data, such as "I like acidic coffee," is generated. This data is then sent to the server as input.

[0755] Step 2:

[0756] The server processes the received preference information using Node.js and stores it in a MongoDB database for analysis. This stored preference data is used as input for data analysis. The information analysis tool compares this data with historical data and data from other users to extract the user's preference patterns. This process outputs analysis results that are useful for the user's beverage selection.

[0757] Step 3:

[0758] Based on the analyzed preference patterns, the server uses selection methods to identify relevant beverages from the database. This process selects the beverage that best matches the user's preferences as a candidate. The selected beverage information is output and used as input for the next step.

[0759] Step 4:

[0760] The server generates a recommendation list using an information generation mechanism. This list includes detailed information, prices, reviews, and related suggestions for the selected beverages. The generated list is sent to the user's terminal and presented to the user as output.

[0761] Step 5:

[0762] The user selects a beverage from a recommendation list, and that information is sent to the server. The selected beverage information is then used as input to proceed to the payment processing flow.

[0763] Step 6:

[0764] The server uses payment processing methods such as the Stripe API to execute electronic payments for the selected beverages. Payment information and status are generated and notified to the user's device after the transaction is complete.

[0765] Step 7:

[0766] The server uses an information suggestion device to suggest additional foods related to the selected beverage. The suggested food information is generated and provided to the user as output.

[0767] Step 8:

[0768] The server uses information exchange mechanisms to communicate with suppliers and confirm details about the ordered beverages. The server processes the communication to determine whether or not the beverages can be supplied, and provides necessary feedback to both the supplier and the user as output.

[0769] 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.

[0770] This invention relates to a system that selects beverages by integrating user preference information and emotional information. This system consists of an information processing device, an analysis device, a selection device, a generation device, an ordering device, a communication device, and an emotional engine.

[0771] Users can input preference information through the device. This information includes preferred flavors, previously enjoyed beverages, and ingredients to avoid. The device also incorporates an emotion sensor that detects the user's emotional state in real time from their facial expressions and voice, and transmits this data to the emotion engine.

[0772] The server receives preference and emotional information transmitted from the terminal. The analysis device matches and analyzes the preference information with emotional data from the emotional engine to identify the user's current mood and long-term preference patterns. This analysis forms the basis for highly personalized beverage selection.

[0773] Based on the analysis results, the server's selection device chooses the optimal beverage. Emotional information plays a crucial role in this selection process; for example, it can suggest a specific herbal tea when the user is in a calm mood, or an energy drink when they are seeking vitality.

[0774] The server's generation system creates a recommendation list centered around the selected beverages and also suggests related food items. This list includes detailed information and reviews, as well as selection reasons tailored to the user's emotional state.

[0775] The user reviews the recommendation list via the terminal and selects the beverage that best suits their mood and preferences. After selection, the terminal generates an order instruction and sends it to the server.

[0776] The server's ordering device places orders with sellers according to received instructions. The communication device communicates with sellers to ensure smooth transactions and utilizes a feedback loop, including sentiment information, to improve the accuracy of future recommendations.

[0777] In this way, a system is built that enables highly accurate beverage selection and purchasing experiences that reflect users' preferences and emotions. Users can improve their daily consumption experience by receiving beverage suggestions that suit their individual mood and situation.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] Users input their preferences using a device. This includes their taste preferences, past favorite beverages, and ingredients they want to avoid. Furthermore, an emotion sensor built into the device collects emotional data through the user's facial expressions and voice.

[0781] Step 2:

[0782] The terminal collects preference information and emotional data and sends it to the server. The information processing device temporarily stores this data.

[0783] Step 3:

[0784] The server's analysis system analyzes preference and emotional information received from users. Using machine learning algorithms, it identifies patterns and changes in preferences based on the user's emotional state, and identifies their current mood and future preferences.

[0785] Step 4:

[0786] Based on the analysis results, the server's selection device chooses the optimal beverage. For example, if the user is relaxed, it might select a beverage like chamomile tea. If the user needs energy, it might suggest espresso.

[0787] Step 5:

[0788] The server's generation system creates a recommendation list centered around the selected beverages. The list includes each beverage's flavor, price, past reviews, and selection criteria based on user sentiment. It also includes suggested food pairings (e.g., whether it pairs well with a particular chocolate).

[0789] Step 6:

[0790] The user reviews the recommendation list on the device. Considering the provided information and their own emotional state, they select the most suitable beverage. Once the selection is complete, the device automatically generates an order instruction.

[0791] Step 7:

[0792] The order instructions generated by the terminal are sent to the ordering device on the server. Based on these instructions, the ordering device sends the order to the seller and makes any necessary adjustments.

[0793] Step 8:

[0794] The server's communication equipment works in conjunction with sellers to manage order processing and ensure smooth operation. Simultaneously, incorporating transaction feedback into sentiment analysis improves the accuracy of future selections.

[0795] Through these steps, it becomes possible to provide a highly accurate beverage selection and purchasing experience that reflects the user's emotions and preferences.

[0796] (Example 2)

[0797] 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".

[0798] In modern society, it is important to select products that meet the preferences and emotions of users, but conventional systems have difficulty in adequately reflecting the diverse needs and emotional states of users. Furthermore, there is a need to develop a commercial transaction system that allows users to purchase selected products quickly and easily.

[0799] 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.

[0800] In this invention, the server includes a user interface means for inputting user preference data, a data analysis means for analyzing the preference data and emotional data to identify the user's mood and long-term preference patterns, and a selection means using an algorithm for selecting beverages based on the analysis results. This makes it possible to select products that reflect the user's preferences and emotions.

[0801] "User interface means" refers to devices or software used by users to input preference data into a system, and includes input devices such as touchscreens and keyboards.

[0802] "Data analysis means" refers to a device or process that receives user preference data and emotional data, analyzes them, and identifies the user's mood and long-term preference patterns.

[0803] "Selection method" refers to an algorithm or process for selecting a beverage suitable for the user based on the analysis results obtained from data analysis methods.

[0804] "Data generation means" refers to a device or program that has the function of creating a recommendation list based on information about selected beverages.

[0805] A "means of commercial transaction" refers to a device or system that has the function of facilitating the order process based on a generated list of recommendations and conducting transactions with the seller.

[0806] A "data suggestion means" refers to a device or algorithm that has the function of suggesting food products related to a selected beverage.

[0807] "Data communication means" refers to a device or protocol used to communicate and exchange information with the seller regarding the ordered goods.

[0808] This invention is a system that selects beverages based on the user's preferences and emotions, and mainly consists of a server, a terminal, and a user. Specific embodiments for implementing this invention are described below.

[0809] The server uses SciKit-Learn, a Python library, as its data analysis tool. Preference and sentiment data are analyzed through algorithms to identify the user's mood and long-term preference patterns. Based on these analysis results, a generative AI model is used to select the most suitable beverage for the user. The beverage selection process uses the prompt "Please suggest the best beverage for this mood."

[0810] The terminal is equipped with a touchscreen and emotion sensor as user interface means, and acquires preference data and real-time emotion data from the user. This data is encrypted and sent to a server for analysis. The terminal also presents the user with a list of recommendations generated by a data generation means and proceeds with the ordering process for the selected beverage.

[0811] Users specify their preferences in detail through the system, or receive beverage suggestions based on emotional information extracted by an emotion sensor. For example, if a user feels like "I want to relax today," the system recommends chamomile tea. Once the user selects a beverage, the terminal uses data communication to send the order information to a server for commercial transactions, ensuring smooth communication with the seller.

[0812] This allows users to receive appropriate product suggestions tailored to their preferences and emotions, enabling them to complete purchases effectively and quickly. This system is implemented using specific software such as Python, SciKit-Learn, and generative AI models.

[0813] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0814] Step 1:

[0815] Users input preference data through the device. This input includes preferred flavors, previously selected beverages, and ingredients they wish to avoid. An emotion sensor built into the device acquires the user's emotion data in real time, detecting their current emotional state from their facial expressions and voice. This input data is temporarily stored in the device's local storage.

[0816] Step 2:

[0817] The terminal sends the acquired preference and emotion data to the server. This transmission is secure using the SSL encryption protocol. The server stores the received data in an analysis database. The output of this step is the analysis dataset on the server.

[0818] Step 3:

[0819] The server's data analysis method uses the SciKit-Learn library to analyze received preference and sentiment data. Clustering and classification algorithms are used in the data processing to identify the user's mood and long-term preference patterns. The analysis results are output as recommendation data.

[0820] Step 4:

[0821] The server selection method involves using a generative AI model based on the analysis results to select the optimal beverage according to the prompt message "Please suggest the best beverage for this mood." The generative AI model generates a list of beverages, which the server then formats into a detailed recommendation list. This list is then output.

[0822] Step 5:

[0823] The server's data generation mechanism sends a formatted recommendation list to the terminal. This list also includes food suggestions related to the selected beverage. The terminal displays the received recommendation list in its user interface and presents the suggestions to the user.

[0824] Step 6:

[0825] The user reviews the recommendation list displayed on the terminal and selects their favorite beverage. This selection is entered, and the terminal generates an order instruction. The order instruction is sent to the server, and the commercial transaction process begins.

[0826] Step 7:

[0827] The server's transaction mechanism communicates with the seller based on the order instructions. This communication uses a RESTful API protocol to ensure that orders are processed accurately. A confirmation message from the seller is returned to the server, and the user is notified of the final completion of the transaction.

[0828] This allows users to experience the selection of the optimal beverage based on their preferences and emotions, along with a smooth purchasing process.

[0829] (Application Example 2)

[0830] 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".

[0831] Conventional beverage selection systems typically based their recommendations on user preferences, but they failed to consider the user's emotional state, resulting in inadequate recommendations for specific emotional conditions. Therefore, the challenge lies in providing more personalized beverage selections tailored to individual users, along with the associated consumption experience.

[0832] 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.

[0833] In this invention, the server includes information processing means for inputting user preference information, analysis means for analyzing and patternizing the preference information, and selection means for selecting the optimal beverage based on the pattern and the user's emotional information. This enables more personalized beverage selection that reflects the user's emotional state.

[0834] "Information processing means" refers to a device or program for inputting and managing user preference information.

[0835] "Analysis means" refers to a device or program that has the function of analyzing input preference information and extracting specific preference patterns.

[0836] "Selection means" refers to a device or program for determining the optimal beverage based on analyzed patterns and user emotional information.

[0837] "Generation means" refers to a device or program created to provide users with a list of selected beverages.

[0838] "Ordering method" refers to a device or program for arranging necessary beverages based on the generated recommendation list.

[0839] An "automatic preparation means" is a device or program for preparing or serving ordered beverages in a manner that suits the user's emotional state.

[0840] "Suggestion means" refers to a device or program for suggesting additional ingredients or food products related to the selected beverage.

[0841] "Communication means" refers to a device or program that facilitates the exchange of information with the supplier.

[0842] In order to implement this invention, it is necessary to coordinate various information processing devices, analysis devices, selection devices, generation devices, ordering devices, automatic preparation devices, proposal devices, and communication devices.

[0843] First, the terminal acquires preference information from the user. In this process, the user inputs information such as their preferences, past drinking history, and ingredients they wish to avoid, through a smart device or interface. This information is then aggregated by an information processing system on the server.

[0844] Next, the server uses analysis tools to analyze the user's emotional information, which is collected separately from the acquired preference information. This analysis uses existing emotion analysis AI modules on data collected through facial recognition cameras and voice analysis microphones. Microsoft Azure's emotion recognition API is one example. The analyzed data is used to determine the user's current emotional state.

[0845] Next, based on these analysis results, a selection process is initiated to determine the optimal beverage. This selection process comprehensively considers preference and emotional information to select the most appropriate beverage for the user.

[0846] Furthermore, the system generates a list of recommended beverages selected by the generation method, and this list includes detailed information, reviews, and reasons for selection. Based on this list of recommendations, users select the beverage that interests them most.

[0847] After selection, the ordering mechanism functions to reserve the selected beverage and place an order with the vending system. The automated preparation mechanism adjusts and serves the selected beverage appropriately according to the user's mood.

[0848] Furthermore, by using the suggestion method, related ingredients that pair well with the selected beverage can also be suggested, resulting in a richer consumer experience.

[0849] Finally, we will use communication methods to facilitate smooth information exchange with suppliers. The feedback obtained during this process will be used to improve the accuracy of future proposals.

[0850] As a concrete example, an analysis of a user's work-related fatigue results in the selection of herbal tea as a relaxing beverage, and preparations for serving it are made based on the emotional analysis. An example of a prompt sentence to be input into the generating AI model is, "Evaluate the stress level from the facial expressions and voice detected by the camera, and explain the reason for selecting a relaxing beverage."

[0851] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0852] Step 1:

[0853] The terminal collects preference information from the user. It obtains data such as preferred tastes, previously enjoyed beverages, and ingredients to avoid, which the user enters through their smart device. At this stage, the terminal receives the information using an input interface and sends it to the server. This information functions as a dataset necessary for subsequent selections.

[0854] Step 2:

[0855] The server receives preference information and emotional data acquired in real time from the terminal. Emotional data is collected from the user's facial expressions and voice tone using the facial recognition camera and voice analysis microphone built into the terminal. The received data is input into an emotional analysis AI module to determine the user's current emotional state. This analysis process outputs emotional state data.

[0856] Step 3:

[0857] Based on the analysis results, the server selects the most suitable beverage using a selection method. Referring to the analyzed preference patterns and emotional state, for example, if the user is seeking calmness, it selects herbal tea. Here, the input preference information and emotional information are matched by an algorithm, and as a result, the most suitable beverage for the user is output.

[0858] Step 4:

[0859] The server generates a recommendation list based on the selected beverage. This list includes detailed information, reviews, and selection reasons tailored to the user's emotional state. The generator compiles this information and outputs it as a list to the terminal. This allows the user to view detailed information and assists in making purchasing decisions.

[0860] Step 5:

[0861] The user selects a beverage from a list of recommendations and sends an order to the server via the terminal. The information of the selected beverage is entered into the ordering system, and an order is placed with the automated vending system. At this stage, order details are output in preparation for the actual procurement of the beverage, and the process proceeds to the next stage.

[0862] Step 6:

[0863] The automated preparation system activates, cooking or serving the selected beverage in a manner tailored to the user's emotional state. Information regarding the beverage preparation is input, and settings such as extraction time and temperature are adjusted to maximize relaxation. This completes the optimal serving experience for the user.

[0864] Step 7:

[0865] The server's communication system exchanges information with suppliers regarding the selected beverage. It receives feedback and delivery information from suppliers and updates the database to improve future suggestions and order accuracy. This process enhances overall system efficiency and user experience.

[0866] Throughout these steps, a highly personalized beverage delivery system is achieved, based on the user's emotional state and preferences.

[0867] 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.

[0868] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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."

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] The following is further disclosed regarding the embodiments described above.

[0889] (Claim 1)

[0890] An information processing device for inputting user preference information,

[0891] An analysis device that analyzes and patterns the preference information,

[0892] A selection device that selects the optimal beverage based on the pattern,

[0893] A generating device that generates a list of recommended beverages selected,

[0894] A system including an ordering device that places orders based on the recommended list.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising a suggestion device for suggesting food products related to the selected beverage.

[0897] (Claim 3)

[0898] The system according to claim 1, further comprising a communication device for communicating with a seller regarding the beverage for which the order was placed.

[0899] "Example 1"

[0900] (Claim 1)

[0901] A terminal device for inputting user preference information,

[0902] An analytical means for analyzing the preference information and identifying patterns using past data,

[0903] A selection method for selecting the optimal beverage based on the analyzed pattern,

[0904] A generation means for generating a recommendation list that includes detailed information and pairing suggestions for the selected beverages,

[0905] An ordering method for ordering beverages based on the recommendation list,

[0906] A selection method for accepting beverage choices from users,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, further comprising a means for proposing food products related to the selected beverage.

[0910] (Claim 3)

[0911] The system according to claim 1, further comprising a means for exchanging information with a sales business operator regarding the beverage for which the order was placed.

[0912] "Application Example 1"

[0913] (Claim 1)

[0914] A data processing means for inputting user preference information,

[0915] Information analysis means for analyzing and patternizing the preference information,

[0916] A selection means for selecting the optimal beverage based on the pattern,

[0917] Information generation means for generating a list of recommended beverages selected,

[0918] Information processing means for placing orders based on the recommendation list,

[0919] A system including payment processing means for performing electronic payment for the selected beverage.

[0920] (Claim 2)

[0921] The system according to claim 1, further comprising an information suggestion device that suggests food products related to the selected beverage.

[0922] (Claim 3)

[0923] The system according to claim 1, further comprising means for exchanging information with a supplier regarding the beverage for which the order has been placed.

[0924] "Example 2 of combining an emotion engine"

[0925] (Claim 1)

[0926] A user interface means for inputting user preference data,

[0927] A data analysis means that analyzes the preference data and emotional data to identify the user's mood and long-term preference patterns,

[0928] A selection means using an algorithm for selecting beverages based on the analysis results,

[0929] A data generation means for generating a list of recommendations including the selected beverages,

[0930] A system including commercial transaction means for generating orders based on the recommendation list.

[0931] (Claim 2)

[0932] The system according to claim 1, further comprising a data suggestion means for suggesting food products related to the selected beverage.

[0933] (Claim 3)

[0934] The system according to claim 1, further comprising data communication means for performing communication with a seller regarding the goods for which an order has been placed.

[0935] "Application example 2 when combining with an emotional engine"

[0936] (Claim 1)

[0937] Information processing means for inputting user preference information,

[0938] An analytical means for analyzing and patternizing the preference information,

[0939] A selection means that selects the optimal beverage based on the pattern and the user's emotional information,

[0940] A generation means for generating a list of recommendations for the selected beverages,

[0941] An ordering method for placing orders based on the recommended list,

[0942] A system including an automated preparation means for preparing the ordered beverage according to the user's emotional state.

[0943] (Claim 2)

[0944] The system according to claim 1, further comprising a means for suggesting ingredients related to the selected beverage.

[0945] (Claim 3)

[0946] The system according to claim 1, further comprising a means for exchanging information with a supplier regarding the beverage for which the order has been placed. [Explanation of symbols]

[0947] 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 information processing device for inputting user preference information, An analysis device that analyzes and patterns the preference information, A selection device that selects the optimal beverage based on the pattern, A generating device that generates a list of recommended beverages selected, A system including an ordering device that places orders based on the recommended list.

2. The system according to claim 1, further comprising a suggestion device for suggesting food products related to the selected beverage.

3. The system according to claim 1, further comprising a communication device for communicating with a seller regarding the beverage for which the order has been placed.

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