system

Abstract

We provide the system. [Solution] A device for inputting user preference information and recording said preference information, A device for selecting suitable food based on the aforementioned preference information, In order to collect supplementary information regarding the selected food, a device that works in conjunction with other devices is used. A device that presents the selected food and supplementary information to the user, A device for completing the procedure for purchasing the aforementioned food, A device that reflects the user's past history and presents food and beverage combinations, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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] Since the various preferences and needs of individual users for food and drink change daily, it is difficult to select and satisfy the optimal products accordingly. Also, in many cases, sufficient information cannot be obtained when pairing different genres of food and drink. Due to these problems, it is not only difficult to find food and drink that match the user's preferences, but it has also become difficult to propose an optimal combination.

Means for Solving the Problems

[0005] This invention provides a system that selects the most suitable food and beverages for a user based on their input preferences. Specifically, it provides a means for recording the user's preferences, selecting multiple suitable food and beverage candidates using a generation AI, and then presenting the optimal candidate. It also includes means for acquiring additional information related to the selected food and beverages, as well as suggested information that would suit other food and beverages, by collaborating with other agents, and providing this information to the user. Such a system can accommodate the diverse preferences of users and achieve a more satisfying selection of food and beverages.

[0006] "User" refers to an entity that receives suggestions and selections of food and beverages through the system.

[0007] "Preference information" refers to information about the types, characteristics, flavors, and other preferences of food and beverages that users like.

[0008] "Food and beverages" refers to all food and beverages that are consumed.

[0009] "Selection" refers to the process of choosing the best option from multiple choices.

[0010] "Generative AI" refers to a method of generating optimal food and beverage options based on user preferences using artificial intelligence technology.

[0011] An "agent" refers to an autonomous software module that collaborates and provides information to achieve a specific objective.

[0012] "Additional information" refers to detailed information related to the selected food and beverages, as well as information related to pairing suggestions.

[0013] "Pairing" refers to combinations of different types of food and drink that complement each other well when consumed together. [Brief explanation of the drawing]

[0014] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which 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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for selecting and providing food and beverages that are suitable for the individual preferences of users. The system consists of user terminals, a central server, and related agents. The operation of the system is described below.

[0036] First, the user launches an application on their device and enters their preferences. This preferences include details such as "I prefer fruity red wines" or "I want a wine that pairs well with cheese." This information is then sent from the device to the server.

[0037] The server updates the user's preference profile based on the received preference information and uses generative AI to generate a list of suitable food and beverage options. In this process, a machine learning algorithm selects the most suitable food and beverage based on the user's past preference history and current preference information.

[0038] Next, the server works with relevant agents to gather information on suitable pairings and additional recommendations for the selected food and beverages. Through collaboration with other agents, it becomes possible to obtain recommendations from a cheese agent for cheeses that pair well with the wine selected by the wine agent.

[0039] The collected information is sent from the server to the terminal and displayed to the user. The displayed content includes a list of selected food and beverages, detailed information about each food and beverage, and recommended pairing information. For example, if the user selects a "fruity red wine," a suggestion for a "Brie cheese" that pairs well with it will also be displayed.

[0040] After the user reviews the presented information and selects the recommended food and beverages, the terminal sends that information to the server, and the purchase process proceeds automatically. This allows users to quickly obtain products that suit their preferences and enhances the enjoyment of food and beverage pairings.

[0041] This system allows companies to provide performance-based recommendations tailored to user preferences, enabling them to efficiently deliver more personalized recommendations. As a result, it can improve the customer experience and promote the sale of food and beverages.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user launches an application on the device. The device reads the user's profile data from local storage and performs the login process.

[0045] Step 2:

[0046] The user inputs their current preferences and desired food and drink. The terminal provides a function to input requests in text format through the user interface, such as "a fruity red wine."

[0047] Step 3:

[0048] The terminal sends the input preference information to the server. The terminal generates the user's preference information as an HTTP request and sends it to the server.

[0049] Step 4:

[0050] The server analyzes the received preference information and updates the user's preference profile. The server accesses the database and updates the profile data based on the received information.

[0051] Step 5:

[0052] The server uses AI generation to create food and drink suggestions that match the user's preferences. The server applies machine learning algorithms to create multiple candidate lists.

[0053] Step 6:

[0054] The server works with relevant agents to collect additional information. Based on the information obtained from the wine agent, the server accesses the cheese agent to retrieve suitable pairing information.

[0055] Step 7:

[0056] The server sends the results obtained to the terminal. The server generates a response summarizing the selected food and beverages, their detailed information, and pairing suggestions, and sends it to the terminal.

[0057] Step 8:

[0058] The terminal displays information received from the server to the user. Through the user interface, the terminal provides the user with a list of details about the selected wine and suggested cheeses.

[0059] Step 9:

[0060] The user selects the product they wish to purchase from the displayed options. The user then clicks the purchase button to confirm their selection.

[0061] Step 10:

[0062] The device sends purchase information to the server and initiates the purchase process. The device sends a purchase request to the server and performs the process, including payment processing.

[0063] (Example 1)

[0064] 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."

[0065] Modern consumers have extremely diverse tastes and seek consumption experiences tailored to their individual needs. However, traditional methods have made it difficult to quickly suggest food and beverages that accurately match individual consumer preferences, and to efficiently collect and present relevant information. As a result, the process of selecting and purchasing suitable products is time-consuming, and it is difficult to experience highly satisfying pairings.

[0066] 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.

[0067] In this invention, the server includes means for generating food and beverage candidates using a generative AI model based on preference information, means for collecting information related to the selected food and beverages by communicating with other information providers, and a device for providing the collected information to the user. As a result, the user can receive a rapid presentation of food and beverages suited to their individual preferences and obtain comprehensive recommendations that include related information. This streamlines the product selection and purchase process and enables a personalized and highly satisfying consumer experience.

[0068] A "user" is someone who uses this system to input their preferences and to select and purchase food and beverages.

[0069] "Preference information" refers to information about a user's individual preferences and tastes, and is data used to select specific food and beverage products.

[0070] A "generative AI model" is an artificial intelligence model designed to generate new suggestions and solutions based on large amounts of data.

[0071] A "prompt message" is a sentence input to a generative AI model, serving as an instruction to obtain the information or solution desired by the user.

[0072] An "information provider" is an external system or agent that provides additional information about food and beverages and related pairings.

[0073] "Transaction procedures" refer to the series of processes involved in the purchase of food and beverages selected by the user, including everything from payment to delivery arrangements.

[0074] "Pairing" is the process of recommending other related food and beverages to complement the selected food and beverage, with the aim of providing users with a more fulfilling consumption experience.

[0075] The description of the embodiments for carrying out the invention is as follows:

[0076] This system aims to provide users with food and beverages optimized for their needs and utilizes terminals, servers, and related agents. The system's configuration consists of bidirectional communication between the server and the user's terminal, and data exchange with multiple agents.

[0077] Users first input their preferences using a dedicated application on their device. This is done via an information input device such as a smartphone or personal computer. This preference information is transmitted to a server via the internet. The server updates its database based on the received preference information and builds the latest user profile.

[0078] Next, the server uses a generative AI model to generate food and beverage candidates that match the user's preferences. In this process, a prompt can be set as follows to instruct the AI ​​model to perform the appropriate action: "Recommend a fruity red wine based on the user's preferences." This allows the system to select the most suitable food and beverage candidates from a large amount of data.

[0079] The server also collaborates with external information providers, acting as agents, to collect complementary information related to the selected food and beverages. Through this process, for example, potential pairings of cheese and other dishes that go well with wine can be obtained. The collected information is then sent back to the terminal and presented to the user visually.

[0080] Users view recommended food and beverages and their details on their device, and if they wish to purchase them, they send that information back to the server from their device. The purchase process is automatically handled by the server, improving the convenience of shopping. This system allows users to quickly select their preferred food and beverages and enjoy them in combination with related information.

[0081] The generative AI models implemented in the system play a role in enriching the customer experience by providing highly accurate recommendations tailored to users with diverse preferences.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user launches an application on their device and enters their preferences. Examples of this information include "I prefer fruity red wine."

[0085] The input information is received by the app and then prepared to be sent to the server. Here, the input is the user's preference information, and the output is the preference information that is ready to be sent to the server.

[0086] Step 2:

[0087] The terminal encrypts the user's input preference information and sends it to the server. This process uses protocols such as TLS to ensure data security.

[0088] The input is preference information stored on the device, and the output is transmitted to the server as encrypted data. The server receives this data and prepares to generate a preference profile.

[0089] Step 3:

[0090] The server updates the database based on the received preference information and creates the user's latest preference profile. This generates a new profile that reflects the individual user's input data.

[0091] The input is preference information received by the server, and the output is an updated user profile. Based on this information, the next step involves using an AI model to generate food and beverage options.

[0092] Step 4:

[0093] The server inputs a prompt message into the generation AI model, instructing it to "generate the optimal food and drink based on the user's preferences." The AI ​​model retrieves relevant information from the database and calculates the best food and drink options.

[0094] The input consists of the user's preference profile and a prompt, while the output is a list of food and drink options selected by the AI. A specific example of the output is a wine name such as "Chardonnay."

[0095] Step 5:

[0096] The server communicates with other information providers, which are agents, to collect relevant information suitable for pairing with candidate food and beverages. For example, it communicates with a wine agent to obtain information such as which cheeses pair well with Chardonnay.

[0097] The input is a selection of food and beverage candidates, and the output is detailed recommendation information, including pairing information. This process yields broader and more comprehensive information.

[0098] Step 6:

[0099] The server sends the collected information back to the terminal and presents it visually to the user. The terminal displays a list of food and beverage options and related information on the user's screen. Here, the selected wines and pairing suggestions are displayed in a list.

[0100] The input consists of food, beverages, and related information received from the server, while the output is the display of information to the user. The user reviews the displayed information and makes a selection.

[0101] Step 7:

[0102] The user selects the food and beverage items they wish to purchase from the displayed options and resends this information to the server via their device. The device receives the user's selection and initiates the purchase process.

[0103] The input is the user's selection information, and the output is the data resent to the server. The server automatically processes the payment based on this information, and the purchase is completed.

[0104] (Application Example 1)

[0105] 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."

[0106] There is a need to improve user satisfaction by efficiently providing optimal food and beverage recommendations tailored to individual user preferences, along with pairing information. However, the current system has the challenge of not being able to fully utilize users' past history and thus not being able to provide completely personalized recommendations. Furthermore, improvements are needed in terms of speeding up the purchase process and providing sufficient pairing information.

[0107] 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.

[0108] In this invention, the server includes means for selecting suitable food based on the user's preference information, means for collecting supplementary information about the selected food in cooperation with other devices, and means for presenting food and beverage combinations that reflect the user's past history. This makes it possible to provide users with personalized and optimal food and beverage suggestions and pairing information, thereby increasing user satisfaction along with a quick purchase process.

[0109] A "user" refers to an individual who uses this system to select food and drinks that suit their preferences.

[0110] "Preference information" refers to information about food and beverages based on the user's preferences and past experiences.

[0111] A "recording device" refers to equipment or software that stores user preference information and makes it available for later use.

[0112] "Suitable foods" refer to foods selected based on the user's preferences.

[0113] "Supplemental information" refers to additional information related to the selected food, including information on how it can be paired with other foods and beverages.

[0114] A "device that interacts with other devices" refers to a device that exchanges information with different devices or systems and works together to achieve its functions.

[0115] "History" refers to a record of food and drink choices and preferences that a user has made in the past.

[0116] A "combination suggestion device" refers to a device or software that displays information to the user about selected food items and suitable beverages or other foods.

[0117] "Purchase procedure" refers to a series of actions taken by a user to purchase food and beverages they have selected.

[0118] This invention is a system that suggests optimal food and beverages based on the user's preference information. The system is mainly composed of user terminals and a central server.

[0119] The server receives and records preference information entered by the user through their terminal. This preference information includes the user's preferences and past selection history. The recorded information is analyzed using a generative AI model to select the most suitable food and beverage combination for the user. In this process, the AI ​​learns the user's patterns and improves the accuracy of its suggestions.

[0120] The user's device displays the selected food item and supplementary information about beverages that pair well with it. This supplementary information is obtained by the server communicating with other agents. For example, if a wine is selected, the wine agent will recommend a suitable cheese to complement it. The information provided in this way is detailed and personalized.

[0121] Based on this information, users can select food and beverages and complete the purchase process. The purchase process is completed quickly on the terminal, and the selected items are delivered to the user.

[0122] This system allows users to enjoy a dining experience perfectly tailored to their preferences. For example, if a user prefers spicy food, the system can suggest appropriate beverages to complement it. An example of a prompt supporting this process would be: "The user prefers spicy food. Please suggest a meal including optimal beverage pairings, along with their past preferences."

[0123] Thus, the invention aims to improve user satisfaction by providing food and beverages tailored to the individual preferences of the users.

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The terminal inputs the user's preference information. This preference information includes favorite foods and past dining history. The input information is sent from the terminal to the server. At this point, the input is the user's preference data, and the output is the transmission of data to the server.

[0127] Step 2:

[0128] The server records the received preference information and analyzes it using a generative AI model. The input is preference information received from the user, and based on this information, it generates output that selects suitable food and beverages by referring to a database. Machine learning algorithms are used for data analysis, and the selection accuracy is improved by learning patterns.

[0129] Step 3:

[0130] The server generates selected food and drink combinations and sends this information to other agents. It then obtains supplementary information from other agents to complete the combinations that best match the selected foods. The input for this step is a list of AI-selected food and drink combinations, and the output is the optimal food and drink combinations, including supplementary information. This process involves specific actions to collect recommendation information from other agents.

[0131] Step 4:

[0132] The terminal, based on information received from the server, presents the user with specific food and beverage recommendations. These recommendations include detailed information about the food and beverages, including pairing suggestions. The input is food and beverage recommendations from the server, and the output is the information presented to the user. At this stage, the user reviews and selects products that suit their preferences.

[0133] Step 5:

[0134] The terminal initiates the purchase process for the food and beverage items selected by the user. Purchase-related data is sent to the server, automating the process. Input is the user's selection, and output is confirmation of the purchase completion. Specifically, this includes connecting to the payment system and checking inventory.

[0135] 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.

[0136] This invention is a system that selects and provides appropriate food and beverages based on the user's individual preferences and emotional state. This system consists of a user terminal, a central server, an emotion engine, and related agents. The system's operation is described below.

[0137] First, the user launches an application installed on the device. The device is equipped with a camera, and the user's facial expressions are analyzed by an emotion engine. In this process, the user's current emotional state is identified, and a variety of emotions such as joy, sadness, and surprise can be detected.

[0138] In addition to their emotional state, users input their preferences for food and beverages through the terminal's interface. The terminal then transmits this information to the server.

[0139] The server updates the user profile based on the received preference and emotional state information, and uses generative AI to generate appropriate food and beverage options. In this process, options tailored to the consumer's emotions are listed and selected.

[0140] Next, the server collaborates with relevant agents to obtain pairing information and additional information suitable for the selected food and beverage. This collaboration allows, for example, a cheese agent to suggest the most suitable cheese based on information obtained from a wine agent.

[0141] The information collected in this way is sent from the server to the terminal and presented to the user. The user reviews the selected food and beverages and detailed information on their pairings in a list format and decides on their purchase based on that information.

[0142] Ultimately, the process of purchasing the food and beverages selected by the user is completed via the terminal. For example, if the user selects "a red wine that suits their preferences," a pairing of "blue cheese" that complements it will also be suggested.

[0143] This system improves user satisfaction and streamlines the product selection process by providing appropriate food and beverage suggestions based on the user's emotional changes. As a result, it can enrich the customer experience and increase their willingness to purchase.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user launches an application on the device. The device will display a login prompt and provide the user with the ability to enter their authentication information.

[0147] Step 2:

[0148] The device's built-in camera and emotion engine work together to capture the user's facial expressions. The device sends the captured image to the emotion engine, which then analyzes the user's emotional state. For example, if the user is smiling, it is recognized as "joy."

[0149] Step 3:

[0150] The user inputs their preferences using the application's interface. For example, they might enter specific requests as text, such as "I'm looking for a drink that pairs well with spicy food."

[0151] Step 4:

[0152] The terminal combines preference information entered by the user with emotional state information generated by the emotion engine, and sends this data to the server.

[0153] Step 5:

[0154] The server analyzes the received data and generates optimized food and drink suggestions based on the user's preference profile and emotional state. The server uses a generation AI to create a list of suggestions, taking into account past preference history and current state.

[0155] Step 6:

[0156] The server collaborates with relevant agents to obtain pairing information and additional information that matches the selected food and beverage. For example, it might use a wine agent to obtain information on cheeses that pair well with the selected wine from a cheese agent.

[0157] Step 7:

[0158] The server sends the selected food and beverage pairing information to the terminal. The data sent includes the product name, detailed description, and emotionally tailored recommendation reasons.

[0159] Step 8:

[0160] The terminal displays the information it receives to the user. The terminal interface is configured to allow the user to easily review the selection results and make a purchase decision.

[0161] Step 9:

[0162] The user selects the product they want to purchase from the presented options. The user reviews the list, checks the details of the selected product, and then clicks the purchase button.

[0163] Step 10:

[0164] The terminal sends the selected purchase information to the server and initiates the purchase process. The server verifies the payment information and processes the settlement, completing the transaction.

[0165] (Example 2)

[0166] 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."

[0167] Conventional food and beverage selection systems tend to make suggestions based solely on user preference information, making it difficult to make optimal selections that take into account the emotional state of individual users. Furthermore, a lack of proper coordination with related agents made it difficult to provide users with a comprehensive dining experience.

[0168] 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.

[0169] In this invention, the server includes means for analyzing the user's emotional state, means for generating multiple food and beverage candidates using generative AI, and means for collecting additional information in cooperation with related agents. This makes it possible to suggest more suitable food and beverages by combining the user's emotional state and preference information.

[0170] "User" refers to an individual or group that uses this system to perform operations or input data.

[0171] "Emotional state" refers to information that represents the type and changes in the user's emotions, and is mainly obtained through facial expression analysis.

[0172] "Preference information" refers to data that represents the types and characteristics of food and beverages that users prefer, and is based on user input.

[0173] "Generative AI" refers to algorithms and models that utilize artificial intelligence technology to generate food and beverage options based on user preferences and emotional states.

[0174] "Related agents" refer to external systems or services that collaborate to provide additional information related to the selected food and beverage items.

[0175] "Additional information" refers to information that is useful when pairing selected food and beverages, and includes pairing data and related product information.

[0176] This invention is a system that suggests individually tailored food and beverages based on the user's emotional state and preference information. The system mainly consists of the user's terminal, a central server, an emotion analysis engine, and related agents. The operation of each element is described below.

[0177] The user first launches an application installed on the device. The device has a camera, which is used to capture the user's facial expressions. During this process, the device uses an emotion analysis engine to identify the user's emotional state from their facial expressions. For example, it uses Google's (registered trademark) facial recognition API to analyze emotions such as joy and surprise and generates the results.

[0178] Users input their preferences, such as their favorite types of food and drinks, through the terminal's interface. The terminal sends this information to a server, which updates the user profile based on it. This profile update also includes collected emotional state information.

[0179] The server uses a generative AI model to generate appropriate food and beverage options based on an updated profile that includes the user's emotional state and preference information. During this process, the generative AI model is prompted with the message, "Please suggest food and beverages that match the user's emotions," and an optimized list of candidates is compiled.

[0180] Next, the server collaborates with relevant agents to gather optimal pairing information and additional details for the suggested food and beverages. This collaboration allows, for example, the server to receive information on the best cheeses to enjoy with certain types of wine.

[0181] For example, if the analysis results indicate that the user wants to "relax," the server, through its AI-generated content, will suggest chamomile tea and also propose pairing it with herbal cookies. This kind of information is sent from the server to the terminal and presented to the user.

[0182] Ultimately, users can complete the purchase process for food and beverages using their devices. This allows users to have an enjoyable consumption experience, and since the suggested products are optimized for their emotional state, increased satisfaction can be expected.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The user launches the app on the device. The device launches the application based on the user's input and displays the home screen. Here, the user taps the start button to access the system.

[0186] Step 2:

[0187] The device captures the user's facial expressions with its camera and collects that data. The input is the user's facial image, and the output generates image data necessary for emotion analysis.

[0188] Step 3:

[0189] The device analyzes facial expression data using an emotion analysis engine. Image data is provided to the emotion analysis engine as input, and category information of emotions such as joy and surprise is obtained as output. In this process, the analysis model estimates the emotional state.

[0190] Step 4:

[0191] The user inputs preference information through the device's interface. The user selects categories of preferred foods and beverages, and this information is collected as input data. The device temporarily stores this information.

[0192] Step 5:

[0193] The terminal sends emotional state and preference information to the server. The input data sent from the terminal consists of analyzed emotional information and preference information entered by the user. The server receives this data and generates output that updates the user's profile.

[0194] Step 6:

[0195] The server generates food and drink suggestions using a generative AI model. The input is an updated user profile, and the prompt "Please suggest food and drink that matches the user's mood" is sent to the model, resulting in a list of food and drink suggestions as output.

[0196] Step 7:

[0197] The server works in conjunction with relevant agents to collect additional information. Using the selected food and beverage information as input, it queries the agents and receives output such as optimal pairing information and related product information.

[0198] Step 8:

[0199] The server sends the final suggestions to the terminal. The output data from the server to the terminal includes a list of food and beverage options and their associated pairing information.

[0200] Step 9:

[0201] The user reviews the information presented through the device and makes a purchase decision. The user's selection is the final input for the purchase decision, and the device completes the purchase process based on that information.

[0202] (Application Example 2)

[0203] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0204] In modern life, selecting food and beverages that suit individual preferences is important, but conventional systems have the challenge of not being able to suggest food and beverages that take into account the user's emotional state. Furthermore, there is a lack of mechanisms that provide appropriate suggestions based on food and beverage pairing information and emotional state, and there is a need to improve user satisfaction.

[0205] 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.

[0206] In this invention, the server includes means for recording user preference information, means for analyzing image data to determine the user's emotional state, and means for selecting multiple food and beverage candidates using a generation AI based on the emotional state and preference information. This makes it possible to present appropriate food and beverage suggestions and pairing information according to the user's emotions.

[0207] "User preference information" refers to information that indicates users' preferences and selection criteria regarding food and beverages.

[0208] "Food and beverages" is a general term encompassing both drinks and food, referring to anything that users consume.

[0209] An "agent" is a program or database that works together to collect and provide information.

[0210] "Emotional state" refers to the temporary psychological state exhibited by the user and is determined through methods such as facial expression analysis.

[0211] "Image data" refers to visual information acquired by a camera device or similar device.

[0212] "Generative AI" is an artificial intelligence system that uses machine learning techniques to learn patterns from data and make predictions and suggestions.

[0213] "Pairing information" refers to recommended information about other food and beverages that can be enjoyed together with the selected food and beverage.

[0214] The system for realizing this invention provides a function to select and suggest food and beverages based on the user's emotional state and preference information. Specifically, a smartphone or other device uses a camera device to capture the user's face and sends the image data to a server. The server uses the image processing library OpenCV to analyze the user's emotional state from the image data.

[0215] The analysis results and the preference information entered by the user through the interface are stored in the server's database. Based on the stored data, a generative AI model uses TENSORFLOW® to generate optimal food and beverage candidates. This generative AI utilizes machine learning technology to suggest food and beverages that best match the user's emotional state and preferences.

[0216] The suggested food and beverage information, along with pairing information collected through collaboration with other agents, is sent back from the server to the terminal. The terminal presents this information to the user, allowing the user to confirm their selection of food and beverages. This process enables the user to easily order appropriate meals that match their mood, thereby increasing their satisfaction.

[0217] For example, if a user is stressed, the system will suggest a spicy dish. It may also suggest adding mint tea to promote refreshment. A key feature is the use of a generative AI model to suggest appropriate food and drink based on the user's emotions. An example of a prompt would be, "If the user is stressed, suggest a refreshing drink that suits their preferences."

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The device activates its camera to acquire facial expression data from the user and takes a picture of the user's face. This capture process inputs the original image data, which is then prepared for processing.

[0221] Step 2:

[0222] The terminal sends the acquired image data to the server. The server receives this image data as input and analyzes the emotional state using facial recognition technology. Specifically, it uses OpenCV to detect various emotional characteristics and outputs the results as emotional state data.

[0223] Step 3:

[0224] Users input their preferences through the terminal's interface. The terminal aggregates this preference information and sends it to the server. This preference information is then stored directly in a database and used in subsequent suggestion processes.

[0225] Step 4:

[0226] The server generates food and drink candidates using a generative AI model based on the input emotional state data and preference information. In this process, TensorFlow is used to output food and drink candidates that match the emotions and preferences through a machine learning algorithm.

[0227] Step 5:

[0228] The server works in conjunction with other agents to obtain pairing information for the generated food and beverage candidates. This pairing information utilizes results obtained from an external database to output the optimal combination.

[0229] Step 6:

[0230] The server sends the selected food and beverage items and associated pairing information to the terminal. The terminal then presents this information to the user and prompts them to select from the menu. At this time, the suggested items and pairing information are displayed in a list format.

[0231] Step 7:

[0232] The user selects their preferred food and drinks from a displayed list and sends their selections to the server via their device. The server receives these selections as input, prepares for the purchase process, and finally confirms the order.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention is a system for selecting and providing food and beverages that are suitable for the individual preferences of users. The system consists of user terminals, a central server, and related agents. The operation of the system is described below.

[0250] First, the user launches an application on their device and enters their preferences. This preferences include details such as "I prefer fruity red wines" or "I want a wine that pairs well with cheese." This information is then sent from the device to the server.

[0251] The server updates the user's preference profile based on the received preference information and uses generative AI to generate a list of suitable food and beverage options. In this process, a machine learning algorithm selects the most suitable food and beverage based on the user's past preference history and current preference information.

[0252] Next, the server works with relevant agents to gather information on suitable pairings and additional recommendations for the selected food and beverages. Through collaboration with other agents, it becomes possible to obtain recommendations from a cheese agent for cheeses that pair well with the wine selected by the wine agent.

[0253] The collected information is sent from the server to the terminal and displayed to the user. The displayed content includes a list of selected food and beverages, detailed information about each food and beverage, and recommended pairing information. For example, if the user selects a "fruity red wine," a suggestion for a "Brie cheese" that pairs well with it will also be displayed.

[0254] After the user reviews the presented information and selects the recommended food and beverages, the terminal sends that information to the server, and the purchase process proceeds automatically. This allows users to quickly obtain products that suit their preferences and enhances the enjoyment of food and beverage pairings.

[0255] This system allows companies to provide performance-based recommendations tailored to user preferences, enabling them to efficiently deliver more personalized recommendations. As a result, it can improve the customer experience and promote the sale of food and beverages.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The user launches an application on the device. The device reads the user's profile data from local storage and performs the login process.

[0259] Step 2:

[0260] The user inputs their current preferences and desired food and drink. The terminal provides a function to input requests in text format through the user interface, such as "a fruity red wine."

[0261] Step 3:

[0262] The terminal sends the input preference information to the server. The terminal generates the user's preference information as an HTTP request and sends it to the server.

[0263] Step 4:

[0264] The server analyzes the received preference information and updates the user's preference profile. The server accesses the database and updates the profile data based on the received information.

[0265] Step 5:

[0266] The server uses AI generation to create food and drink suggestions that match the user's preferences. The server applies machine learning algorithms to create multiple candidate lists.

[0267] Step 6:

[0268] The server works with relevant agents to collect additional information. Based on the information obtained from the wine agent, the server accesses the cheese agent to retrieve suitable pairing information.

[0269] Step 7:

[0270] The server sends the results obtained to the terminal. The server generates a response summarizing the selected food and beverages, their detailed information, and pairing suggestions, and sends it to the terminal.

[0271] Step 8:

[0272] The terminal displays information received from the server to the user. Through the user interface, the terminal provides the user with a list of details about the selected wine and suggested cheeses.

[0273] Step 9:

[0274] The user selects the product they wish to purchase from the displayed options. The user then clicks the purchase button to confirm their selection.

[0275] Step 10:

[0276] The device sends purchase information to the server and initiates the purchase process. The device sends a purchase request to the server and performs the process, including payment processing.

[0277] (Example 1)

[0278] 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".

[0279] Modern consumers have extremely diverse tastes and seek consumption experiences tailored to their individual needs. However, traditional methods have made it difficult to quickly suggest food and beverages that accurately match individual consumer preferences, and to efficiently collect and present relevant information. As a result, the process of selecting and purchasing suitable products is time-consuming, and it is difficult to experience highly satisfying pairings.

[0280] 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.

[0281] In this invention, the server includes means for generating food and drink candidates using a generative AI model based on preference information, means for communicating with another information provider to collect information related to the selected food and drink, and a device for providing the collected information to the user. As a result, the user can receive a prompt presentation of food and drink suitable for their individual preferences and obtain comprehensive recommendations including relevant information. This streamlines the product selection and purchase process and enables a highly satisfying individualized consumption experience.

[0282] A "user" is an entity that inputs preference information using this system and conducts food and drink selection and purchase procedures.

[0283] "Preference information" is information regarding the individual preferences and tastes of the user and is data used for the selection of specific food and drink.

[0284] A "generative AI model" is an artificial intelligence model for creating new proposals and solutions based on a large amount of data.

[0285] A "prompt sentence" is a sentence input into the generative AI model and serves as an instruction for obtaining the information and solutions desired by the user.

[0286] An "information provider" is an external system or agent that provides additional information regarding food and drink and related pairings.

[0287] A "transaction procedure" is a series of processes related to the purchase of the food and drink selected by the user and includes everything from payment to delivery arrangements.

[0288] "Pairing" is a process of recommending other related food and drink in accordance with the selected food and drink and is for providing the user with a more fulfilling consumption experience.

[0289] The description of the embodiments for implementing the invention is as follows.

[0290] This system aims to provide users with food and beverages optimized for their needs and utilizes terminals, servers, and related agents. The system's configuration consists of bidirectional communication between the server and the user's terminal, and data exchange with multiple agents.

[0291] Users first input their preferences using a dedicated application on their device. This is done via an information input device such as a smartphone or personal computer. This preference information is transmitted to a server via the internet. The server updates its database based on the received preference information and builds the latest user profile.

[0292] Next, the server uses a generative AI model to generate food and beverage candidates that match the user's preferences. In this process, a prompt can be set as follows to instruct the AI ​​model to perform the appropriate action: "Recommend a fruity red wine based on the user's preferences." This allows the system to select the most suitable food and beverage candidates from a large amount of data.

[0293] The server also collaborates with external information providers, acting as agents, to collect complementary information related to the selected food and beverages. Through this process, for example, potential pairings of cheese and other dishes that go well with wine can be obtained. The collected information is then sent back to the terminal and presented to the user visually.

[0294] Users view recommended food and beverages and their details on their device, and if they wish to purchase them, they send that information back to the server from their device. The purchase process is automatically handled by the server, improving the convenience of shopping. This system allows users to quickly select their preferred food and beverages and enjoy them in combination with related information.

[0295] The generative AI models implemented in the system play a role in enriching the customer experience by providing highly accurate recommendations tailored to users with diverse preferences.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The user launches an application on their device and enters their preferences. Examples of this information include "I prefer fruity red wine."

[0299] The input information is received by the app and then prepared to be sent to the server. Here, the input is the user's preference information, and the output is the preference information that is ready to be sent to the server.

[0300] Step 2:

[0301] The terminal encrypts the user's input preference information and sends it to the server. This process uses protocols such as TLS to ensure data security.

[0302] The input is preference information stored on the device, and the output is transmitted to the server as encrypted data. The server receives this data and prepares to generate a preference profile.

[0303] Step 3:

[0304] The server updates the database based on the received preference information and creates the user's latest preference profile. This generates a new profile that reflects the individual user's input data.

[0305] The input is preference information received by the server, and the output is an updated user profile. Based on this information, the next step involves using an AI model to generate food and beverage options.

[0306] Step 4:

[0307] The server inputs the prompt text into the generative AI model and gives an instruction such as "Please generate the optimal food and drink based on the user's preferences". The AI model calls relevant information from the database and calculates the optimal candidates for food and drink.

[0308] The input is the user's preference profile and the prompt text, and the output is a list of food and drink candidates selected by the AI. Specific output examples include wine names such as "Chardonnay".

[0309] Step 5:

[0310] The server communicates with an agent, which is another information provider, and collects relevant information suitable for combination with the candidate food and drink. For example, it communicates with a wine agent to obtain information such as cheese suitable for Chardonnay.

[0311] The input is the selected food and drink candidates, and the output is detailed recommendation information including pairing information. Through this process, more extensive and comprehensive information can be obtained.

[0312] Step 6:

[0313] The server sends back the collected information to the terminal and visually presents it to the user. The terminal displays the list of food and drink candidates and the relevant information on the user's screen. Here, the selected wine and pairing suggestions are listed.

[0314] The input is the food and drink and the relevant information received from the server, and the output is the display of information to the user. The user checks the displayed information and makes a selection.

[0315] Step 7:

[0316] The user selects what they want to purchase from the presented food and drink and resends the information to the server via the terminal. The terminal receives the user's selection and initiates the purchase process.

[0317] The input is the user's selection information, and the output is the data resent to the server. The server automatically processes the payment based on this information, and the purchase is completed.

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0320] There is a need to improve user satisfaction by efficiently providing optimal food and beverage recommendations tailored to individual user preferences, along with pairing information. However, the current system has the challenge of not being able to fully utilize users' past history and thus not being able to provide completely personalized recommendations. Furthermore, improvements are needed in terms of speeding up the purchase process and providing sufficient pairing information.

[0321] 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.

[0322] In this invention, the server includes means for selecting suitable food based on the user's preference information, means for collecting supplementary information about the selected food in cooperation with other devices, and means for presenting food and beverage combinations that reflect the user's past history. This makes it possible to provide users with personalized and optimal food and beverage suggestions and pairing information, thereby increasing user satisfaction along with a quick purchase process.

[0323] A "user" refers to an individual who uses this system to select food and drinks that suit their preferences.

[0324] "Preference information" refers to information about food and beverages based on the user's preferences and past experiences.

[0325] A "recording device" refers to equipment or software that stores user preference information and makes it available for later use.

[0326] "Suitable foods" refer to foods selected based on the user's preferences.

[0327] "Supplemental information" refers to additional information related to the selected food, including information on how it can be paired with other foods and beverages.

[0328] A "device that interacts with other devices" refers to a device that exchanges information with different devices or systems and works together to achieve its functions.

[0329] "History" refers to a record of food and drink choices and preferences that a user has made in the past.

[0330] A "combination suggestion device" refers to a device or software that displays information to the user about selected food items and suitable beverages or other foods.

[0331] "Purchase procedure" refers to a series of actions taken by a user to purchase food and beverages they have selected.

[0332] This invention is a system that suggests optimal food and beverages based on the user's preference information. The system is mainly composed of user terminals and a central server.

[0333] The server receives and records preference information entered by the user through their terminal. This preference information includes the user's preferences and past selection history. The recorded information is analyzed using a generative AI model to select the most suitable food and beverage combination for the user. In this process, the AI ​​learns the user's patterns and improves the accuracy of its suggestions.

[0334] The user's device displays the selected food item and supplementary information about beverages that pair well with it. This supplementary information is obtained by the server communicating with other agents. For example, if a wine is selected, the wine agent will recommend a suitable cheese to complement it. The information provided in this way is detailed and personalized.

[0335] Based on this information, users can select food and beverages and complete the purchase process. The purchase process is completed quickly on the terminal, and the selected items are delivered to the user.

[0336] This system allows users to enjoy a dining experience perfectly tailored to their preferences. For example, if a user prefers spicy food, the system can suggest appropriate beverages to complement it. An example of a prompt supporting this process would be: "The user prefers spicy food. Please suggest a meal including optimal beverage pairings, along with their past preferences."

[0337] Thus, the invention aims to improve user satisfaction by providing food and beverages tailored to the individual preferences of the users.

[0338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0339] Step 1:

[0340] The terminal inputs the user's preference information. This preference information includes favorite foods and past dining history. The input information is sent from the terminal to the server. At this point, the input is the user's preference data, and the output is the transmission of data to the server.

[0341] Step 2:

[0342] The server records the received preference information and analyzes it using a generative AI model. The input is preference information received from the user, and based on this information, it generates output that selects suitable food and beverages by referring to a database. Machine learning algorithms are used for data analysis, and the selection accuracy is improved by learning patterns.

[0343] Step 3:

[0344] The server generates selected food and drink combinations and sends this information to other agents. It then obtains supplementary information from other agents to complete the combinations that best match the selected foods. The input for this step is a list of AI-selected food and drink combinations, and the output is the optimal food and drink combinations, including supplementary information. This process involves specific actions to collect recommendation information from other agents.

[0345] Step 4:

[0346] The terminal, based on information received from the server, presents the user with specific food and beverage recommendations. These recommendations include detailed information about the food and beverages, including pairing suggestions. The input is food and beverage recommendations from the server, and the output is the information presented to the user. At this stage, the user reviews and selects products that suit their preferences.

[0347] Step 5:

[0348] The terminal initiates the purchase process for the food and beverage items selected by the user. Purchase-related data is sent to the server, automating the process. Input is the user's selection, and output is confirmation of the purchase completion. Specifically, this includes connecting to the payment system and checking inventory.

[0349] 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.

[0350] This invention is a system that selects and provides appropriate food and beverages based on the user's individual preferences and emotional state. This system consists of a user terminal, a central server, an emotion engine, and related agents. The system's operation is described below.

[0351] First, the user launches an application installed on the device. The device is equipped with a camera, and the user's facial expressions are analyzed by an emotion engine. In this process, the user's current emotional state is identified, and a variety of emotions such as joy, sadness, and surprise can be detected.

[0352] In addition to their emotional state, users input their preferences for food and beverages through the terminal's interface. The terminal then transmits this information to the server.

[0353] The server updates the user profile based on the received preference and emotional state information, and uses generative AI to generate appropriate food and beverage options. In this process, options tailored to the consumer's emotions are listed and selected.

[0354] Next, the server collaborates with relevant agents to obtain pairing information and additional information suitable for the selected food and beverage. This collaboration allows, for example, a cheese agent to suggest the most suitable cheese based on information obtained from a wine agent.

[0355] The information collected in this way is sent from the server to the terminal and presented to the user. The user reviews the selected food and beverages and detailed information on their pairings in a list format and decides on their purchase based on that information.

[0356] Ultimately, the process of purchasing the food and beverages selected by the user is completed via the terminal. For example, if the user selects "a red wine that suits their preferences," a pairing of "blue cheese" that complements it will also be suggested.

[0357] This system improves user satisfaction and streamlines the product selection process by providing appropriate food and beverage suggestions based on the user's emotional changes. As a result, it can enrich the customer experience and increase their willingness to purchase.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] The user launches an application on the device. The device will display a login prompt and provide the user with the ability to enter their authentication information.

[0361] Step 2:

[0362] The device's built-in camera and emotion engine work together to capture the user's facial expressions. The device sends the captured image to the emotion engine, which then analyzes the user's emotional state. For example, if the user is smiling, it is recognized as "joy."

[0363] Step 3:

[0364] The user inputs their preferences using the application's interface. For example, they might enter specific requests as text, such as "I'm looking for a drink that pairs well with spicy food."

[0365] Step 4:

[0366] The terminal combines preference information entered by the user with emotional state information generated by the emotion engine, and sends this data to the server.

[0367] Step 5:

[0368] The server analyzes the received data and generates optimized food and drink suggestions based on the user's preference profile and emotional state. The server uses a generation AI to create a list of suggestions, taking into account past preference history and current state.

[0369] Step 6:

[0370] The server collaborates with relevant agents to obtain pairing information and additional information that matches the selected food and beverage. For example, it might use a wine agent to obtain information on cheeses that pair well with the selected wine from a cheese agent.

[0371] Step 7:

[0372] The server sends the selected food and beverage pairing information to the terminal. The data sent includes the product name, detailed description, and emotionally tailored recommendation reasons.

[0373] Step 8:

[0374] The terminal displays the information it receives to the user. The terminal interface is configured to allow the user to easily review the selection results and make a purchase decision.

[0375] Step 9:

[0376] The user selects the product they want to purchase from the presented options. The user reviews the list, checks the details of the selected product, and then clicks the purchase button.

[0377] Step 10:

[0378] The terminal sends the selected purchase information to the server and initiates the purchase process. The server verifies the payment information and processes the settlement, completing the transaction.

[0379] (Example 2)

[0380] 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".

[0381] Conventional food and beverage selection systems tend to make suggestions based solely on user preference information, making it difficult to make optimal selections that take into account the emotional state of individual users. Furthermore, a lack of proper coordination with related agents made it difficult to provide users with a comprehensive dining experience.

[0382] 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.

[0383] In this invention, the server includes means for analyzing the user's emotional state, means for generating multiple food and beverage candidates using generative AI, and means for collecting additional information in cooperation with related agents. This makes it possible to suggest more suitable food and beverages by combining the user's emotional state and preference information.

[0384] "User" refers to an individual or group that uses this system to perform operations or input data.

[0385] "Emotional state" refers to information that represents the type and changes in the user's emotions, and is mainly obtained through facial expression analysis.

[0386] "Preference information" refers to data that represents the types and characteristics of food and beverages that users prefer, and is based on user input.

[0387] "Generative AI" refers to algorithms and models that utilize artificial intelligence technology to generate food and beverage options based on user preferences and emotional states.

[0388] "Related agents" refer to external systems or services that collaborate to provide additional information related to the selected food and beverage items.

[0389] "Additional information" refers to information that is useful when pairing selected food and beverages, and includes pairing data and related product information.

[0390] This invention is a system that suggests individually tailored food and beverages based on the user's emotional state and preference information. The system mainly consists of the user's terminal, a central server, an emotion analysis engine, and related agents. The operation of each element is described below.

[0391] The user first launches an application installed on the device. The device has a camera, which is used to capture the user's facial expressions. During this process, the device uses an emotion analysis engine to identify the user's emotional state from their facial expressions. For example, it might use Google's facial recognition API to analyze emotions such as joy or surprise and generate the results.

[0392] Users input their preferences, such as their favorite types of food and drinks, through the terminal's interface. The terminal sends this information to a server, which updates the user profile based on it. This profile update also includes collected emotional state information.

[0393] The server uses a generative AI model to generate appropriate food and beverage options based on an updated profile that includes the user's emotional state and preference information. During this process, the generative AI model is prompted with the message, "Please suggest food and beverages that match the user's emotions," and an optimized list of candidates is compiled.

[0394] Next, the server collaborates with relevant agents to gather optimal pairing information and additional details for the suggested food and beverages. This collaboration allows, for example, the server to receive information on the best cheeses to enjoy with certain types of wine.

[0395] For example, if the analysis results indicate that the user wants to "relax," the server, through its AI-generated content, will suggest chamomile tea and also propose pairing it with herbal cookies. This kind of information is sent from the server to the terminal and presented to the user.

[0396] Ultimately, users can complete the purchase process for food and beverages using their devices. This allows users to have an enjoyable consumption experience, and since the suggested products are optimized for their emotional state, increased satisfaction can be expected.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] The user launches the app on the device. The device launches the application based on the user's input and displays the home screen. Here, the user taps the start button to access the system.

[0400] Step 2:

[0401] The device captures the user's facial expressions with its camera and collects that data. The input is the user's facial image, and the output generates image data necessary for emotion analysis.

[0402] Step 3:

[0403] The device analyzes facial expression data using an emotion analysis engine. Image data is provided to the emotion analysis engine as input, and category information of emotions such as joy and surprise is obtained as output. In this process, the analysis model estimates the emotional state.

[0404] Step 4:

[0405] The user inputs preference information through the device's interface. The user selects categories of preferred foods and beverages, and this information is collected as input data. The device temporarily stores this information.

[0406] Step 5:

[0407] The terminal sends emotional state and preference information to the server. The input data sent from the terminal consists of analyzed emotional information and preference information entered by the user. The server receives this data and generates output that updates the user's profile.

[0408] Step 6:

[0409] The server generates food and drink suggestions using a generative AI model. The input is an updated user profile, and the prompt "Please suggest food and drink that matches the user's mood" is sent to the model, resulting in a list of food and drink suggestions as output.

[0410] Step 7:

[0411] The server works in conjunction with relevant agents to collect additional information. Using the selected food and beverage information as input, it queries the agents and receives output such as optimal pairing information and related product information.

[0412] Step 8:

[0413] The server sends the final suggestions to the terminal. The output data from the server to the terminal includes a list of food and beverage options and their associated pairing information.

[0414] Step 9:

[0415] The user reviews the information presented through the device and makes a purchase decision. The user's selection is the final input for the purchase decision, and the device completes the purchase process based on that information.

[0416] (Application Example 2)

[0417] 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 as the "terminal".

[0418] In modern life, selecting food and beverages that suit individual preferences is important, but conventional systems have the challenge of not being able to suggest food and beverages that take into account the user's emotional state. Furthermore, there is a lack of mechanisms that provide appropriate suggestions based on food and beverage pairing information and emotional state, and there is a need to improve user satisfaction.

[0419] 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.

[0420] In this invention, the server includes means for recording user preference information, means for analyzing image data to determine the user's emotional state, and means for selecting multiple food and beverage candidates using a generation AI based on the emotional state and preference information. This makes it possible to present appropriate food and beverage suggestions and pairing information according to the user's emotions.

[0421] "User preference information" refers to information that indicates users' preferences and selection criteria regarding food and beverages.

[0422] "Food and beverages" is a general term encompassing both drinks and food, referring to anything that users consume.

[0423] An "agent" is a program or database that works together to collect and provide information.

[0424] "Emotional state" refers to the temporary psychological state exhibited by the user and is determined through methods such as facial expression analysis.

[0425] "Image data" refers to visual information acquired by a camera device or similar device.

[0426] "Generative AI" is an artificial intelligence system that uses machine learning techniques to learn patterns from data and make predictions and suggestions.

[0427] "Pairing information" refers to recommended information about other food and beverages that can be enjoyed together with the selected food and beverage.

[0428] The system for realizing this invention provides a function to select and suggest food and beverages based on the user's emotional state and preference information. Specifically, a smartphone or other device uses a camera device to capture the user's face and sends the image data to a server. The server uses the image processing library OpenCV to analyze the user's emotional state from the image data.

[0429] The analysis results and the user's preference information entered through the interface are stored in the server's database. Based on the stored data, a generative AI model uses TensorFlow to generate optimal food and beverage candidates. This generative AI utilizes machine learning techniques to suggest food and beverages that best match the user's emotional state and preferences.

[0430] The suggested food and beverage information, along with pairing information collected through collaboration with other agents, is sent back from the server to the terminal. The terminal presents this information to the user, allowing the user to confirm their selection of food and beverages. This process enables the user to easily order appropriate meals that match their mood, thereby increasing their satisfaction.

[0431] For example, if a user is stressed, the system will suggest a spicy dish. It may also suggest adding mint tea to promote refreshment. A key feature is the use of a generative AI model to suggest appropriate food and drink based on the user's emotions. An example of a prompt would be, "If the user is stressed, suggest a refreshing drink that suits their preferences."

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The device activates its camera to acquire facial expression data from the user and takes a picture of the user's face. This capture process inputs the original image data, which is then prepared for processing.

[0435] Step 2:

[0436] The terminal sends the acquired image data to the server. The server receives this image data as input and analyzes the emotional state using facial recognition technology. Specifically, it uses OpenCV to detect various emotional characteristics and outputs the results as emotional state data.

[0437] Step 3:

[0438] Users input their preferences through the terminal's interface. The terminal aggregates this preference information and sends it to the server. This preference information is then stored directly in a database and used in subsequent suggestion processes.

[0439] Step 4:

[0440] The server generates food and drink candidates using a generative AI model based on the input emotional state data and preference information. In this process, TensorFlow is used to output food and drink candidates that match the emotions and preferences through a machine learning algorithm.

[0441] Step 5:

[0442] The server works in conjunction with other agents to obtain pairing information for the generated food and beverage candidates. This pairing information utilizes results obtained from an external database to output the optimal combination.

[0443] Step 6:

[0444] The server sends the selected food and beverage items and associated pairing information to the terminal. The terminal then presents this information to the user and prompts them to select from the menu. At this time, the suggested items and pairing information are displayed in a list format.

[0445] Step 7:

[0446] The user selects their preferred food and drinks from a displayed list and sends their selections to the server via their device. The server receives these selections as input, prepares for the purchase process, and finally confirms the order.

[0447] 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.

[0448] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0449] 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.

[0450] [Third Embodiment]

[0451] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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".

[0463] This invention is a system for selecting and providing food and beverages that are suitable for the individual preferences of users. The system consists of user terminals, a central server, and related agents. The operation of the system is described below.

[0464] First, the user launches an application on their device and enters their preferences. This preferences include details such as "I prefer fruity red wines" or "I want a wine that pairs well with cheese." This information is then sent from the device to the server.

[0465] The server updates the user's preference profile based on the received preference information and uses generative AI to generate a list of suitable food and beverage options. In this process, a machine learning algorithm selects the most suitable food and beverage based on the user's past preference history and current preference information.

[0466] Next, the server works with relevant agents to gather information on suitable pairings and additional recommendations for the selected food and beverages. Through collaboration with other agents, it becomes possible to obtain recommendations from a cheese agent for cheeses that pair well with the wine selected by the wine agent.

[0467] The collected information is sent from the server to the terminal and displayed to the user. The displayed content includes a list of selected food and beverages, detailed information about each food and beverage, and recommended pairing information. For example, if the user selects a "fruity red wine," a suggestion for a "Brie cheese" that pairs well with it will also be displayed.

[0468] After the user reviews the presented information and selects the recommended food and beverages, the terminal sends that information to the server, and the purchase process proceeds automatically. This allows users to quickly obtain products that suit their preferences and enhances the enjoyment of food and beverage pairings.

[0469] This system allows companies to provide performance-based recommendations tailored to user preferences, enabling them to efficiently deliver more personalized recommendations. As a result, it can improve the customer experience and promote the sale of food and beverages.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The user launches an application on the device. The device reads the user's profile data from local storage and performs the login process.

[0473] Step 2:

[0474] The user inputs their current preferences and desired food and drink. The terminal provides a function to input requests in text format through the user interface, such as "a fruity red wine."

[0475] Step 3:

[0476] The terminal sends the input preference information to the server. The terminal generates the user's preference information as an HTTP request and sends it to the server.

[0477] Step 4:

[0478] The server analyzes the received preference information and updates the user's preference profile. The server accesses the database and updates the profile data based on the received information.

[0479] Step 5:

[0480] The server uses AI generation to create food and drink suggestions that match the user's preferences. The server applies machine learning algorithms to create multiple candidate lists.

[0481] Step 6:

[0482] The server works with relevant agents to collect additional information. Based on the information obtained from the wine agent, the server accesses the cheese agent to retrieve suitable pairing information.

[0483] Step 7:

[0484] The server sends the results obtained to the terminal. The server generates a response summarizing the selected food and beverages, their detailed information, and pairing suggestions, and sends it to the terminal.

[0485] Step 8:

[0486] The terminal displays information received from the server to the user. Through the user interface, the terminal provides the user with a list of details about the selected wine and suggested cheeses.

[0487] Step 9:

[0488] The user selects the product they wish to purchase from the displayed options. The user then clicks the purchase button to confirm their selection.

[0489] Step 10:

[0490] The device sends purchase information to the server and initiates the purchase process. The device sends a purchase request to the server and performs the process, including payment processing.

[0491] (Example 1)

[0492] 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."

[0493] Modern consumers have extremely diverse tastes and seek consumption experiences tailored to their individual needs. However, traditional methods have made it difficult to quickly suggest food and beverages that accurately match individual consumer preferences, and to efficiently collect and present relevant information. As a result, the process of selecting and purchasing suitable products is time-consuming, and it is difficult to experience highly satisfying pairings.

[0494] 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.

[0495] In this invention, the server includes means for generating food and beverage candidates using a generative AI model based on preference information, means for collecting information related to the selected food and beverages by communicating with other information providers, and a device for providing the collected information to the user. As a result, the user can receive a rapid presentation of food and beverages suited to their individual preferences and obtain comprehensive recommendations that include related information. This streamlines the product selection and purchase process and enables a personalized and highly satisfying consumer experience.

[0496] A "user" is someone who uses this system to input their preferences and to select and purchase food and beverages.

[0497] "Preference information" refers to information about a user's individual preferences and tastes, and is data used to select specific food and beverage products.

[0498] A "generative AI model" is an artificial intelligence model designed to generate new suggestions and solutions based on large amounts of data.

[0499] A "prompt message" is a sentence input to a generative AI model, serving as an instruction to obtain the information or solution desired by the user.

[0500] An "information provider" is an external system or agent that provides additional information about food and beverages and related pairings.

[0501] "Transaction procedures" refer to the series of processes involved in the purchase of food and beverages selected by the user, including everything from payment to delivery arrangements.

[0502] "Pairing" is the process of recommending other related food and beverages to complement the selected food and beverage, with the aim of providing users with a more fulfilling consumption experience.

[0503] The description of the embodiments for carrying out the invention is as follows:

[0504] This system aims to provide users with food and beverages optimized for their needs and utilizes terminals, servers, and related agents. The system's configuration consists of bidirectional communication between the server and the user's terminal, and data exchange with multiple agents.

[0505] Users first input their preferences using a dedicated application on their device. This is done via an information input device such as a smartphone or personal computer. This preference information is transmitted to a server via the internet. The server updates its database based on the received preference information and builds the latest user profile.

[0506] Next, the server uses a generative AI model to generate food and beverage candidates that match the user's preferences. In this process, a prompt can be set as follows to instruct the AI ​​model to perform the appropriate action: "Recommend a fruity red wine based on the user's preferences." This allows the system to select the most suitable food and beverage candidates from a large amount of data.

[0507] The server also collaborates with external information providers, acting as agents, to collect complementary information related to the selected food and beverages. Through this process, for example, potential pairings of cheese and other dishes that go well with wine can be obtained. The collected information is then sent back to the terminal and presented to the user visually.

[0508] Users view recommended food and beverages and their details on their device, and if they wish to purchase them, they send that information back to the server from their device. The purchase process is automatically handled by the server, improving the convenience of shopping. This system allows users to quickly select their preferred food and beverages and enjoy them in combination with related information.

[0509] The generative AI models implemented in the system play a role in enriching the customer experience by providing highly accurate recommendations tailored to users with diverse preferences.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] The user launches an application on their device and enters their preferences. Examples of this information include "I prefer fruity red wine."

[0513] The input information is received by the app and then prepared to be sent to the server. Here, the input is the user's preference information, and the output is the preference information that is ready to be sent to the server.

[0514] Step 2:

[0515] The terminal encrypts the user's input preference information and sends it to the server. This process uses protocols such as TLS to ensure data security.

[0516] The input is preference information stored on the device, and the output is transmitted to the server as encrypted data. The server receives this data and prepares to generate a preference profile.

[0517] Step 3:

[0518] The server updates the database based on the received preference information and creates the user's latest preference profile. This generates a new profile that reflects the individual user's input data.

[0519] The input is preference information received by the server, and the output is an updated user profile. Based on this information, the next step involves using an AI model to generate food and beverage options.

[0520] Step 4:

[0521] The server inputs a prompt message into the generation AI model, instructing it to "generate the optimal food and drink based on the user's preferences." The AI ​​model retrieves relevant information from the database and calculates the best food and drink options.

[0522] The input consists of the user's preference profile and a prompt, while the output is a list of food and drink options selected by the AI. A specific example of the output is a wine name such as "Chardonnay."

[0523] Step 5:

[0524] The server communicates with other information providers, which are agents, to collect relevant information suitable for pairing with candidate food and beverages. For example, it communicates with a wine agent to obtain information such as which cheeses pair well with Chardonnay.

[0525] The input is a selection of food and beverage candidates, and the output is detailed recommendation information, including pairing information. This process yields broader and more comprehensive information.

[0526] Step 6:

[0527] The server sends the collected information back to the terminal and presents it visually to the user. The terminal displays a list of food and beverage options and related information on the user's screen. Here, the selected wines and pairing suggestions are displayed in a list.

[0528] The input consists of food, beverages, and related information received from the server, while the output is the display of information to the user. The user reviews the displayed information and makes a selection.

[0529] Step 7:

[0530] The user selects the food and beverage items they wish to purchase from the displayed options and resends this information to the server via their device. The device receives the user's selection and initiates the purchase process.

[0531] The input is the user's selection information, and the output is the data resent to the server. The server automatically processes the payment based on this information, and the purchase is completed.

[0532] (Application Example 1)

[0533] 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."

[0534] There is a need to improve user satisfaction by efficiently providing optimal food and beverage recommendations tailored to individual user preferences, along with pairing information. However, the current system has the challenge of not being able to fully utilize users' past history and thus not being able to provide completely personalized recommendations. Furthermore, improvements are needed in terms of speeding up the purchase process and providing sufficient pairing information.

[0535] 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.

[0536] In this invention, the server includes means for selecting suitable food based on the user's preference information, means for collecting supplementary information about the selected food in cooperation with other devices, and means for presenting food and beverage combinations that reflect the user's past history. This makes it possible to provide users with personalized and optimal food and beverage suggestions and pairing information, thereby increasing user satisfaction along with a quick purchase process.

[0537] A "user" refers to an individual who uses this system to select food and drinks that suit their preferences.

[0538] "Preference information" refers to information about food and beverages based on the user's preferences and past experiences.

[0539] A "recording device" refers to equipment or software that stores user preference information and makes it available for later use.

[0540] "Suitable foods" refer to foods selected based on the user's preferences.

[0541] "Supplemental information" refers to additional information related to the selected food, including information on how it can be paired with other foods and beverages.

[0542] A "device that interacts with other devices" refers to a device that exchanges information with different devices or systems and works together to achieve its functions.

[0543] "History" refers to a record of food and drink choices and preferences that a user has made in the past.

[0544] A "combination suggestion device" refers to a device or software that displays information to the user about selected food items and suitable beverages or other foods.

[0545] "Purchase procedure" refers to a series of actions taken by a user to purchase food and beverages they have selected.

[0546] This invention is a system that suggests optimal food and beverages based on the user's preference information. The system is mainly composed of user terminals and a central server.

[0547] The server receives and records preference information entered by the user through their terminal. This preference information includes the user's preferences and past selection history. The recorded information is analyzed using a generative AI model to select the most suitable food and beverage combination for the user. In this process, the AI ​​learns the user's patterns and improves the accuracy of its suggestions.

[0548] The user's device displays the selected food item and supplementary information about beverages that pair well with it. This supplementary information is obtained by the server communicating with other agents. For example, if a wine is selected, the wine agent will recommend a suitable cheese to complement it. The information provided in this way is detailed and personalized.

[0549] Based on this information, users can select food and beverages and complete the purchase process. The purchase process is completed quickly on the terminal, and the selected items are delivered to the user.

[0550] This system allows users to enjoy a dining experience perfectly tailored to their preferences. For example, if a user prefers spicy food, the system can suggest appropriate beverages to complement it. An example of a prompt supporting this process would be: "The user prefers spicy food. Please suggest a meal including optimal beverage pairings, along with their past preferences."

[0551] Thus, the invention aims to improve user satisfaction by providing food and beverages tailored to the individual preferences of the users.

[0552] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0553] Step 1:

[0554] The terminal inputs the user's preference information. This preference information includes favorite foods and past dining history. The input information is sent from the terminal to the server. At this point, the input is the user's preference data, and the output is the transmission of data to the server.

[0555] Step 2:

[0556] The server records the received preference information and analyzes it using a generative AI model. The input is preference information received from the user, and based on this information, it generates output that selects suitable food and beverages by referring to a database. Machine learning algorithms are used for data analysis, and the selection accuracy is improved by learning patterns.

[0557] Step 3:

[0558] The server generates selected food and drink combinations and sends this information to other agents. It then obtains supplementary information from other agents to complete the combinations that best match the selected foods. The input for this step is a list of AI-selected food and drink combinations, and the output is the optimal food and drink combinations, including supplementary information. This process involves specific actions to collect recommendation information from other agents.

[0559] Step 4:

[0560] The terminal, based on information received from the server, presents the user with specific food and beverage recommendations. These recommendations include detailed information about the food and beverages, including pairing suggestions. The input is food and beverage recommendations from the server, and the output is the information presented to the user. At this stage, the user reviews and selects products that suit their preferences.

[0561] Step 5:

[0562] The terminal initiates the purchase process for the food and beverage items selected by the user. Purchase-related data is sent to the server, automating the process. Input is the user's selection, and output is confirmation of the purchase completion. Specifically, this includes connecting to the payment system and checking inventory.

[0563] 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.

[0564] This invention is a system that selects and provides appropriate food and beverages based on the user's individual preferences and emotional state. This system consists of a user terminal, a central server, an emotion engine, and related agents. The system's operation is described below.

[0565] First, the user launches an application installed on the device. The device is equipped with a camera, and the user's facial expressions are analyzed by an emotion engine. In this process, the user's current emotional state is identified, and a variety of emotions such as joy, sadness, and surprise can be detected.

[0566] In addition to their emotional state, users input their preferences for food and beverages through the terminal's interface. The terminal then transmits this information to the server.

[0567] The server updates the user profile based on the received preference and emotional state information, and uses generative AI to generate appropriate food and beverage options. In this process, options tailored to the consumer's emotions are listed and selected.

[0568] Next, the server collaborates with relevant agents to obtain pairing information and additional information suitable for the selected food and beverage. This collaboration allows, for example, a cheese agent to suggest the most suitable cheese based on information obtained from a wine agent.

[0569] The information collected in this way is sent from the server to the terminal and presented to the user. The user reviews the selected food and beverages and detailed information on their pairings in a list format and decides on their purchase based on that information.

[0570] Ultimately, the process of purchasing the food and beverages selected by the user is completed via the terminal. For example, if the user selects "a red wine that suits their preferences," a pairing of "blue cheese" that complements it will also be suggested.

[0571] This system improves user satisfaction and streamlines the product selection process by providing appropriate food and beverage suggestions based on the user's emotional changes. As a result, it can enrich the customer experience and increase their willingness to purchase.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The user launches an application on the device. The device will display a login prompt and provide the user with the ability to enter their authentication information.

[0575] Step 2:

[0576] The device's built-in camera and emotion engine work together to capture the user's facial expressions. The device sends the captured image to the emotion engine, which then analyzes the user's emotional state. For example, if the user is smiling, it is recognized as "joy."

[0577] Step 3:

[0578] The user inputs their preferences using the application's interface. For example, they might enter specific requests as text, such as "I'm looking for a drink that pairs well with spicy food."

[0579] Step 4:

[0580] The terminal combines preference information entered by the user with emotional state information generated by the emotion engine, and sends this data to the server.

[0581] Step 5:

[0582] The server analyzes the received data and generates optimized food and drink suggestions based on the user's preference profile and emotional state. The server uses a generation AI to create a list of suggestions, taking into account past preference history and current state.

[0583] Step 6:

[0584] The server collaborates with relevant agents to obtain pairing information and additional information that matches the selected food and beverage. For example, it might use a wine agent to obtain information on cheeses that pair well with the selected wine from a cheese agent.

[0585] Step 7:

[0586] The server sends the selected food and beverage pairing information to the terminal. The data sent includes the product name, detailed description, and emotionally tailored recommendation reasons.

[0587] Step 8:

[0588] The terminal displays the information it receives to the user. The terminal interface is configured to allow the user to easily review the selection results and make a purchase decision.

[0589] Step 9:

[0590] The user selects the product they want to purchase from the presented options. The user reviews the list, checks the details of the selected product, and then clicks the purchase button.

[0591] Step 10:

[0592] The terminal sends the selected purchase information to the server and initiates the purchase process. The server verifies the payment information and processes the settlement, completing the transaction.

[0593] (Example 2)

[0594] 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."

[0595] Conventional food and beverage selection systems tend to make suggestions based solely on user preference information, making it difficult to make optimal selections that take into account the emotional state of individual users. Furthermore, a lack of proper coordination with related agents made it difficult to provide users with a comprehensive dining experience.

[0596] 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.

[0597] In this invention, the server includes means for analyzing the user's emotional state, means for generating multiple food and beverage candidates using generative AI, and means for collecting additional information in cooperation with related agents. This makes it possible to suggest more suitable food and beverages by combining the user's emotional state and preference information.

[0598] "User" refers to an individual or group that uses this system to perform operations or input data.

[0599] "Emotional state" refers to information that represents the type and changes in the user's emotions, and is mainly obtained through facial expression analysis.

[0600] "Preference information" refers to data that represents the types and characteristics of food and beverages that users prefer, and is based on user input.

[0601] "Generative AI" refers to algorithms and models that utilize artificial intelligence technology to generate food and beverage options based on user preferences and emotional states.

[0602] "Related agents" refer to external systems or services that collaborate to provide additional information related to the selected food and beverage items.

[0603] "Additional information" refers to information that is useful when pairing selected food and beverages, and includes pairing data and related product information.

[0604] This invention is a system that suggests individually tailored food and beverages based on the user's emotional state and preference information. The system mainly consists of the user's terminal, a central server, an emotion analysis engine, and related agents. The operation of each element is described below.

[0605] The user first launches an application installed on the device. The device has a camera, which is used to capture the user's facial expressions. During this process, the device uses an emotion analysis engine to identify the user's emotional state from their facial expressions. For example, it might use Google's facial recognition API to analyze emotions such as joy or surprise and generate the results.

[0606] Users input their preferences, such as their favorite types of food and drinks, through the terminal's interface. The terminal sends this information to a server, which updates the user profile based on it. This profile update also includes collected emotional state information.

[0607] The server uses a generative AI model to generate appropriate food and beverage options based on an updated profile that includes the user's emotional state and preference information. During this process, the generative AI model is prompted with the message, "Please suggest food and beverages that match the user's emotions," and an optimized list of candidates is compiled.

[0608] Next, the server collaborates with relevant agents to gather optimal pairing information and additional details for the suggested food and beverages. This collaboration allows, for example, the server to receive information on the best cheeses to enjoy with certain types of wine.

[0609] For example, if the analysis results indicate that the user wants to "relax," the server, through its AI-generated content, will suggest chamomile tea and also propose pairing it with herbal cookies. This kind of information is sent from the server to the terminal and presented to the user.

[0610] Ultimately, users can complete the purchase process for food and beverages using their devices. This allows users to have an enjoyable consumption experience, and since the suggested products are optimized for their emotional state, increased satisfaction can be expected.

[0611] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0612] Step 1:

[0613] The user launches the app on the device. The device launches the application based on the user's input and displays the home screen. Here, the user taps the start button to access the system.

[0614] Step 2:

[0615] The device captures the user's facial expressions with its camera and collects that data. The input is the user's facial image, and the output generates image data necessary for emotion analysis.

[0616] Step 3:

[0617] The device analyzes facial expression data using an emotion analysis engine. Image data is provided to the emotion analysis engine as input, and category information of emotions such as joy and surprise is obtained as output. In this process, the analysis model estimates the emotional state.

[0618] Step 4:

[0619] The user inputs preference information through the device's interface. The user selects categories of preferred foods and beverages, and this information is collected as input data. The device temporarily stores this information.

[0620] Step 5:

[0621] The terminal sends emotional state and preference information to the server. The input data sent from the terminal consists of analyzed emotional information and preference information entered by the user. The server receives this data and generates output that updates the user's profile.

[0622] Step 6:

[0623] The server generates food and drink suggestions using a generative AI model. The input is an updated user profile, and the prompt "Please suggest food and drink that matches the user's mood" is sent to the model, resulting in a list of food and drink suggestions as output.

[0624] Step 7:

[0625] The server works in conjunction with relevant agents to collect additional information. Using the selected food and beverage information as input, it queries the agents and receives output such as optimal pairing information and related product information.

[0626] Step 8:

[0627] The server sends the final suggestions to the terminal. The output data from the server to the terminal includes a list of food and beverage options and their associated pairing information.

[0628] Step 9:

[0629] The user reviews the information presented through the device and makes a purchase decision. The user's selection is the final input for the purchase decision, and the device completes the purchase process based on that information.

[0630] (Application Example 2)

[0631] 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."

[0632] In modern life, selecting food and beverages that suit individual preferences is important, but conventional systems have the challenge of not being able to suggest food and beverages that take into account the user's emotional state. Furthermore, there is a lack of mechanisms that provide appropriate suggestions based on food and beverage pairing information and emotional state, and there is a need to improve user satisfaction.

[0633] 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.

[0634] In this invention, the server includes means for recording user preference information, means for analyzing image data to determine the user's emotional state, and means for selecting multiple food and beverage candidates using a generation AI based on the emotional state and preference information. This makes it possible to present appropriate food and beverage suggestions and pairing information according to the user's emotions.

[0635] "User preference information" refers to information that indicates users' preferences and selection criteria regarding food and beverages.

[0636] "Food and beverages" is a general term encompassing both drinks and food, referring to anything that users consume.

[0637] An "agent" is a program or database that works together to collect and provide information.

[0638] "Emotional state" refers to the temporary psychological state exhibited by the user and is determined through methods such as facial expression analysis.

[0639] "Image data" refers to visual information acquired by a camera device or similar device.

[0640] "Generative AI" is an artificial intelligence system that uses machine learning techniques to learn patterns from data and make predictions and suggestions.

[0641] "Pairing information" refers to recommended information about other food and beverages that can be enjoyed together with the selected food and beverage.

[0642] The system for realizing this invention provides a function to select and suggest food and beverages based on the user's emotional state and preference information. Specifically, a smartphone or other device uses a camera device to capture the user's face and sends the image data to a server. The server uses the image processing library OpenCV to analyze the user's emotional state from the image data.

[0643] The analysis results and the user's preference information entered through the interface are stored in the server's database. Based on the stored data, a generative AI model uses TensorFlow to generate optimal food and beverage candidates. This generative AI utilizes machine learning techniques to suggest food and beverages that best match the user's emotional state and preferences.

[0644] The suggested food and beverage information, along with pairing information collected through collaboration with other agents, is sent back from the server to the terminal. The terminal presents this information to the user, allowing the user to confirm their selection of food and beverages. This process enables the user to easily order appropriate meals that match their mood, thereby increasing their satisfaction.

[0645] For example, if a user is stressed, the system will suggest a spicy dish. It may also suggest adding mint tea to promote refreshment. A key feature is the use of a generative AI model to suggest appropriate food and drink based on the user's emotions. An example of a prompt would be, "If the user is stressed, suggest a refreshing drink that suits their preferences."

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] The device activates its camera to acquire facial expression data from the user and takes a picture of the user's face. This capture process inputs the original image data, which is then prepared for processing.

[0649] Step 2:

[0650] The terminal sends the acquired image data to the server. The server receives this image data as input and analyzes the emotional state using facial recognition technology. Specifically, it uses OpenCV to detect various emotional characteristics and outputs the results as emotional state data.

[0651] Step 3:

[0652] Users input their preferences through the terminal's interface. The terminal aggregates this preference information and sends it to the server. This preference information is then stored directly in a database and used in subsequent suggestion processes.

[0653] Step 4:

[0654] The server generates food and drink candidates using a generative AI model based on the input emotional state data and preference information. In this process, TensorFlow is used to output food and drink candidates that match the emotions and preferences through a machine learning algorithm.

[0655] Step 5:

[0656] The server works in conjunction with other agents to obtain pairing information for the generated food and beverage candidates. This pairing information utilizes results obtained from an external database to output the optimal combination.

[0657] Step 6:

[0658] The server sends the selected food and beverage items and associated pairing information to the terminal. The terminal then presents this information to the user and prompts them to select from the menu. At this time, the suggested items and pairing information are displayed in a list format.

[0659] Step 7:

[0660] The user selects their preferred food and drinks from a displayed list and sends their selections to the server via their device. The server receives these selections as input, prepares for the purchase process, and finally confirms the order.

[0661] 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.

[0662] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0663] 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.

[0664] [Fourth Embodiment]

[0665] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0666] 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.

[0667] 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).

[0668] 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.

[0669] 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.

[0670] 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).

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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".

[0678] This invention is a system for selecting and providing food and beverages that are suitable for the individual preferences of users. The system consists of user terminals, a central server, and related agents. The operation of the system is described below.

[0679] First, the user launches an application on their device and enters their preferences. This preferences include details such as "I prefer fruity red wines" or "I want a wine that pairs well with cheese." This information is then sent from the device to the server.

[0680] The server updates the user's preference profile based on the received preference information and uses generative AI to generate a list of suitable food and beverage options. In this process, a machine learning algorithm selects the most suitable food and beverage based on the user's past preference history and current preference information.

[0681] Next, the server works with relevant agents to gather information on suitable pairings and additional recommendations for the selected food and beverages. Through collaboration with other agents, it becomes possible to obtain recommendations from a cheese agent for cheeses that pair well with the wine selected by the wine agent.

[0682] The collected information is sent from the server to the terminal and displayed to the user. The displayed content includes a list of selected food and beverages, detailed information about each food and beverage, and recommended pairing information. For example, if the user selects a "fruity red wine," a suggestion for a "Brie cheese" that pairs well with it will also be displayed.

[0683] After the user reviews the presented information and selects the recommended food and beverages, the terminal sends that information to the server, and the purchase process proceeds automatically. This allows users to quickly obtain products that suit their preferences and enhances the enjoyment of food and beverage pairings.

[0684] This system allows companies to provide performance-based recommendations tailored to user preferences, enabling them to efficiently deliver more personalized recommendations. As a result, it can improve the customer experience and promote the sale of food and beverages.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The user launches an application on the device. The device reads the user's profile data from local storage and performs the login process.

[0688] Step 2:

[0689] The user inputs their current preferences and desired food and drink. The terminal provides a function to input requests in text format through the user interface, such as "a fruity red wine."

[0690] Step 3:

[0691] The terminal sends the input preference information to the server. The terminal generates the user's preference information as an HTTP request and sends it to the server.

[0692] Step 4:

[0693] The server analyzes the received preference information and updates the user's preference profile. The server accesses the database and updates the profile data based on the received information.

[0694] Step 5:

[0695] The server uses AI generation to create food and drink suggestions that match the user's preferences. The server applies machine learning algorithms to create multiple candidate lists.

[0696] Step 6:

[0697] The server works with relevant agents to collect additional information. Based on the information obtained from the wine agent, the server accesses the cheese agent to retrieve suitable pairing information.

[0698] Step 7:

[0699] The server sends the results obtained to the terminal. The server generates a response summarizing the selected food and beverages, their detailed information, and pairing suggestions, and sends it to the terminal.

[0700] Step 8:

[0701] The terminal displays information received from the server to the user. Through the user interface, the terminal provides the user with a list of details about the selected wine and suggested cheeses.

[0702] Step 9:

[0703] The user selects the product they wish to purchase from the displayed options. The user then clicks the purchase button to confirm their selection.

[0704] Step 10:

[0705] The device sends purchase information to the server and initiates the purchase process. The device sends a purchase request to the server and performs the process, including payment processing.

[0706] (Example 1)

[0707] 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".

[0708] Modern consumers have extremely diverse tastes and seek consumption experiences tailored to their individual needs. However, traditional methods have made it difficult to quickly suggest food and beverages that accurately match individual consumer preferences, and to efficiently collect and present relevant information. As a result, the process of selecting and purchasing suitable products is time-consuming, and it is difficult to experience highly satisfying pairings.

[0709] 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.

[0710] In this invention, the server includes means for generating food and beverage candidates using a generative AI model based on preference information, means for collecting information related to the selected food and beverages by communicating with other information providers, and a device for providing the collected information to the user. As a result, the user can receive a rapid presentation of food and beverages suited to their individual preferences and obtain comprehensive recommendations that include related information. This streamlines the product selection and purchase process and enables a personalized and highly satisfying consumer experience.

[0711] A "user" is someone who uses this system to input their preferences and to select and purchase food and beverages.

[0712] "Preference information" refers to information about a user's individual preferences and tastes, and is data used to select specific food and beverage products.

[0713] A "generative AI model" is an artificial intelligence model designed to generate new suggestions and solutions based on large amounts of data.

[0714] A "prompt message" is a sentence input to a generative AI model, serving as an instruction to obtain the information or solution desired by the user.

[0715] An "information provider" is an external system or agent that provides additional information about food and beverages and related pairings.

[0716] "Transaction procedures" refer to the series of processes involved in the purchase of food and beverages selected by the user, including everything from payment to delivery arrangements.

[0717] "Pairing" is the process of recommending other related food and beverages to complement the selected food and beverage, with the aim of providing users with a more fulfilling consumption experience.

[0718] The description of the embodiments for carrying out the invention is as follows:

[0719] This system aims to provide users with food and beverages optimized for their needs and utilizes terminals, servers, and related agents. The system's configuration consists of bidirectional communication between the server and the user's terminal, and data exchange with multiple agents.

[0720] Users first input their preferences using a dedicated application on their device. This is done via an information input device such as a smartphone or personal computer. This preference information is transmitted to a server via the internet. The server updates its database based on the received preference information and builds the latest user profile.

[0721] Next, the server uses a generative AI model to generate food and beverage candidates that match the user's preferences. In this process, a prompt can be set as follows to instruct the AI ​​model to perform the appropriate action: "Recommend a fruity red wine based on the user's preferences." This allows the system to select the most suitable food and beverage candidates from a large amount of data.

[0722] The server also collaborates with external information providers, acting as agents, to collect complementary information related to the selected food and beverages. Through this process, for example, potential pairings of cheese and other dishes that go well with wine can be obtained. The collected information is then sent back to the terminal and presented to the user visually.

[0723] Users view recommended food and beverages and their details on their device, and if they wish to purchase them, they send that information back to the server from their device. The purchase process is automatically handled by the server, improving the convenience of shopping. This system allows users to quickly select their preferred food and beverages and enjoy them in combination with related information.

[0724] The generative AI models implemented in the system play a role in enriching the customer experience by providing highly accurate recommendations tailored to users with diverse preferences.

[0725] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0726] Step 1:

[0727] The user launches an application on their device and enters their preferences. Examples of this information include "I prefer fruity red wine."

[0728] The input information is received by the app and then prepared to be sent to the server. Here, the input is the user's preference information, and the output is the preference information that is ready to be sent to the server.

[0729] Step 2:

[0730] The terminal encrypts the user's input preference information and sends it to the server. This process uses protocols such as TLS to ensure data security.

[0731] The input is preference information stored on the device, and the output is transmitted to the server as encrypted data. The server receives this data and prepares to generate a preference profile.

[0732] Step 3:

[0733] The server updates the database based on the received preference information and creates the user's latest preference profile. This generates a new profile that reflects the individual user's input data.

[0734] The input is preference information received by the server, and the output is an updated user profile. Based on this information, the next step involves using an AI model to generate food and beverage options.

[0735] Step 4:

[0736] The server inputs a prompt message into the generation AI model, instructing it to "generate the optimal food and drink based on the user's preferences." The AI ​​model retrieves relevant information from the database and calculates the best food and drink options.

[0737] The input consists of the user's preference profile and a prompt, while the output is a list of food and drink options selected by the AI. A specific example of the output is a wine name such as "Chardonnay."

[0738] Step 5:

[0739] The server communicates with other information providers, which are agents, to collect relevant information suitable for pairing with candidate food and beverages. For example, it communicates with a wine agent to obtain information such as which cheeses pair well with Chardonnay.

[0740] The input is a selection of food and beverage candidates, and the output is detailed recommendation information, including pairing information. This process yields broader and more comprehensive information.

[0741] Step 6:

[0742] The server sends the collected information back to the terminal and presents it visually to the user. The terminal displays a list of food and beverage options and related information on the user's screen. Here, the selected wines and pairing suggestions are displayed in a list.

[0743] The input consists of food, beverages, and related information received from the server, while the output is the display of information to the user. The user reviews the displayed information and makes a selection.

[0744] Step 7:

[0745] The user selects the food and beverage items they wish to purchase from the displayed options and resends this information to the server via their device. The device receives the user's selection and initiates the purchase process.

[0746] The input is the user's selection information, and the output is the data resent to the server. The server automatically processes the payment based on this information, and the purchase is completed.

[0747] (Application Example 1)

[0748] 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".

[0749] There is a need to improve user satisfaction by efficiently providing optimal food and beverage recommendations tailored to individual user preferences, along with pairing information. However, the current system has the challenge of not being able to fully utilize users' past history and thus not being able to provide completely personalized recommendations. Furthermore, improvements are needed in terms of speeding up the purchase process and providing sufficient pairing information.

[0750] 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.

[0751] In this invention, the server includes means for selecting suitable food based on the user's preference information, means for collecting supplementary information about the selected food in cooperation with other devices, and means for presenting food and beverage combinations that reflect the user's past history. This makes it possible to provide users with personalized and optimal food and beverage suggestions and pairing information, thereby increasing user satisfaction along with a quick purchase process.

[0752] A "user" refers to an individual who uses this system to select food and drinks that suit their preferences.

[0753] "Preference information" refers to information about food and beverages based on the user's preferences and past experiences.

[0754] A "recording device" refers to equipment or software that stores user preference information and makes it available for later use.

[0755] "Suitable foods" refer to foods selected based on the user's preferences.

[0756] "Supplemental information" refers to additional information related to the selected food, including information on how it can be paired with other foods and beverages.

[0757] A "device that interacts with other devices" refers to a device that exchanges information with different devices or systems and works together to achieve its functions.

[0758] "History" refers to a record of food and drink choices and preferences that a user has made in the past.

[0759] A "combination suggestion device" refers to a device or software that displays information to the user about selected food items and suitable beverages or other foods.

[0760] "Purchase procedure" refers to a series of actions taken by a user to purchase food and beverages they have selected.

[0761] This invention is a system that suggests optimal food and beverages based on the user's preference information. The system is mainly composed of user terminals and a central server.

[0762] The server receives and records preference information entered by the user through their terminal. This preference information includes the user's preferences and past selection history. The recorded information is analyzed using a generative AI model to select the most suitable food and beverage combination for the user. In this process, the AI ​​learns the user's patterns and improves the accuracy of its suggestions.

[0763] The user's device displays the selected food item and supplementary information about beverages that pair well with it. This supplementary information is obtained by the server communicating with other agents. For example, if a wine is selected, the wine agent will recommend a suitable cheese to complement it. The information provided in this way is detailed and personalized.

[0764] Based on this information, users can select food and beverages and complete the purchase process. The purchase process is completed quickly on the terminal, and the selected items are delivered to the user.

[0765] This system allows users to enjoy a dining experience perfectly tailored to their preferences. For example, if a user prefers spicy food, the system can suggest appropriate beverages to complement it. An example of a prompt supporting this process would be: "The user prefers spicy food. Please suggest a meal including optimal beverage pairings, along with their past preferences."

[0766] Thus, the invention aims to improve user satisfaction by providing food and beverages tailored to the individual preferences of the users.

[0767] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0768] Step 1:

[0769] The terminal inputs the user's preference information. This preference information includes favorite foods and past dining history. The input information is sent from the terminal to the server. At this point, the input is the user's preference data, and the output is the transmission of data to the server.

[0770] Step 2:

[0771] The server records the received preference information and analyzes it using a generative AI model. The input is preference information received from the user, and based on this information, it generates output that selects suitable food and beverages by referring to a database. Machine learning algorithms are used for data analysis, and the selection accuracy is improved by learning patterns.

[0772] Step 3:

[0773] The server generates selected food and drink combinations and sends this information to other agents. It then obtains supplementary information from other agents to complete the combinations that best match the selected foods. The input for this step is a list of AI-selected food and drink combinations, and the output is the optimal food and drink combinations, including supplementary information. This process involves specific actions to collect recommendation information from other agents.

[0774] Step 4:

[0775] The terminal, based on information received from the server, presents the user with specific food and beverage recommendations. These recommendations include detailed information about the food and beverages, including pairing suggestions. The input is food and beverage recommendations from the server, and the output is the information presented to the user. At this stage, the user reviews and selects products that suit their preferences.

[0776] Step 5:

[0777] The terminal initiates the purchase process for the food and beverage items selected by the user. Purchase-related data is sent to the server, automating the process. Input is the user's selection, and output is confirmation of the purchase completion. Specifically, this includes connecting to the payment system and checking inventory.

[0778] 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.

[0779] This invention is a system that selects and provides appropriate food and beverages based on the user's individual preferences and emotional state. This system consists of a user terminal, a central server, an emotion engine, and related agents. The system's operation is described below.

[0780] First, the user launches an application installed on the device. The device is equipped with a camera, and the user's facial expressions are analyzed by an emotion engine. In this process, the user's current emotional state is identified, and a variety of emotions such as joy, sadness, and surprise can be detected.

[0781] In addition to their emotional state, users input their preferences for food and beverages through the terminal's interface. The terminal then transmits this information to the server.

[0782] The server updates the user profile based on the received preference and emotional state information, and uses generative AI to generate appropriate food and beverage options. In this process, options tailored to the consumer's emotions are listed and selected.

[0783] Next, the server collaborates with relevant agents to obtain pairing information and additional information suitable for the selected food and beverage. This collaboration allows, for example, a cheese agent to suggest the most suitable cheese based on information obtained from a wine agent.

[0784] The information collected in this way is sent from the server to the terminal and presented to the user. The user reviews the selected food and beverages and detailed information on their pairings in a list format and decides on their purchase based on that information.

[0785] Ultimately, the process of purchasing the food and beverages selected by the user is completed via the terminal. For example, if the user selects "a red wine that suits their preferences," a pairing of "blue cheese" that complements it will also be suggested.

[0786] This system improves user satisfaction and streamlines the product selection process by providing appropriate food and beverage suggestions based on the user's emotional changes. As a result, it can enrich the customer experience and increase their willingness to purchase.

[0787] The following describes the processing flow.

[0788] Step 1:

[0789] The user launches an application on the device. The device will display a login prompt and provide the user with the ability to enter their authentication information.

[0790] Step 2:

[0791] The device's built-in camera and emotion engine work together to capture the user's facial expressions. The device sends the captured image to the emotion engine, which then analyzes the user's emotional state. For example, if the user is smiling, it is recognized as "joy."

[0792] Step 3:

[0793] The user inputs their preferences using the application's interface. For example, they might enter specific requests as text, such as "I'm looking for a drink that pairs well with spicy food."

[0794] Step 4:

[0795] The terminal combines preference information entered by the user with emotional state information generated by the emotion engine, and sends this data to the server.

[0796] Step 5:

[0797] The server analyzes the received data and generates optimized food and drink suggestions based on the user's preference profile and emotional state. The server uses a generation AI to create a list of suggestions, taking into account past preference history and current state.

[0798] Step 6:

[0799] The server collaborates with relevant agents to obtain pairing information and additional information that matches the selected food and beverage. For example, it might use a wine agent to obtain information on cheeses that pair well with the selected wine from a cheese agent.

[0800] Step 7:

[0801] The server sends the selected food and beverage pairing information to the terminal. The data sent includes the product name, detailed description, and emotionally tailored recommendation reasons.

[0802] Step 8:

[0803] The terminal displays the information it receives to the user. The terminal interface is configured to allow the user to easily review the selection results and make a purchase decision.

[0804] Step 9:

[0805] The user selects the product they want to purchase from the presented options. The user reviews the list, checks the details of the selected product, and then clicks the purchase button.

[0806] Step 10:

[0807] The terminal sends the selected purchase information to the server and initiates the purchase process. The server verifies the payment information and processes the settlement, completing the transaction.

[0808] (Example 2)

[0809] 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".

[0810] Conventional food and beverage selection systems tend to make suggestions based solely on user preference information, making it difficult to make optimal selections that take into account the emotional state of individual users. Furthermore, a lack of proper coordination with related agents made it difficult to provide users with a comprehensive dining experience.

[0811] 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.

[0812] In this invention, the server includes means for analyzing the user's emotional state, means for generating multiple food and beverage candidates using generative AI, and means for collecting additional information in cooperation with related agents. This makes it possible to suggest more suitable food and beverages by combining the user's emotional state and preference information.

[0813] "User" refers to an individual or group that uses this system to perform operations or input data.

[0814] "Emotional state" refers to information that represents the type and changes in the user's emotions, and is mainly obtained through facial expression analysis.

[0815] "Preference information" refers to data that represents the types and characteristics of food and beverages that users prefer, and is based on user input.

[0816] "Generative AI" refers to algorithms and models that utilize artificial intelligence technology to generate food and beverage options based on user preferences and emotional states.

[0817] "Related agents" refer to external systems or services that collaborate to provide additional information related to the selected food and beverage items.

[0818] "Additional information" refers to information that is useful when pairing selected food and beverages, and includes pairing data and related product information.

[0819] This invention is a system that suggests individually tailored food and beverages based on the user's emotional state and preference information. The system mainly consists of the user's terminal, a central server, an emotion analysis engine, and related agents. The operation of each element is described below.

[0820] The user first launches an application installed on the device. The device has a camera, which is used to capture the user's facial expressions. During this process, the device uses an emotion analysis engine to identify the user's emotional state from their facial expressions. For example, it might use Google's facial recognition API to analyze emotions such as joy or surprise and generate the results.

[0821] Users input their preferences, such as their favorite types of food and drinks, through the terminal's interface. The terminal sends this information to a server, which updates the user profile based on it. This profile update also includes collected emotional state information.

[0822] The server uses a generative AI model to generate appropriate food and beverage options based on an updated profile that includes the user's emotional state and preference information. During this process, the generative AI model is prompted with the message, "Please suggest food and beverages that match the user's emotions," and an optimized list of candidates is compiled.

[0823] Next, the server collaborates with relevant agents to gather optimal pairing information and additional details for the suggested food and beverages. This collaboration allows, for example, the server to receive information on the best cheeses to enjoy with certain types of wine.

[0824] For example, if the analysis results indicate that the user wants to "relax," the server, through its AI-generated content, will suggest chamomile tea and also propose pairing it with herbal cookies. This kind of information is sent from the server to the terminal and presented to the user.

[0825] Ultimately, users can complete the purchase process for food and beverages using their devices. This allows users to have an enjoyable consumption experience, and since the suggested products are optimized for their emotional state, increased satisfaction can be expected.

[0826] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0827] Step 1:

[0828] The user launches the app on the device. The device launches the application based on the user's input and displays the home screen. Here, the user taps the start button to access the system.

[0829] Step 2:

[0830] The device captures the user's facial expressions with its camera and collects that data. The input is the user's facial image, and the output generates image data necessary for emotion analysis.

[0831] Step 3:

[0832] The device analyzes facial expression data using an emotion analysis engine. Image data is provided to the emotion analysis engine as input, and category information of emotions such as joy and surprise is obtained as output. In this process, the analysis model estimates the emotional state.

[0833] Step 4:

[0834] The user inputs preference information through the device's interface. The user selects categories of preferred foods and beverages, and this information is collected as input data. The device temporarily stores this information.

[0835] Step 5:

[0836] The terminal sends emotional state and preference information to the server. The input data sent from the terminal consists of analyzed emotional information and preference information entered by the user. The server receives this data and generates output that updates the user's profile.

[0837] Step 6:

[0838] The server generates food and drink suggestions using a generative AI model. The input is an updated user profile, and the prompt "Please suggest food and drink that matches the user's mood" is sent to the model, resulting in a list of food and drink suggestions as output.

[0839] Step 7:

[0840] The server works in conjunction with relevant agents to collect additional information. Using the selected food and beverage information as input, it queries the agents and receives output such as optimal pairing information and related product information.

[0841] Step 8:

[0842] The server sends the final suggestions to the terminal. The output data from the server to the terminal includes a list of food and beverage options and their associated pairing information.

[0843] Step 9:

[0844] The user reviews the information presented through the device and makes a purchase decision. The user's selection is the final input for the purchase decision, and the device completes the purchase process based on that information.

[0845] (Application Example 2)

[0846] 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".

[0847] In modern life, selecting food and beverages that suit individual preferences is important, but conventional systems have the challenge of not being able to suggest food and beverages that take into account the user's emotional state. Furthermore, there is a lack of mechanisms that provide appropriate suggestions based on food and beverage pairing information and emotional state, and there is a need to improve user satisfaction.

[0848] 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.

[0849] In this invention, the server includes means for recording user preference information, means for analyzing image data to determine the user's emotional state, and means for selecting multiple food and beverage candidates using a generation AI based on the emotional state and preference information. This makes it possible to present appropriate food and beverage suggestions and pairing information according to the user's emotions.

[0850] "User preference information" refers to information that indicates users' preferences and selection criteria regarding food and beverages.

[0851] "Food and beverages" is a general term encompassing both drinks and food, referring to anything that users consume.

[0852] An "agent" is a program or database that works together to collect and provide information.

[0853] "Emotional state" refers to the temporary psychological state exhibited by the user and is determined through methods such as facial expression analysis.

[0854] "Image data" refers to visual information acquired by a camera device or similar device.

[0855] "Generative AI" is an artificial intelligence system that uses machine learning techniques to learn patterns from data and make predictions and suggestions.

[0856] "Pairing information" refers to recommended information about other food and beverages that can be enjoyed together with the selected food and beverage.

[0857] The system for realizing this invention provides a function to select and suggest food and beverages based on the user's emotional state and preference information. Specifically, a smartphone or other device uses a camera device to capture the user's face and sends the image data to a server. The server uses the image processing library OpenCV to analyze the user's emotional state from the image data.

[0858] The analysis results and the user's preference information entered through the interface are stored in the server's database. Based on the stored data, a generative AI model uses TensorFlow to generate optimal food and beverage candidates. This generative AI utilizes machine learning techniques to suggest food and beverages that best match the user's emotional state and preferences.

[0859] The suggested food and beverage information, along with pairing information collected through collaboration with other agents, is sent back from the server to the terminal. The terminal presents this information to the user, allowing the user to confirm their selection of food and beverages. This process enables the user to easily order appropriate meals that match their mood, thereby increasing their satisfaction.

[0860] For example, if a user is stressed, the system will suggest a spicy dish. It may also suggest adding mint tea to promote refreshment. A key feature is the use of a generative AI model to suggest appropriate food and drink based on the user's emotions. An example of a prompt would be, "If the user is stressed, suggest a refreshing drink that suits their preferences."

[0861] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0862] Step 1:

[0863] The device activates its camera to acquire facial expression data from the user and takes a picture of the user's face. This capture process inputs the original image data, which is then prepared for processing.

[0864] Step 2:

[0865] The terminal sends the acquired image data to the server. The server receives this image data as input and analyzes the emotional state using facial recognition technology. Specifically, it uses OpenCV to detect various emotional characteristics and outputs the results as emotional state data.

[0866] Step 3:

[0867] Users input their preferences through the terminal's interface. The terminal aggregates this preference information and sends it to the server. This preference information is then stored directly in a database and used in subsequent suggestion processes.

[0868] Step 4:

[0869] The server generates food and drink candidates using a generative AI model based on the input emotional state data and preference information. In this process, TensorFlow is used to output food and drink candidates that match the emotions and preferences through a machine learning algorithm.

[0870] Step 5:

[0871] The server works in conjunction with other agents to obtain pairing information for the generated food and beverage candidates. This pairing information utilizes results obtained from an external database to output the optimal combination.

[0872] Step 6:

[0873] The server sends the selected food and beverage items and associated pairing information to the terminal. The terminal then presents this information to the user and prompts them to select from the menu. At this time, the suggested items and pairing information are displayed in a list format.

[0874] Step 7:

[0875] The user selects their preferred food and drinks from a displayed list and sends their selections to the server via their device. The server receives these selections as input, prepares for the purchase process, and finally confirms the order.

[0876] 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.

[0877] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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."

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] The following is further disclosed regarding the embodiments described above.

[0898] (Claim 1)

[0899] A means for inputting user preference information and recording said preference information,

[0900] A means for selecting suitable food and beverages based on the aforementioned preference information,

[0901] In order to collect additional information related to the selected food and beverages, a means of collaborating with other agents is provided.

[0902] A means of presenting the selected food and beverages and additional information to the user,

[0903] Means for completing the purchase procedure for the aforementioned food and beverages,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, wherein, based on the aforementioned preference information, a generation AI is used to select multiple food and beverage candidates, and the optimal one is presented from among the candidates.

[0907] (Claim 3)

[0908] The system according to claim 1, which, in cooperation with the aforementioned other agents, obtains information to suggest other food and beverages that are suitable for the selected food and beverages.

[0909] "Example 1"

[0910] (Claim 1)

[0911] A device for acquiring user preference information,

[0912] A means for recording the preference information and updating the database,

[0913] A means for generating food and beverage candidates using a generative AI model based on the aforementioned preference information,

[0914] A means for selecting the most suitable food and beverage from the aforementioned candidates,

[0915] A means for collecting information related to the selected food and beverages by communicating with another information provider,

[0916] A device that provides the collected information to the user,

[0917] A means of automatically carrying out transaction procedures based on the user's selection,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, further comprising means for obtaining appropriate food and beverage candidates by using prompt sentences as input to the aforementioned generating AI model.

[0921] (Claim 3)

[0922] The system according to claim 1, further comprising means for obtaining information that suggests ingredients or beverages to complement the selected food and beverages in cooperation with the aforementioned other information provider.

[0923] "Application Example 1"

[0924] (Claim 1)

[0925] A device for inputting user preference information and recording said preference information,

[0926] A device for selecting suitable food based on the aforementioned preference information,

[0927] In order to collect supplementary information regarding the selected food, a device that works in conjunction with other devices is used.

[0928] A device that presents the selected food and supplementary information to the user,

[0929] A device for completing the procedure for purchasing the aforementioned food,

[0930] A device that reflects the user's past history and presents food and beverage combinations,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] The system according to claim 1, wherein, based on the aforementioned preference information, a plurality of food candidates are selected using generating artificial intelligence, and the optimal one is presented from among the candidates.

[0934] (Claim 3)

[0935] The system according to claim 1, wherein, in cooperation with the aforementioned other devices, it acquires information suggesting other food and beverages that are suitable for the selected food, and presents this information including information on food and beverage combinations.

[0936] "Example 2 of combining an emotion engine"

[0937] (Claim 1)

[0938] A means of analyzing the emotional state of users,

[0939] A means for selecting suitable food and beverages based on the analyzed emotional state and preference information,

[0940] A means for generating multiple food and beverage candidates using a generation AI,

[0941] In order to collect additional information related to the selected food and beverages, a means of coordinating with relevant agents is provided.

[0942] A means of presenting the selected food and beverages and additional information to the user,

[0943] Means for completing the purchase procedure for the aforementioned food and beverages,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, which identifies an emotional state by analyzing the user's facial expressions and selects appropriate food or beverages based on that emotional state.

[0947] (Claim 3)

[0948] The system according to claim 1, which, in cooperation with the aforementioned other agents, obtains information to suggest other food and beverages that are suitable for the selected food and beverages.

[0949] "Application example 2 when combining with an emotional engine"

[0950] (Claim 1)

[0951] A means for inputting user preference information and recording said preference information,

[0952] A means for selecting suitable food and beverages based on the aforementioned preference information,

[0953] In order to collect additional information related to the selected food and beverages, a means of collaborating with other agents is provided.

[0954] A means of presenting the selected food and beverages and additional information to the user,

[0955] Means for completing the purchase procedure for the aforementioned food and beverages,

[0956] A means of analyzing image data to determine the emotional state of the user,

[0957] A means for selecting multiple food and beverage candidates using AI based on the aforementioned emotional state and preference information,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, which presents the most suitable candidate from among the aforementioned candidates.

[0961] (Claim 3)

[0962] The system according to claim 1, which, in cooperation with the aforementioned other agents, obtains information to suggest other food and beverages that are suitable for the selected food and beverages. [Explanation of symbols]

[0963] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for inputting user preference information and recording said preference information, A device for selecting suitable food based on the aforementioned preference information, In order to collect supplementary information regarding the selected food, a device that works in conjunction with other devices is used. A device that presents the selected food and supplementary information to the user, A device for completing the procedure for purchasing the aforementioned food, A device that reflects the user's past history and presents food and beverage combinations, A system that includes this.

2. The system according to claim 1, wherein, based on the aforementioned preference information, a plurality of food candidates are selected using generating artificial intelligence, and the optimal one is presented from among the candidates.

3. The system according to claim 1, wherein, in cooperation with the aforementioned other devices, it acquires information suggesting other food and beverages that are suitable for the selected food, and presents this information including information on food and beverage combinations.

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