System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving selection information related to cooking and seasoning from a user terminal; generation means for generating types and quantities of necessary seasonings and spices based on the selection information; and automatic mixing means for mixing and packing the types and quantities of the seasonings and spices generated by the generation means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's society, where food waste is a problem, many households encounter the problem of seasonings and spices not being used up and reaching their expiration date. This wasteful waste has a significant impact on the environment and must be reduced to achieve sustainable consumption. Another issue is the effort required for users to find the right seasonings and spices for their dishes. The present invention aims to solve these problems by providing a system that prevents food waste by using only the amount needed and allows users to easily obtain the right seasonings and spices. [Means for solving the problem]

[0005] The present invention solves the above problems with a system including a means for receiving selection information regarding dishes and seasonings from a user terminal, a generation means for generating the types and amounts of seasonings and spices needed based on the selection information, and an automatic blending means for blending and packing the types and amounts of seasonings and spices generated by the generation means. In particular, by using an artificial intelligence model as the generation means, the types and amounts of seasonings and spices appropriate for a dish can be accurately calculated, and the automatic blending means blends and packs the required amounts, allowing users to use them without waste. Furthermore, by installing the automatic blending means in supermarkets, users can conveniently obtain seasonings and spices.

[0006] A "user terminal" is a device operated by a user to send and receive information via the Internet, etc., and includes smartphones and tablets.

[0007] "Selection information" is information generated when a user selects a specific dish and its seasoning, and is data that is sent to the server.

[0008] "Generation means" refers to a mechanism or algorithm for calculating and generating the types and amounts of seasonings and spices required based on input information.

[0009] "Automatic blending means" refers to a device or system for accurately blending and packing the types and amounts of seasonings and spices produced by the production means.

[0010] An "artificial intelligence model" is an algorithm trained to solve a specific problem, and is a technology for data analysis and prediction.

[0011] "Mixing and packing" refers to the process of mixing specified quantities of seasonings and spices and filling them into suitable containers or packages.

[0012] "Supermarket" refers to a large retail store selling food and daily necessities, where automated dispensing means are installed. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. The specific program processing of the system is described below.

[0035] System configuration

[0036] 1. User Device

[0037] It is a smartphone or tablet operated by the user, and sends information about food and seasoning selections to a server via an app.

[0038] 2. Server

[0039] The system analyzes the selection information received from the user's device and calculates the types and amounts of seasonings and spices required based on the generated AI model.

[0040] The calculation results are sent to the automatic compounding machine.

[0041] 3. Automatic blending machine

[0042] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[0043] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[0044] Program processing

[0045] User Device

[0046] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app sends the selection information to the server.

[0047] server

[0048] The server receives the selection information sent from the user's device. The server then analyzes the selection information and sends a request to the generative AI model. The AI ​​model calculates the types and amounts of seasonings and spices needed based on the specified dish and flavor. The calculation results are sent back to the server, which then sends the data to the automatic mixer.

[0049] Automatic blending machine

[0050] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[0051] Specific examples

[0052] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0053] In this way, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[0057] Step 2:

[0058] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app then sends this selection information to the server.

[0059] Step 3:

[0060] The server receives the selection information (food and seasoning) sent by the user, analyzes the received information, and creates a request to the generative AI model.

[0061] Step 4:

[0062] The server sends a request to the generative AI model, which receives the request and calculates the types and amounts of seasonings and spices needed based on the selected dish and flavor.

[0063] Step 5:

[0064] The generative AI model sends the calculation results (e.g., 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil) to the server. The server receives the calculation results and packs the data.

[0065] Step 6:

[0066] The server sends data to the automatic compounding machine, which reads the data received from the server and prepares compounding instructions.

[0067] Step 7:

[0068] The automatic mixing machine accurately measures the specified amount of seasonings (20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil) and places them in a mixing container.

[0069] Step 8:

[0070] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[0071] Step 9:

[0072] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[0073] Step 10:

[0074] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[0075] Through this series of steps, users can obtain the amount of seasonings and spices they need without waste, allowing them to use up all the seasonings and reduce food waste.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] With conventional seasoning and spice blends, it is difficult to quickly and accurately blend according to individual user needs, and it is also difficult to provide them in portions that do not go to waste. As a result, many households and restaurants have problems with surpluses or shortages of seasonings, resulting in food waste and increased cooking effort.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating prompt sentences based on the selection information using a generative AI model and receiving the calculation results, automatic mixing means for mixing and packing the types and amounts of seasonings and spices generated by the generating means, and means for providing seasonings and spices to the user using a QR code reader. This enables the fast and accurate provision of seasonings and spices according to the individual requests of the user, and ensures that amounts are provided without waste.

[0081] A "user terminal" is an electronic device such as a smartphone or tablet that allows a user to input information about food and seasoning selections.

[0082] "Selection information" refers to detailed information about the type of food and seasonings that the user inputs through the terminal.

[0083] The "generation means" is a mechanism for calculating the types and amounts of seasonings and spices required based on the selection information received from the user terminal.

[0084] A "generative AI model" is an artificial intelligence model used to calculate the types and amounts of seasonings and spices based on selected information.

[0085] A "prompt" is a document that allows a generative AI model to input selected information in a specific format.

[0086] The "automatic blending means" is a mechanism that blends specified seasonings and spices based on the calculation results from the server and packs them into small packages.

[0087] A "QR code reader" is a device that scans the generated QR code, allowing users to receive the seasonings and spices they have purchased.

[0088] "Commercial establishment" means a place where goods are generally sold, including supermarkets and other retail stores.

[0089] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. Specific embodiments of the system are described below.

[0090] System configuration

[0091] 1. User Device

[0092] The user operates a smartphone or tablet, which sends information about the food and seasoning selection to a server via an application. This application provides an interface for the user to select the name of the food, the strength of the seasoning, etc.

[0093] 2. Server

[0094] The server analyzes the selection information received from the user device and calculates the types and amounts of seasonings and spices required based on the generated AI model. Specifically, the server has the following functions:

[0095] Receiving selection information from the user device

[0096] Analyze the selection information and send a prompt to the generative AI model

[0097] Receiving and analyzing the results calculated by the generative AI model

[0098] Sending calculation results to the automatic compounding machine

[0099] 3. Automatic blending machine

[0100] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. The mixed and packed products can be smoothly collected by the user using a QR code. The automatic mixing machine is installed in retail stores such as commercial facilities.

[0101] Program processing flow

[0102] A user launches the app on their smartphone or tablet and selects their preferred dish and seasoning from the menu that appears. For example, the user selects mapo tofu and spicy. Once the user has completed their selection, the app sends the information to the server.

[0103] The server receives the selection information sent from the user device, then analyzes the data and sends a prompt to the generative AI model: "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasonings and spices needed."

[0104] Based on the prompts received, the generative AI model calculates the types and amounts of seasonings and spices that correspond to the specified dish and flavoring—for example, 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil, etc.—and sends the calculation results back to the server.

[0105] The server receives the calculation results from the AI ​​model and sends the data to the automatic blending machine. The automatic blending machine uses the data from the server to extract the required amounts of seasonings and spices and begin blending. For example, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes all the seasonings together. Once blending is complete, it packs them into individual packages.

[0106] Finally, users can take a QR code generated by the smartphone app and scan it into a QR code reader on an automated dispensing machine installed in a commercial facility to receive the finished, packaged condiments and spices.

[0107] Specific examples

[0108] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0109] Prompt Sentence Examples

[0110] Examples of specific prompts to input to a generative AI model include:

[0111] The user selected "Mapo Tofu" and "Spicy." Please calculate the type and amount of seasonings needed.

[0112] As described above, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] A user launches an app on their smartphone or tablet and selects their preferred dish and its seasoning from the menu. Specifically, the user opens the app, taps "Mapo Tofu" from the "Chinese Food" category, and then selects "Spicy." The entered information is the dish name "Mapo Tofu" and the seasoning "Spicy," and this selection information is sent from the app to the server.

[0116] Step 2:

[0117] The server receives the selection information sent from the user's device. The server analyzes the received information (dish name and seasoning) and generates a prompt for the generative AI model. For example, it creates a prompt such as, "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasoning needed." After this prompt is generated, the server sends it to the generative AI model.

[0118] Step 3:

[0119] Based on the prompt received from the server, the generative AI model calculates the type and amount of seasonings and spices that correspond to the specified dish and flavor. For example, it generates a calculation result such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." This calculation result is then sent back to the server from the generative AI model.

[0120] Step 4:

[0121] The server receives the calculation results from the generative AI model and sends the data to the automatic blending machine. Specifically, the calculation results sent are "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The automatic blending machine is then prepared to operate based on this data.

[0122] Step 5:

[0123] Based on the data received from the server, the automatic mixing machine mixes the required amount of specified seasonings and spices and packs them into small packages. Specifically, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes them together. After mixing, the mixed seasoning is packed into small packages.

[0124] Step 6:

[0125] The user takes a QR code generated by the smartphone app and scans it with the QR code reader of the automatic blending machine installed in the commercial facility. If the QR code is scanned correctly, the automatic blending machine will provide the blending package to the user. At this point, the user can receive the packaged seasonings and spices.

[0126] Through the above process steps, the present invention allows for the rapid and accurate provision of seasonings and spices according to the user's individual needs, and allows the user to obtain seasonings in amounts that are not wasted.

[0127] (Application example 1)

[0128] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0129] In recent years, home cooking has become more diverse, and users need to be able to quickly obtain the right amount of seasonings and spices to suit their preferences. However, individually preparing many different seasonings and spices is time-consuming and labor-intensive, and can also result in food waste. Furthermore, there is a lack of automated methods for blending seasonings to suit specific dishes and flavors. Therefore, there is a need for a system that can quickly provide the right amount of seasonings and spices based on the user's selection, thereby reducing waste.

[0130] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0131] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating the types and amounts of required seasonings and spices based on the selection information, means for automatically mixing and packing the types and amounts of seasonings and spices generated by the means for mixing, means for generating and displaying an identification code for receiving the mixed and packed seasonings and spices, and means for identifying and providing the seasonings and spices using the identification code, thereby enabling users to quickly obtain the required amounts of seasonings and spices according to their preferences without waste.

[0132] A "user terminal" is a device operated by a user, including a smartphone or tablet.

[0133] "Selection information regarding dish and seasoning" is information about the type of dish and its seasoning selected by the user.

[0134] The "generation means" is a technical means for calculating the type and amount of required seasonings and spices based on the selected information.

[0135] An "artificial intelligence model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new data.

[0136] "Mixing and packing means" means equipment for mixing and packaging specified seasonings and spices in prescribed quantities.

[0137] "Automatic blending means" refers to a machine that automatically blends and packs the necessary seasonings and spices.

[0138] "Retail store" refers to any store that sells goods to the general public.

[0139] An "identification code" is a code used to identify a specific product or information, and includes QR codes and barcodes.

[0140] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. As a specific program process of the system of the present invention, a method for linking the user terminal, the server, and the automatic mixer will be described below.

[0141] System configuration and functions

[0142] 1. User Device

[0143] The user terminal is a smartphone or tablet, and through a dedicated app, the user can input information about the food and seasonings they choose, which is then sent to the server.

[0144] 2. Server

[0145] The server analyzes the selection information received from the user's device and uses a generative AI model to calculate the types and amounts of seasonings and spices needed. The results are then sent to the automatic mixer.

[0146] 3. Automatic blending machine

[0147] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. This device is installed in retail stores, and users can receive their products using a QR code.

[0148] Hardware and software examples

[0149] Hardware

[0150] Smartphone (user device)

[0151] Cloud Server (Server)

[0152] Automatic mixing machine (equipped with a microcontroller such as Raspberry Pi)

[0153] QR Code Reader

[0154] software

[0155] Smartphone app development frameworks (React Native, Flutter, etc.)

[0156] Server-side frameworks (Node.js, Express)

[0157] Generative AI models (e.g., GPT-4)

[0158] Database systems (SQL, NoSQL)

[0159] Explanation of program processing

[0160] 1. User terminal processing

[0161] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[0162] The selected information is saved as form input in the app and a POST request is sent to the server.

[0163] 2. Server Processing

[0164] The server receives the POST request and parses the entered selections.

[0165] The preprocessed information is sent as prompts to a generative AI model (GPT-4) to calculate the types and amounts of seasonings and spices needed.

[0166] Example prompt sentence:

[0167] User selected dish: Mapo tofu

[0168] Preferred spice level: Medium

[0169] Provide the specific amounts and types of ingredients required.

[0170] The generative AI model responds by sending details of the seasonings and spices to the server, such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil."

[0171] 3. Automatic compounding machine processing

[0172] The server sends the data returned by the AI ​​model to the automatic compounding machine.

[0173] Based on the received data, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[0174] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[0175] Specific examples

[0176] For example, if a user wants to cook mapo tofu with a "medium" level of spiciness, the selection information "mapo tofu" and "medium" is sent to the server. The server then sends a prompt to the generative AI model to calculate the type and amount of seasonings and spices needed. The calculation results (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil") are sent to the automatic blending machine, which blends and packs the food. The user can then receive the blended seasonings and spices by scanning the QR code on the automatic blending machine.

[0177] Thus, by using the system of the present invention, the user can obtain seasonings and spices quickly and without waste.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[0181] Input: Dish "Mapo Tofu" and seasoning "Medium spicy"

[0182] Output: Selection information ready to send

[0183] What happens: The user selects a menu in the app and presses the select button, which saves the input information and prepares it to be sent to the server.

[0184] Step 2:

[0185] The terminal sends the selected information to the server as a POST request.

[0186] Input: Selection information (dish "Mapo tofu", seasoning "medium spicy")

[0187] Output: Information sent to the server

[0188] What happens: Once the user confirms their selection, the app sends a POST request to the server.

[0189] Step 3:

[0190] The server receives the POST request and parses the entered selections.

[0191] Input: User selection information

[0192] Output: Parsed selection information

[0193] What happens: The server parses the POST request and extracts the selected dish and seasoning information.

[0194] Step 4:

[0195] The server sends the preprocessed information as prompts to the generative AI model, which calculates the types and amounts of seasonings and spices needed.

[0196] Input: Prompt text (e.g., "User selected dish: Mapo tofu\nPreferred spice level: Medium\nProvide the specific amounts and types of ingredients required.")

[0197] Output: Calculated seasoning and spice types and amounts

[0198] How it works: The server sends a prompt to the generative AI model (GPT-4) and receives the required amount of seasonings and spices in text format.

[0199] Step 5:

[0200] The server analyzes the data returned from the AI ​​model and sends it to the automatic mixer.

[0201] Input: AI model output data (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil")

[0202] Output: Ingredient and spice data sent to the automatic mixer

[0203] Specific operation: The server analyzes the output data of the AI ​​model, converts it into a format that the automatic mixer can understand, and sends it.

[0204] Step 6:

[0205] Based on the data received, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[0206] Input: Condiment and spice data sent from the server

[0207] Output: Mixed and packaged seasonings and spices

[0208] Specific operation: The automatic mixing machine uses motors and measuring devices to accurately measure and pack seasonings.

[0209] Step 7:

[0210] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[0211] Input: Finished seasoning and spice packaging information

[0212] Output: QR code displayed on the user's device

[0213] How it works: The automatic dispensing machine generates a QR code based on the package information, which is then immediately displayed on the user's device.

[0214] Step 8:

[0215] Users scan the generated QR code with a QR code reader installed in the automatic mixing machine to receive packaged seasonings and spices.

[0216] Input: QR code displayed on the user's device

[0217] Output: Condiments and spices received

[0218] How it works: The user scans the QR code with a QR code reader and receives packaged condiments and spices from the automatic dispensing machine.

[0219] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0220] The present invention combines a system including a user terminal, a server, and an automatic mixer with an emotion engine that recognizes the user's emotions, and mixes and packs seasonings and spices based on the user's selection. The specific program processing of the system is described below.

[0221] System configuration

[0222] 1. User Device

[0223] It is a smartphone or tablet operated by the user, and sends the emotion information recognized by the emotion engine along with the food and seasoning selection information to the server via the app.

[0224] 2. Server

[0225] The system analyzes the selection and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on a generative AI model.

[0226] The calculation results are sent to the automatic compounding machine.

[0227] 3. Automatic blending machine

[0228] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[0229] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[0230] 4. Emotion Engine

[0231] It is built into the user's device and recognizes the user's emotions by analyzing facial recognition data, voice data, and behavioral data.

[0232] The user's selection information is corrected based on the recognized emotion.

[0233] Program processing

[0234] User Device

[0235] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app's built-in emotion engine recognizes the user's emotion (e.g., happiness, anger, sadness, etc.) and sends that information to the server.

[0236] server

[0237] The server receives the selection information and emotion information sent from the user's device. The server then analyzes the selection information and emotion information and makes a request to the generative AI model. The AI ​​model calculates the type and amount of seasonings and spices needed based on the specified dish, seasoning, and the user's emotion. For example, if the user is angry, it will make adjustments such as increasing the spiciness. The calculation results are returned to the server, which then sends the data to the automatic mixer.

[0238] Automatic blending machine

[0239] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[0240] Specific examples

[0241] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app's built-in emotion engine recognizes the user's emotions and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the received information and calculates the required amount of seasonings and spices based on the generative AI model. For example, the server calculates "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil" to increase the spiciness. This calculation result is then sent from the server to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0242] In this way, the present invention allows users to quickly and efficiently obtain seasonings and spices that suit their mood at the time, thereby reducing food waste and improving the user experience.

[0243] The processing flow will be explained below.

[0244] Step 1:

[0245] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[0246] Step 2:

[0247] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app recognizes this selection information.

[0248] Step 3:

[0249] The emotion engine built into the user device analyzes the user's facial recognition data, voice data, or behavioral data to detect whether the user is currently angry, and sends this emotion information along with the selection information to the server.

[0250] Step 4:

[0251] The server receives the selection and emotion information sent by the user, analyzes the received information, and makes a request to the generative AI model.

[0252] Step 5:

[0253] The server sends a request to the generative AI model, which receives the request and calculates the type and amount of seasonings and spices needed based on the selected dish (mapo tofu), seasoning (spicy), and the user's emotion (anger).

[0254] Step 6:

[0255] The generative AI model sends the calculated results (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil) to the server. The calculated results are spicier than the usual amounts.

[0256] Step 7:

[0257] The server receives the calculation results, packs the data, and sends the packed data to the automatic compounding machine.

[0258] Step 8:

[0259] The automatic mixing machine reads the data received from the server and prepares the mixing instructions. It accurately measures the specified amount of seasonings (25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil) and places them in the mixing container.

[0260] Step 9:

[0261] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[0262] Step 10:

[0263] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[0264] Step 11:

[0265] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[0266] Through this series of steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time, reducing food waste and improving the user experience.

[0267] Example 2

[0268] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] In modern society, detailed customization is required to meet the diverse seasoning and spice needs of users. However, conventional methods have had challenges in generating seasonings that take the user's feelings into account and in quickly packaging them. It has also been difficult to provide seasonings in the right amount without waste. For this reason, a system that can improve the user experience while reducing food waste is needed.

[0270] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0271] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for collecting user emotion information using an emotion engine built into the user terminal, means for generating the types and amounts of seasonings and spices required based on the selection information and emotion information, and automatic blending means for blending and packing the types and amounts of seasonings and spices generated by the generation means, thereby making it possible to provide customized seasonings and spices that take into account the user's emotion information.

[0272] A "user terminal" is a device operated by a user, such as a smartphone or tablet, that has the function of transmitting cooking and seasoning information, as well as emotional information, to a server.

[0273] An "emotion engine" is software built into the user's device that has the function of recognizing emotional information by analyzing the user's facial expressions and voice.

[0274] "Selection information" refers to information about the food and its seasonings selected by the user using the terminal.

[0275] "Emotional information" refers to information about the user's emotional state as recognized by the emotion engine.

[0276] The "generation means" is a function incorporated in the server, and has the function of calculating and generating the types and amounts of seasonings and spices based on the selection information and emotional information.

[0277] "Artificial intelligence model" refers to an algorithm or system that uses adaptive learning techniques to predict or generate results based on specified conditions.

[0278] "Automatic blending means" refers to an automated machine or the like that actually blends and packs the seasonings and spices produced by the production means.

[0279] "Retail store" refers to a facility where users ultimately purchase and receive products, and is a store that provides products to general consumers.

[0280] This invention incorporates an emotion engine that recognizes the user's emotions into a system that combines a user terminal, a server, and an automatic mixer. This system makes it possible to customize, mix, and pack seasonings and spices based on the user's selection information and emotional information.

[0281] System configuration

[0282] User Device

[0283] The user device is a smartphone or tablet that the user operates. A dedicated application and emotion engine are installed on this device, which analyzes the user's facial expressions and voice. Through the application, the user selects their preferred dish and seasoning, and sends this along with the emotion information recognized by the emotion engine to the server. Examples of hardware used include iPhones and Android smartphones. The software used is a dedicated application and emotion recognition engine (for example, Microsoft Azure Face API).

[0284] server

[0285] The server analyzes the selection information and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on the generative AI model. Python and TensorFlow are used as data analysis tools for this process. The calculation results are sent to the automatic blending machine. As a specific example of operation, the prompt "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the types and amounts of seasonings needed" is input into the generative AI model, which then outputs the appropriate types and amounts. The hardware used can be, for example, an EC2 instance from Amazon Web Services (AWS).

[0286] Automatic blending machine

[0287] An automatic mixer is a device that mixes the necessary seasonings and spices based on data received from a server and packs them into small packages. It is installed in retail stores and supermarkets, and users receive products using a set QR code. Based on data sent from the server, the automatic mixer measures out the appropriate amount of seasonings, such as chili bean paste, soy sauce, sugar, and chili oil, and seals them into packages. The hardware used includes dedicated automatic mixers. The software includes control software (e.g., embedded Linux) for executing instructions from the server.

[0288] Specific examples

[0289] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. After confirming this selection, the user's device's built-in emotion engine recognizes the user's emotion and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the information and calculates the appropriate amount of seasonings and spices based on a generative AI model. For example, the calculation results may be "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil." The calculation results are then sent from the server to an automatic blender, which blends and packs the necessary seasonings and spices. The user can receive the packaged product by scanning the QR code generated by the smartphone app with a QR code reader. In this way, users can quickly obtain the seasonings and spices that best suit their current emotion without waste. This reduces food waste and improves the user experience.

[0290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0291] Program processing flow

[0292] Step 1: User selection and emotional information collection

[0293] User Device

[0294] Input: The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. The user specifically selects "Mapo tofu" and "spicy."

[0295] Processing: Once the selection is complete, the app's built-in emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice. The emotion engine recognizes the user's emotional state and records it as data.

[0296] Output: Selection information (food: mapo tofu, seasoning: spicy) and emotion information (emotion: anger)

[0297] Step 2: Sending selection and emotion information

[0298] User Device

[0299] Input: Choice information and emotion information collected in step 1

[0300] Processing: The user terminal sends the selected dish and seasoning, as well as the recognized emotion information, to the server.

[0301] Output: Selection information and emotion information sent to the server

[0302] Step 3: Analyze the information and generate instructions

[0303] server

[0304] Input: Selection information and emotion information sent from the user device

[0305] Processing: The server analyzes the selection and emotion information, then creates a prompt for the generative AI model and sends the request.

[0306] Prompt: "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the type and amount of seasoning needed."

[0307] A generative AI model calculates the appropriate types and amounts of seasonings and spices based on prompts.

[0308] Output: Calculation result (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil)

[0309] Step 4: Mixing and packaging seasonings and spices

[0310] Automatic blending machine

[0311] Input: Calculation results sent from the server (types and amounts of seasonings and spices)

[0312] Processing: The automatic mixer mixes the required amount of seasonings and spices.

[0313] Specifically, the condiments are measured and mixed in order according to the instructions, such as measuring 25g of chili bean paste, then 15ml of soy sauce.

[0314] The blended seasonings and spices are packaged in small portions.

[0315] Output: Mixed and packed small packages

[0316] Step 5: Receive your product via QR code

[0317] User terminal, user

[0318] Input: QR code generated by the user on their smartphone app

[0319] Process: The user scans the QR code with the QR code reader installed on the automatic dispensing machine.

[0320] Output: Mixed and packaged seasonings and spices provided by the automatic mixer

[0321] Through these steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time.

[0322] (Application example 2)

[0323] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0324] Currently, most automated mixing systems only mix seasonings and spices based on user-input selection information. As a result, they do not take into account the user's emotions or psychological state, making it impossible to provide a fully personalized dining experience. Furthermore, there is no mechanism for the system to automatically suggest seasonings that suit the user's emotions, leaving room for further improvements in user satisfaction.

[0325] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving selection information related to dishes and seasonings from a user terminal, emotion analysis means for analyzing the user's emotional information and correcting the selection information, generation means for generating the types and amounts of necessary seasonings and spices based on the selection information and emotional information, and means for transmitting the generated types and amounts of seasonings and spices to the automatic blending machine. This makes it possible to blend seasonings and spices individually according to the user's emotional state.

[0326] A "user terminal" is an electronic device operated by a user, including a smartphone, tablet, or PC.

[0327] "Selection information regarding dish and seasoning" is information regarding the type of dish and its seasoning selected by the user, and is data indicating the cooking result desired by the user.

[0328] The "emotion analysis means" is a function for recognizing the user's emotions by analyzing the user's facial recognition data, voice data, and behavioral data.

[0329] The "generation means" is a function for calculating and generating the types and amounts of seasonings and spices required based on the selection information and emotional information.

[0330] "Automatic blending means" refers to a mechanical device that actually mixes the types and quantities of seasonings and spices produced and fills them into appropriate packages.

[0331] An "artificial intelligence model" is a model that operates based on machine learning algorithms and is trained to perform a specific task (in this case, calculating quantities of seasonings and spices).

[0332] "Retail establishment" is a general term for places such as supermarkets, convenience stores, and shopping malls where consumers can purchase goods.

[0333] An embodiment of the present invention will now be described in detail.

[0334] System Configuration

[0335] This system mainly consists of a user terminal, a server, and an automatic mixing machine. It also includes an emotion analysis means for recognizing the user's emotions.

[0336] User Device

[0337] The user device is an electronic device such as a smartphone or tablet, on which a dedicated application is installed. The user launches the application and selects the dish and seasoning. Furthermore, the device's built-in camera and microphone are used to capture the user's facial recognition data and voice data.

[0338] server

[0339] The server performs the following functions:

[0340] 1. Information receiving means: receives selection information and emotion information transmitted from the user terminal.

[0341] 2. Emotion analysis means: Analyze the user's emotions based on the received emotional information.

[0342] 3. Generation method: Based on emotional and selection information, a generative AI model is used to calculate the types and amounts of seasonings and spices needed.

[0343] 4. Data transmission means: Transmits the calculation results to the automatic compounding machine.

[0344] Hardware and software used

[0345] User device: iOS or Android device

[0346] Server: Cloud server (e.g. AWS, Google Cloud)

[0347] Sentiment analysis methods: Microsoft Azure Cognitive Services, Amazon Rekognition, Google Cloud Vision, etc.

[0348] Generative AI models: OpenAI GPT-3, etc.

[0349] Automatic mixer: Automatic seasoning mixer, QR code reader, control microcomputer

[0350] Specific examples

[0351] For example, a user visits a brick-and-mortar store and launches the smartphone app. They select "special ramen" and "spicy" from the menu, then scan their face using the app's camera. The system uses emotion analysis to recognize that the user is smiling. This information is sent to a server, which uses a generative AI model to calculate the appropriate types and amounts of seasonings and spices. In this case, the calculated result is "400ml of ramen soup, 20ml of soy sauce, and 5ml of chili oil." The information is then sent to an automatic blending machine, which blends the ramen.

[0352] Prompt Sentence Examples

[0353] 1. Prompt for sentiment analysis program:

[0354] image_data: user_face_image

[0355] audio_data: user_voice_recording

[0356] environment: inside_store

[0357] 2. Prompt for the generative AI model:

[0358] selected_dish: Special Ramen

[0359] taste_preference: spicy

[0360] user_emotion: happiness

[0361] In this way, personalized seasoning and spice blends can be created that take into account the user's emotional state and preferences, resulting in increased user satisfaction and a more personalized dining experience.

[0362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0363] Step 1:

[0364] A user visits a physical store and launches the smartphone app. The user selects a dish (e.g., special ramen) and its seasoning (e.g., spicy) from the app's menu and enters their selection information. This selection information is saved on the device and used in the next step.

[0365] Step 2:

[0366] After the user inputs the selection information, the terminal uses a built-in camera and microphone to acquire the user's facial recognition data and voice data, thereby obtaining input data for the emotion analysis means to analyze the user's emotions.

[0367] Step 3:

[0368] The emotion analysis means built into the device analyzes the facial recognition data and voice data to recognize the user's emotional state (e.g., happiness). The emotion analysis means obtains emotion data using APIs such as Microsoft Azure Cognitive Services and Amazon Rekognition. The analysis results (emotional state) are stored on the device.

[0369] Step 4:

[0370] The terminal transmits the selection information (food and seasoning) and the analysis result (emotional state) together to the server. This transmitted data is formatted as a prompt sentence including the selection information and the emotion information.

[0371] Step 5:

[0372] The server analyzes the received selection information and emotional information and generates a prompt for the generative AI model. The prompt is sent to the generative AI model (e.g., OpenAI GPT-3) to calculate the type and amount of seasonings and spices needed. The dish, seasoning, and emotional state are input into this calculation.

[0373] Step 6:

[0374] The generative AI model responds by returning the appropriate types and amounts of seasonings and spices (e.g., 400ml of ramen soup, 20ml of soy sauce, 5ml of chili oil). The server receives this calculation result.

[0375] Step 7:

[0376] The server transmits the received data on the types and amounts of seasonings and spices to the automatic blending unit, which then blends the actual seasonings and spices based on the data and fills them into appropriate packages. This blending process is managed by a control microcomputer.

[0377] Step 8:

[0378] Once the automated dispensing means has completed dispensing and packing, the generated package is ready for collection by the user, who can use the QR code to collect the generated package from a dedicated machine in the physical store.

[0379] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0380] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0382] [Second embodiment]

[0383] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0384] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0385] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0387] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0389] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0390] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0391] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0392] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0394] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0395] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. The specific program processing of the system is described below.

[0396] System configuration

[0397] 1. User Device

[0398] It is a smartphone or tablet operated by the user, and sends information about food and seasoning selections to a server via an app.

[0399] 2. Server

[0400] The system analyzes the selection information received from the user's device and calculates the types and amounts of seasonings and spices required based on the generated AI model.

[0401] The calculation results are sent to the automatic compounding machine.

[0402] 3. Automatic blending machine

[0403] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[0404] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[0405] Program processing

[0406] User Device

[0407] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app sends the selection information to the server.

[0408] server

[0409] The server receives the selection information sent from the user's device. The server then analyzes the selection information and sends a request to the generative AI model. The AI ​​model calculates the types and amounts of seasonings and spices needed based on the specified dish and flavor. The calculation results are sent back to the server, which then sends the data to the automatic mixer.

[0410] Automatic blending machine

[0411] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[0412] Specific examples

[0413] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0414] In this way, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[0415] The processing flow will be explained below.

[0416] Step 1:

[0417] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[0418] Step 2:

[0419] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app then sends this selection information to the server.

[0420] Step 3:

[0421] The server receives the selection information (food and seasoning) sent by the user, analyzes the received information, and creates a request to the generative AI model.

[0422] Step 4:

[0423] The server sends a request to the generative AI model, which receives the request and calculates the types and amounts of seasonings and spices needed based on the selected dish and flavor.

[0424] Step 5:

[0425] The generative AI model sends the calculation results (e.g., 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil) to the server. The server receives the calculation results and packs the data.

[0426] Step 6:

[0427] The server sends data to the automatic compounding machine, which reads the data received from the server and prepares compounding instructions.

[0428] Step 7:

[0429] The automatic mixing machine accurately measures the specified amount of seasonings (20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil) and places them in a mixing container.

[0430] Step 8:

[0431] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[0432] Step 9:

[0433] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[0434] Step 10:

[0435] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[0436] Through this series of steps, users can obtain the amount of seasonings and spices they need without waste, allowing them to use up all the seasonings and reduce food waste.

[0437] Example 1

[0438] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0439] With conventional seasoning and spice blends, it is difficult to quickly and accurately blend according to individual user needs, and it is also difficult to provide them in portions that do not go to waste. As a result, many households and restaurants have problems with surpluses or shortages of seasonings, resulting in food waste and increased cooking effort.

[0440] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0441] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating prompt sentences based on the selection information using a generative AI model and receiving the calculation results, automatic mixing means for mixing and packing the types and amounts of seasonings and spices generated by the generating means, and means for providing seasonings and spices to the user using a QR code reader. This enables the fast and accurate provision of seasonings and spices according to the individual requests of the user, and ensures that amounts are provided without waste.

[0442] A "user terminal" is an electronic device such as a smartphone or tablet that allows a user to input information about food and seasoning selections.

[0443] "Selection information" refers to detailed information about the type of food and seasonings that the user inputs through the terminal.

[0444] The "generation means" is a mechanism for calculating the types and amounts of seasonings and spices required based on the selection information received from the user terminal.

[0445] A "generative AI model" is an artificial intelligence model used to calculate the types and amounts of seasonings and spices based on selected information.

[0446] A "prompt" is a document that allows a generative AI model to input selected information in a specific format.

[0447] The "automatic blending means" is a mechanism that blends specified seasonings and spices based on the calculation results from the server and packs them into small packages.

[0448] A "QR code reader" is a device that scans the generated QR code, allowing users to receive the seasonings and spices they have purchased.

[0449] "Commercial establishment" means a place where goods are generally sold, including supermarkets and other retail stores.

[0450] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. Specific embodiments of the system are described below.

[0451] System configuration

[0452] 1. User Device

[0453] The user operates a smartphone or tablet, which sends information about the food and seasoning selection to a server via an application. This application provides an interface for the user to select the name of the food, the strength of the seasoning, etc.

[0454] 2. Server

[0455] The server analyzes the selection information received from the user device and calculates the types and amounts of seasonings and spices required based on the generated AI model. Specifically, the server has the following functions:

[0456] Receiving selection information from the user device

[0457] Analyze the selection information and send a prompt to the generative AI model

[0458] Receiving and analyzing the results calculated by the generative AI model

[0459] Sending calculation results to the automatic compounding machine

[0460] 3. Automatic blending machine

[0461] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. The mixed and packed products can be smoothly collected by the user using a QR code. The automatic mixing machine is installed in retail stores such as commercial facilities.

[0462] Program processing flow

[0463] A user launches the app on their smartphone or tablet and selects their preferred dish and seasoning from the menu that appears. For example, the user selects mapo tofu and spicy. Once the user has completed their selection, the app sends the information to the server.

[0464] The server receives the selection information sent from the user device, then analyzes the data and sends a prompt to the generative AI model: "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasonings and spices needed."

[0465] Based on the prompts received, the generative AI model calculates the types and amounts of seasonings and spices that correspond to the specified dish and flavoring—for example, 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil, etc.—and sends the calculation results back to the server.

[0466] The server receives the calculation results from the AI ​​model and sends the data to the automatic blending machine. The automatic blending machine uses the data from the server to extract the required amounts of seasonings and spices and begin blending. For example, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes all the seasonings together. Once blending is complete, it packs them into individual packages.

[0467] Finally, users can take a QR code generated by the smartphone app and scan it into a QR code reader on an automated dispensing machine installed in a commercial facility to receive the finished, packaged condiments and spices.

[0468] Specific examples

[0469] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0470] Prompt Sentence Examples

[0471] Examples of specific prompts to input to a generative AI model include:

[0472] The user selected "Mapo Tofu" and "Spicy." Please calculate the type and amount of seasonings needed.

[0473] As described above, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[0474] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0475] Step 1:

[0476] A user launches an app on their smartphone or tablet and selects their preferred dish and its seasoning from the menu. Specifically, the user opens the app, taps "Mapo Tofu" from the "Chinese Food" category, and then selects "Spicy." The entered information is the dish name "Mapo Tofu" and the seasoning "Spicy," and this selection information is sent from the app to the server.

[0477] Step 2:

[0478] The server receives the selection information sent from the user's device. The server analyzes the received information (dish name and seasoning) and generates a prompt for the generative AI model. For example, it creates a prompt such as, "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasoning needed." After this prompt is generated, the server sends it to the generative AI model.

[0479] Step 3:

[0480] Based on the prompt received from the server, the generative AI model calculates the type and amount of seasonings and spices that correspond to the specified dish and flavor. For example, it generates a calculation result such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." This calculation result is then sent back to the server from the generative AI model.

[0481] Step 4:

[0482] The server receives the calculation results from the generative AI model and sends the data to the automatic blending machine. Specifically, the calculation results sent are "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The automatic blending machine is then prepared to operate based on this data.

[0483] Step 5:

[0484] Based on the data received from the server, the automatic mixing machine mixes the required amount of specified seasonings and spices and packs them into small packages. Specifically, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes them together. After mixing, the mixed seasoning is packed into small packages.

[0485] Step 6:

[0486] The user takes a QR code generated by the smartphone app and scans it with the QR code reader of the automatic blending machine installed in the commercial facility. If the QR code is scanned correctly, the automatic blending machine will provide the blending package to the user. At this point, the user can receive the packaged seasonings and spices.

[0487] Through the above process steps, the present invention allows for the rapid and accurate provision of seasonings and spices according to the user's individual needs, and allows the user to obtain seasonings in amounts that are not wasted.

[0488] (Application example 1)

[0489] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0490] In recent years, home cooking has become more diverse, and users need to be able to quickly obtain the right amount of seasonings and spices to suit their preferences. However, individually preparing many different seasonings and spices is time-consuming and labor-intensive, and can also result in food waste. Furthermore, there is a lack of automated methods for blending seasonings to suit specific dishes and flavors. Therefore, there is a need for a system that can quickly provide the right amount of seasonings and spices based on the user's selection, thereby reducing waste.

[0491] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0492] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating the types and amounts of required seasonings and spices based on the selection information, means for automatically mixing and packing the types and amounts of seasonings and spices generated by the means for mixing, means for generating and displaying an identification code for receiving the mixed and packed seasonings and spices, and means for identifying and providing the seasonings and spices using the identification code, thereby enabling users to quickly obtain the required amounts of seasonings and spices according to their preferences without waste.

[0493] A "user terminal" is a device operated by a user, including a smartphone or tablet.

[0494] "Selection information regarding dish and seasoning" is information about the type of dish and its seasoning selected by the user.

[0495] The "generation means" is a technical means for calculating the type and amount of required seasonings and spices based on the selected information.

[0496] An "artificial intelligence model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new data.

[0497] "Mixing and packing means" means equipment for mixing and packaging specified seasonings and spices in prescribed quantities.

[0498] "Automatic blending means" refers to a machine that automatically blends and packs the necessary seasonings and spices.

[0499] "Retail store" refers to any store that sells goods to the general public.

[0500] An "identification code" is a code used to identify a specific product or information, and includes QR codes and barcodes.

[0501] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. As a specific program process of the system of the present invention, a method for linking the user terminal, the server, and the automatic mixer will be described below.

[0502] System configuration and functions

[0503] 1. User Device

[0504] The user terminal is a smartphone or tablet, and through a dedicated app, the user can input information about the food and seasonings they choose, which is then sent to the server.

[0505] 2. Server

[0506] The server analyzes the selection information received from the user's device and uses a generative AI model to calculate the types and amounts of seasonings and spices needed. The results are then sent to the automatic mixer.

[0507] 3. Automatic blending machine

[0508] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. This device is installed in retail stores, and users can receive their products using a QR code.

[0509] Hardware and software examples

[0510] Hardware

[0511] Smartphone (user device)

[0512] Cloud Server (Server)

[0513] Automatic mixing machine (equipped with a microcontroller such as Raspberry Pi)

[0514] QR Code Reader

[0515] software

[0516] Smartphone app development frameworks (React Native, Flutter, etc.)

[0517] Server-side frameworks (Node.js, Express)

[0518] Generative AI models (e.g., GPT-4)

[0519] Database systems (SQL, NoSQL)

[0520] Explanation of program processing

[0521] 1. User terminal processing

[0522] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[0523] The selected information is saved as form input in the app and a POST request is sent to the server.

[0524] 2. Server Processing

[0525] The server receives the POST request and parses the entered selections.

[0526] The preprocessed information is sent as prompts to a generative AI model (GPT-4) to calculate the types and amounts of seasonings and spices needed.

[0527] Example prompt sentence:

[0528] User selected dish: Mapo tofu

[0529] Preferred spice level: Medium

[0530] Provide the specific amounts and types of ingredients required.

[0531] The generative AI model responds by sending details of the seasonings and spices to the server, such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil."

[0532] 3. Automatic compounding machine processing

[0533] The server sends the data returned by the AI ​​model to the automatic compounding machine.

[0534] Based on the received data, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[0535] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[0536] Specific examples

[0537] For example, if a user wants to cook mapo tofu with a "medium" level of spiciness, the selection information "mapo tofu" and "medium" is sent to the server. The server then sends a prompt to the generative AI model to calculate the type and amount of seasonings and spices needed. The calculation results (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil") are sent to the automatic blending machine, which blends and packs the food. The user can then receive the blended seasonings and spices by scanning the QR code on the automatic blending machine.

[0538] Thus, by using the system of the present invention, the user can obtain seasonings and spices quickly and without waste.

[0539] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0540] Step 1:

[0541] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[0542] Input: Dish "Mapo Tofu" and seasoning "Medium spicy"

[0543] Output: Selection information ready to send

[0544] What happens: The user selects a menu in the app and presses the select button, which saves the input information and prepares it to be sent to the server.

[0545] Step 2:

[0546] The terminal sends the selected information to the server as a POST request.

[0547] Input: Selection information (dish "Mapo tofu", seasoning "medium spicy")

[0548] Output: Information sent to the server

[0549] What happens: Once the user confirms their selection, the app sends a POST request to the server.

[0550] Step 3:

[0551] The server receives the POST request and parses the entered selections.

[0552] Input: User selection information

[0553] Output: Parsed selection information

[0554] What happens: The server parses the POST request and extracts the selected dish and seasoning information.

[0555] Step 4:

[0556] The server sends the preprocessed information as prompts to the generative AI model, which calculates the types and amounts of seasonings and spices needed.

[0557] Input: Prompt text (e.g., "User selected dish: Mapo tofu\nPreferred spice level: Medium\nProvide the specific amounts and types of ingredients required.")

[0558] Output: Calculated seasoning and spice types and amounts

[0559] How it works: The server sends a prompt to the generative AI model (GPT-4) and receives the required amount of seasonings and spices in text format.

[0560] Step 5:

[0561] The server analyzes the data returned from the AI ​​model and sends it to the automatic mixer.

[0562] Input: AI model output data (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil")

[0563] Output: Ingredient and spice data sent to the automatic mixer

[0564] Specific operation: The server analyzes the output data of the AI ​​model, converts it into a format that the automatic mixer can understand, and sends it.

[0565] Step 6:

[0566] Based on the data received, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[0567] Input: Condiment and spice data sent from the server

[0568] Output: Mixed and packaged seasonings and spices

[0569] Specific operation: The automatic mixing machine uses motors and measuring devices to accurately measure and pack seasonings.

[0570] Step 7:

[0571] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[0572] Input: Finished seasoning and spice packaging information

[0573] Output: QR code displayed on the user's device

[0574] How it works: The automatic dispensing machine generates a QR code based on the package information, which is then immediately displayed on the user's device.

[0575] Step 8:

[0576] Users scan the generated QR code with a QR code reader installed in the automatic mixing machine to receive packaged seasonings and spices.

[0577] Input: QR code displayed on the user's device

[0578] Output: Condiments and spices received

[0579] How it works: The user scans the QR code with a QR code reader and receives packaged condiments and spices from the automatic dispensing machine.

[0580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0581] The present invention combines a system including a user terminal, a server, and an automatic mixer with an emotion engine that recognizes the user's emotions, and mixes and packs seasonings and spices based on the user's selection. The specific program processing of the system is described below.

[0582] System configuration

[0583] 1. User Device

[0584] It is a smartphone or tablet operated by the user, and sends the emotion information recognized by the emotion engine along with the food and seasoning selection information to the server via the app.

[0585] 2. Server

[0586] The system analyzes the selection and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on a generative AI model.

[0587] The calculation results are sent to the automatic compounding machine.

[0588] 3. Automatic blending machine

[0589] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[0590] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[0591] 4. Emotion Engine

[0592] It is built into the user's device and recognizes the user's emotions by analyzing facial recognition data, voice data, and behavioral data.

[0593] The user's selection information is corrected based on the recognized emotion.

[0594] Program processing

[0595] User Device

[0596] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app's built-in emotion engine recognizes the user's emotion (e.g., happiness, anger, sadness, etc.) and sends that information to the server.

[0597] server

[0598] The server receives the selection information and emotion information sent from the user's device. The server then analyzes the selection information and emotion information and makes a request to the generative AI model. The AI ​​model calculates the type and amount of seasonings and spices needed based on the specified dish, seasoning, and the user's emotion. For example, if the user is angry, it will make adjustments such as increasing the spiciness. The calculation results are returned to the server, which then sends the data to the automatic mixer.

[0599] Automatic blending machine

[0600] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[0601] Specific examples

[0602] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app's built-in emotion engine recognizes the user's emotions and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the received information and calculates the required amount of seasonings and spices based on the generative AI model. For example, the server calculates "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil" to increase the spiciness. This calculation result is then sent from the server to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0603] In this way, the present invention allows users to quickly and efficiently obtain seasonings and spices that suit their mood at the time, thereby reducing food waste and improving the user experience.

[0604] The processing flow will be explained below.

[0605] Step 1:

[0606] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[0607] Step 2:

[0608] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app recognizes this selection information.

[0609] Step 3:

[0610] The emotion engine built into the user device analyzes the user's facial recognition data, voice data, or behavioral data to detect whether the user is currently angry, and sends this emotion information along with the selection information to the server.

[0611] Step 4:

[0612] The server receives the selection and emotion information sent by the user, analyzes the received information, and makes a request to the generative AI model.

[0613] Step 5:

[0614] The server sends a request to the generative AI model, which receives the request and calculates the type and amount of seasonings and spices needed based on the selected dish (mapo tofu), seasoning (spicy), and the user's emotion (anger).

[0615] Step 6:

[0616] The generative AI model sends the calculated results (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil) to the server. The calculated results are spicier than the usual amounts.

[0617] Step 7:

[0618] The server receives the calculation results, packs the data, and sends the packed data to the automatic compounding machine.

[0619] Step 8:

[0620] The automatic mixing machine reads the data received from the server and prepares the mixing instructions. It accurately measures the specified amount of seasonings (25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil) and places them in the mixing container.

[0621] Step 9:

[0622] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[0623] Step 10:

[0624] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[0625] Step 11:

[0626] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[0627] Through this series of steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time, reducing food waste and improving the user experience.

[0628] Example 2

[0629] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0630] In modern society, detailed customization is required to meet the diverse seasoning and spice needs of users. However, conventional methods have had challenges in generating seasonings that take the user's feelings into account and in quickly packaging them. It has also been difficult to provide seasonings in the right amount without waste. For this reason, a system that can improve the user experience while reducing food waste is needed.

[0631] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0632] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for collecting user emotion information using an emotion engine built into the user terminal, means for generating the types and amounts of seasonings and spices required based on the selection information and emotion information, and automatic blending means for blending and packing the types and amounts of seasonings and spices generated by the generation means, thereby making it possible to provide customized seasonings and spices that take into account the user's emotion information.

[0633] A "user terminal" is a device operated by a user, such as a smartphone or tablet, that has the function of transmitting cooking and seasoning information, as well as emotional information, to a server.

[0634] An "emotion engine" is software built into the user's device that has the function of recognizing emotional information by analyzing the user's facial expressions and voice.

[0635] "Selection information" refers to information about the food and its seasonings selected by the user using the terminal.

[0636] "Emotional information" refers to information about the user's emotional state as recognized by the emotion engine.

[0637] The "generation means" is a function incorporated in the server, and has the function of calculating and generating the types and amounts of seasonings and spices based on the selection information and emotional information.

[0638] "Artificial intelligence model" refers to an algorithm or system that uses adaptive learning techniques to predict or generate results based on specified conditions.

[0639] "Automatic blending means" refers to an automated machine or the like that actually blends and packs the seasonings and spices produced by the production means.

[0640] "Retail store" refers to a facility where users ultimately purchase and receive products, and is a store that provides products to general consumers.

[0641] This invention incorporates an emotion engine that recognizes the user's emotions into a system that combines a user terminal, a server, and an automatic mixer. This system makes it possible to customize, mix, and pack seasonings and spices based on the user's selection information and emotional information.

[0642] System configuration

[0643] User Device

[0644] The user device is a smartphone or tablet that the user operates. A dedicated application and emotion engine are installed on this device, which analyzes the user's facial expressions and voice. Through the application, the user selects their preferred dish and seasoning, and sends this along with the emotion information recognized by the emotion engine to the server. Examples of hardware used include iPhones and Android smartphones. The software used is a dedicated application and emotion recognition engine (for example, Microsoft Azure Face API).

[0645] server

[0646] The server analyzes the selection information and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on the generative AI model. Python and TensorFlow are used as data analysis tools for this process. The calculation results are sent to the automatic blending machine. As a specific example of operation, the prompt "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the types and amounts of seasonings needed" is input into the generative AI model, which then outputs the appropriate types and amounts. The hardware used can be, for example, an EC2 instance from Amazon Web Services (AWS).

[0647] Automatic blending machine

[0648] An automatic mixer is a device that mixes the necessary seasonings and spices based on data received from a server and packs them into small packages. It is installed in retail stores and supermarkets, and users receive products using a set QR code. Based on data sent from the server, the automatic mixer measures out the appropriate amount of seasonings, such as chili bean paste, soy sauce, sugar, and chili oil, and seals them into packages. The hardware used includes dedicated automatic mixers. The software includes control software (e.g., embedded Linux) for executing instructions from the server.

[0649] Specific examples

[0650] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. After confirming this selection, the user's device's built-in emotion engine recognizes the user's emotion and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the information and calculates the appropriate amount of seasonings and spices based on a generative AI model. For example, the calculation results may be "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil." The calculation results are then sent from the server to an automatic blender, which blends and packs the necessary seasonings and spices. The user can receive the packaged product by scanning the QR code generated by the smartphone app with a QR code reader. In this way, users can quickly obtain the seasonings and spices that best suit their current emotion without waste. This reduces food waste and improves the user experience.

[0651] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0652] Program processing flow

[0653] Step 1: User selection and emotional information collection

[0654] User Device

[0655] Input: The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. The user specifically selects "Mapo tofu" and "spicy."

[0656] Processing: Once the selection is complete, the app's built-in emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice. The emotion engine recognizes the user's emotional state and records it as data.

[0657] Output: Selection information (food: mapo tofu, seasoning: spicy) and emotion information (emotion: anger)

[0658] Step 2: Sending selection and emotion information

[0659] User Device

[0660] Input: Choice information and emotion information collected in step 1

[0661] Processing: The user terminal sends the selected dish and seasoning, as well as the recognized emotion information, to the server.

[0662] Output: Selection information and emotion information sent to the server

[0663] Step 3: Analyze the information and generate instructions

[0664] server

[0665] Input: Selection information and emotion information sent from the user device

[0666] Processing: The server analyzes the selection and emotion information, then creates a prompt for the generative AI model and sends the request.

[0667] Prompt: "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the type and amount of seasoning needed."

[0668] A generative AI model calculates the appropriate types and amounts of seasonings and spices based on prompts.

[0669] Output: Calculation result (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil)

[0670] Step 4: Mixing and packaging seasonings and spices

[0671] Automatic blending machine

[0672] Input: Calculation results sent from the server (types and amounts of seasonings and spices)

[0673] Processing: The automatic mixer mixes the required amount of seasonings and spices.

[0674] Specifically, the condiments are measured and mixed in order according to the instructions, such as measuring 25g of chili bean paste, then 15ml of soy sauce.

[0675] The blended seasonings and spices are packaged in small portions.

[0676] Output: Mixed and packed small packages

[0677] Step 5: Receive your product via QR code

[0678] User terminal, user

[0679] Input: QR code generated by the user on their smartphone app

[0680] Process: The user scans the QR code with the QR code reader installed on the automatic dispensing machine.

[0681] Output: Mixed and packaged seasonings and spices provided by the automatic mixer

[0682] Through these steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time.

[0683] (Application example 2)

[0684] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0685] Currently, most automated mixing systems only mix seasonings and spices based on user-input selection information. As a result, they do not take into account the user's emotions or psychological state, making it impossible to provide a fully personalized dining experience. Furthermore, there is no mechanism for the system to automatically suggest seasonings that suit the user's emotions, leaving room for further improvements in user satisfaction.

[0686] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving selection information related to dishes and seasonings from a user terminal, emotion analysis means for analyzing the user's emotional information and correcting the selection information, generation means for generating the types and amounts of necessary seasonings and spices based on the selection information and emotional information, and means for transmitting the generated types and amounts of seasonings and spices to the automatic blending machine. This makes it possible to blend seasonings and spices individually according to the user's emotional state.

[0687] A "user terminal" is an electronic device operated by a user, including a smartphone, tablet, or PC.

[0688] "Selection information regarding dish and seasoning" is information regarding the type of dish and its seasoning selected by the user, and is data indicating the cooking result desired by the user.

[0689] The "emotion analysis means" is a function for recognizing the user's emotions by analyzing the user's facial recognition data, voice data, and behavioral data.

[0690] The "generation means" is a function for calculating and generating the types and amounts of seasonings and spices required based on the selection information and emotional information.

[0691] "Automatic blending means" refers to a mechanical device that actually mixes the types and quantities of seasonings and spices produced and fills them into appropriate packages.

[0692] An "artificial intelligence model" is a model that operates based on machine learning algorithms and is trained to perform a specific task (in this case, calculating quantities of seasonings and spices).

[0693] "Retail establishment" is a general term for places such as supermarkets, convenience stores, and shopping malls where consumers can purchase goods.

[0694] An embodiment of the present invention will now be described in detail.

[0695] System Configuration

[0696] This system mainly consists of a user terminal, a server, and an automatic mixing machine. It also includes an emotion analysis means for recognizing the user's emotions.

[0697] User Device

[0698] The user device is an electronic device such as a smartphone or tablet, on which a dedicated application is installed. The user launches the application and selects the dish and seasoning. Furthermore, the device's built-in camera and microphone are used to capture the user's facial recognition data and voice data.

[0699] server

[0700] The server performs the following functions:

[0701] 1. Information receiving means: receives selection information and emotion information transmitted from the user terminal.

[0702] 2. Emotion analysis means: Analyze the user's emotions based on the received emotional information.

[0703] 3. Generation method: Based on emotional and selection information, a generative AI model is used to calculate the types and amounts of seasonings and spices needed.

[0704] 4. Data transmission means: Transmits the calculation results to the automatic compounding machine.

[0705] Hardware and software used

[0706] User device: iOS or Android device

[0707] Server: Cloud server (e.g. AWS, Google Cloud)

[0708] Sentiment analysis methods: Microsoft Azure Cognitive Services, Amazon Rekognition, Google Cloud Vision, etc.

[0709] Generative AI models: OpenAI GPT-3, etc.

[0710] Automatic mixer: Automatic seasoning mixer, QR code reader, control microcomputer

[0711] Specific examples

[0712] For example, a user visits a brick-and-mortar store and launches the smartphone app. They select "special ramen" and "spicy" from the menu, then scan their face using the app's camera. The system uses emotion analysis to recognize that the user is smiling. This information is sent to a server, which uses a generative AI model to calculate the appropriate types and amounts of seasonings and spices. In this case, the calculated result is "400ml of ramen soup, 20ml of soy sauce, and 5ml of chili oil." The information is then sent to an automatic blending machine, which blends the ramen.

[0713] Prompt Sentence Examples

[0714] 1. Prompt for sentiment analysis program:

[0715] image_data: user_face_image

[0716] audio_data: user_voice_recording

[0717] environment: inside_store

[0718] 2. Prompt for the generative AI model:

[0719] selected_dish: Special Ramen

[0720] taste_preference: spicy

[0721] user_emotion: happiness

[0722] In this way, personalized seasoning and spice blends can be created that take into account the user's emotional state and preferences, resulting in increased user satisfaction and a more personalized dining experience.

[0723] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0724] Step 1:

[0725] A user visits a physical store and launches the smartphone app. The user selects a dish (e.g., special ramen) and its seasoning (e.g., spicy) from the app's menu and enters their selection information. This selection information is saved on the device and used in the next step.

[0726] Step 2:

[0727] After the user inputs the selection information, the terminal uses a built-in camera and microphone to acquire the user's facial recognition data and voice data, thereby obtaining input data for the emotion analysis means to analyze the user's emotions.

[0728] Step 3:

[0729] The emotion analysis means built into the device analyzes the facial recognition data and voice data to recognize the user's emotional state (e.g., happiness). The emotion analysis means obtains emotion data using APIs such as Microsoft Azure Cognitive Services and Amazon Rekognition. The analysis results (emotional state) are stored on the device.

[0730] Step 4:

[0731] The terminal transmits the selection information (food and seasoning) and the analysis result (emotional state) together to the server. This transmitted data is formatted as a prompt sentence including the selection information and the emotion information.

[0732] Step 5:

[0733] The server analyzes the received selection information and emotional information and generates a prompt for the generative AI model. The prompt is sent to the generative AI model (e.g., OpenAI GPT-3) to calculate the type and amount of seasonings and spices needed. The dish, seasoning, and emotional state are input into this calculation.

[0734] Step 6:

[0735] The generative AI model responds by returning the appropriate types and amounts of seasonings and spices (e.g., 400ml of ramen soup, 20ml of soy sauce, 5ml of chili oil). The server receives this calculation result.

[0736] Step 7:

[0737] The server transmits the received data on the types and amounts of seasonings and spices to the automatic blending unit, which then blends the actual seasonings and spices based on the data and fills them into appropriate packages. This blending process is managed by a control microcomputer.

[0738] Step 8:

[0739] Once the automated dispensing means has completed dispensing and packing, the generated package is ready for collection by the user, who can use the QR code to collect the generated package from a dedicated machine in the physical store.

[0740] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0741] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0742] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0743] [Third embodiment]

[0744] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0745] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0746] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0747] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0748] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0749] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0750] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0751] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0752] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0753] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0754] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0755] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0756] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. The specific program processing of the system is described below.

[0757] System configuration

[0758] 1. User Device

[0759] It is a smartphone or tablet operated by the user, and sends information about food and seasoning selections to a server via an app.

[0760] 2. Server

[0761] The system analyzes the selection information received from the user's device and calculates the types and amounts of seasonings and spices required based on the generated AI model.

[0762] The calculation results are sent to the automatic compounding machine.

[0763] 3. Automatic blending machine

[0764] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[0765] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[0766] Program processing

[0767] User Device

[0768] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app sends the selection information to the server.

[0769] server

[0770] The server receives the selection information sent from the user's device. The server then analyzes the selection information and sends a request to the generative AI model. The AI ​​model calculates the types and amounts of seasonings and spices needed based on the specified dish and flavor. The calculation results are sent back to the server, which then sends the data to the automatic mixer.

[0771] Automatic blending machine

[0772] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[0773] Specific examples

[0774] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0775] In this way, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[0776] The processing flow will be explained below.

[0777] Step 1:

[0778] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[0779] Step 2:

[0780] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app then sends this selection information to the server.

[0781] Step 3:

[0782] The server receives the selection information (food and seasoning) sent by the user, analyzes the received information, and creates a request to the generative AI model.

[0783] Step 4:

[0784] The server sends a request to the generative AI model, which receives the request and calculates the types and amounts of seasonings and spices needed based on the selected dish and flavor.

[0785] Step 5:

[0786] The generative AI model sends the calculation results (e.g., 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil) to the server. The server receives the calculation results and packs the data.

[0787] Step 6:

[0788] The server sends data to the automatic compounding machine, which reads the data received from the server and prepares compounding instructions.

[0789] Step 7:

[0790] The automatic mixing machine accurately measures the specified amount of seasonings (20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil) and places them in a mixing container.

[0791] Step 8:

[0792] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[0793] Step 9:

[0794] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[0795] Step 10:

[0796] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[0797] Through this series of steps, users can obtain the amount of seasonings and spices they need without waste, allowing them to use up all the seasonings and reduce food waste.

[0798] Example 1

[0799] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0800] With conventional seasoning and spice blends, it is difficult to quickly and accurately blend according to individual user needs, and it is also difficult to provide them in portions that do not go to waste. As a result, many households and restaurants have problems with surpluses or shortages of seasonings, resulting in food waste and increased cooking effort.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0802] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating prompt sentences based on the selection information using a generative AI model and receiving the calculation results, automatic mixing means for mixing and packing the types and amounts of seasonings and spices generated by the generating means, and means for providing seasonings and spices to the user using a QR code reader. This enables the fast and accurate provision of seasonings and spices according to the individual requests of the user, and ensures that amounts are provided without waste.

[0803] A "user terminal" is an electronic device such as a smartphone or tablet that allows a user to input information about food and seasoning selections.

[0804] "Selection information" refers to detailed information about the type of food and seasonings that the user inputs through the terminal.

[0805] The "generation means" is a mechanism for calculating the types and amounts of seasonings and spices required based on the selection information received from the user terminal.

[0806] A "generative AI model" is an artificial intelligence model used to calculate the types and amounts of seasonings and spices based on selected information.

[0807] A "prompt" is a document that allows a generative AI model to input selected information in a specific format.

[0808] The "automatic blending means" is a mechanism that blends specified seasonings and spices based on the calculation results from the server and packs them into small packages.

[0809] A "QR code reader" is a device that scans the generated QR code, allowing users to receive the seasonings and spices they have purchased.

[0810] "Commercial establishment" means a place where goods are generally sold, including supermarkets and other retail stores.

[0811] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. Specific embodiments of the system are described below.

[0812] System configuration

[0813] 1. User Device

[0814] The user operates a smartphone or tablet, which sends information about the food and seasoning selection to a server via an application. This application provides an interface for the user to select the name of the food, the strength of the seasoning, etc.

[0815] 2. Server

[0816] The server analyzes the selection information received from the user device and calculates the types and amounts of seasonings and spices required based on the generated AI model. Specifically, the server has the following functions:

[0817] Receiving selection information from the user device

[0818] Analyze the selection information and send a prompt to the generative AI model

[0819] Receiving and analyzing the results calculated by the generative AI model

[0820] Sending calculation results to the automatic compounding machine

[0821] 3. Automatic blending machine

[0822] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. The mixed and packed products can be smoothly collected by the user using a QR code. The automatic mixing machine is installed in retail stores such as commercial facilities.

[0823] Program processing flow

[0824] A user launches the app on their smartphone or tablet and selects their preferred dish and seasoning from the menu that appears. For example, the user selects mapo tofu and spicy. Once the user has completed their selection, the app sends the information to the server.

[0825] The server receives the selection information sent from the user device, then analyzes the data and sends a prompt to the generative AI model: "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasonings and spices needed."

[0826] Based on the prompts received, the generative AI model calculates the types and amounts of seasonings and spices that correspond to the specified dish and flavoring—for example, 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil, etc.—and sends the calculation results back to the server.

[0827] The server receives the calculation results from the AI ​​model and sends the data to the automatic blending machine. The automatic blending machine uses the data from the server to extract the required amounts of seasonings and spices and begin blending. For example, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes all the seasonings together. Once blending is complete, it packs them into individual packages.

[0828] Finally, users can take a QR code generated by the smartphone app and scan it into a QR code reader on an automated dispensing machine installed in a commercial facility to receive the finished, packaged condiments and spices.

[0829] Specific examples

[0830] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0831] Prompt Sentence Examples

[0832] Examples of specific prompts to input to a generative AI model include:

[0833] The user selected "Mapo Tofu" and "Spicy." Please calculate the type and amount of seasonings needed.

[0834] As described above, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[0835] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0836] Step 1:

[0837] A user launches an app on their smartphone or tablet and selects their preferred dish and its seasoning from the menu. Specifically, the user opens the app, taps "Mapo Tofu" from the "Chinese Food" category, and then selects "Spicy." The entered information is the dish name "Mapo Tofu" and the seasoning "Spicy," and this selection information is sent from the app to the server.

[0838] Step 2:

[0839] The server receives the selection information sent from the user's device. The server analyzes the received information (dish name and seasoning) and generates a prompt for the generative AI model. For example, it creates a prompt such as, "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasoning needed." After this prompt is generated, the server sends it to the generative AI model.

[0840] Step 3:

[0841] Based on the prompt received from the server, the generative AI model calculates the type and amount of seasonings and spices that correspond to the specified dish and flavor. For example, it generates a calculation result such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." This calculation result is then sent back to the server from the generative AI model.

[0842] Step 4:

[0843] The server receives the calculation results from the generative AI model and sends the data to the automatic blending machine. Specifically, the calculation results sent are "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The automatic blending machine is then prepared to operate based on this data.

[0844] Step 5:

[0845] Based on the data received from the server, the automatic mixing machine mixes the required amount of specified seasonings and spices and packs them into small packages. Specifically, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes them together. After mixing, the mixed seasoning is packed into small packages.

[0846] Step 6:

[0847] The user takes a QR code generated by the smartphone app and scans it with the QR code reader of the automatic blending machine installed in the commercial facility. If the QR code is scanned correctly, the automatic blending machine will provide the blending package to the user. At this point, the user can receive the packaged seasonings and spices.

[0848] Through the above process steps, the present invention allows for the rapid and accurate provision of seasonings and spices according to the user's individual needs, and allows the user to obtain seasonings in amounts that are not wasted.

[0849] (Application example 1)

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

[0851] In recent years, home cooking has become more diverse, and users need to be able to quickly obtain the right amount of seasonings and spices to suit their preferences. However, individually preparing many different seasonings and spices is time-consuming and labor-intensive, and can also result in food waste. Furthermore, there is a lack of automated methods for blending seasonings to suit specific dishes and flavors. Therefore, there is a need for a system that can quickly provide the right amount of seasonings and spices based on the user's selection, thereby reducing waste.

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

[0853] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating the types and amounts of required seasonings and spices based on the selection information, means for automatically mixing and packing the types and amounts of seasonings and spices generated by the means for mixing, means for generating and displaying an identification code for receiving the mixed and packed seasonings and spices, and means for identifying and providing the seasonings and spices using the identification code, thereby enabling users to quickly obtain the required amounts of seasonings and spices according to their preferences without waste.

[0854] A "user terminal" is a device operated by a user, including a smartphone or tablet.

[0855] "Selection information regarding dish and seasoning" is information about the type of dish and its seasoning selected by the user.

[0856] The "generation means" is a technical means for calculating the type and amount of required seasonings and spices based on the selected information.

[0857] An "artificial intelligence model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new data.

[0858] "Mixing and packing means" means equipment for mixing and packaging specified seasonings and spices in prescribed quantities.

[0859] "Automatic blending means" refers to a machine that automatically blends and packs the necessary seasonings and spices.

[0860] "Retail store" refers to any store that sells goods to the general public.

[0861] An "identification code" is a code used to identify a specific product or information, and includes QR codes and barcodes.

[0862] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. As a specific program process of the system of the present invention, a method for linking the user terminal, the server, and the automatic mixer will be described below.

[0863] System configuration and functions

[0864] 1. User Device

[0865] The user terminal is a smartphone or tablet, and through a dedicated app, the user can input information about the food and seasonings they choose, which is then sent to the server.

[0866] 2. Server

[0867] The server analyzes the selection information received from the user's device and uses a generative AI model to calculate the types and amounts of seasonings and spices needed. The results are then sent to the automatic mixer.

[0868] 3. Automatic blending machine

[0869] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. This device is installed in retail stores, and users can receive their products using a QR code.

[0870] Hardware and software examples

[0871] Hardware

[0872] Smartphone (user device)

[0873] Cloud Server (Server)

[0874] Automatic mixing machine (equipped with a microcontroller such as Raspberry Pi)

[0875] QR Code Reader

[0876] software

[0877] Smartphone app development frameworks (React Native, Flutter, etc.)

[0878] Server-side frameworks (Node.js, Express)

[0879] Generative AI models (e.g., GPT-4)

[0880] Database systems (SQL, NoSQL)

[0881] Explanation of program processing

[0882] 1. User terminal processing

[0883] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[0884] The selected information is saved as form input in the app and a POST request is sent to the server.

[0885] 2. Server Processing

[0886] The server receives the POST request and parses the entered selections.

[0887] The preprocessed information is sent as prompts to a generative AI model (GPT-4) to calculate the types and amounts of seasonings and spices needed.

[0888] Example prompt sentence:

[0889] User selected dish: Mapo tofu

[0890] Preferred spice level: Medium

[0891] Provide the specific amounts and types of ingredients required.

[0892] The generative AI model responds by sending details of the seasonings and spices to the server, such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil."

[0893] 3. Automatic compounding machine processing

[0894] The server sends the data returned by the AI ​​model to the automatic compounding machine.

[0895] Based on the received data, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[0896] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[0897] Specific examples

[0898] For example, if a user wants to cook mapo tofu with a "medium" level of spiciness, the selection information "mapo tofu" and "medium" is sent to the server. The server then sends a prompt to the generative AI model to calculate the type and amount of seasonings and spices needed. The calculation results (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil") are sent to the automatic blending machine, which blends and packs the food. The user can then receive the blended seasonings and spices by scanning the QR code on the automatic blending machine.

[0899] Thus, by using the system of the present invention, the user can obtain seasonings and spices quickly and without waste.

[0900] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0901] Step 1:

[0902] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[0903] Input: Dish "Mapo Tofu" and seasoning "Medium spicy"

[0904] Output: Selection information ready to send

[0905] What happens: The user selects a menu in the app and presses the select button, which saves the input information and prepares it to be sent to the server.

[0906] Step 2:

[0907] The terminal sends the selected information to the server as a POST request.

[0908] Input: Selection information (dish "Mapo tofu", seasoning "medium spicy")

[0909] Output: Information sent to the server

[0910] What happens: Once the user confirms their selection, the app sends a POST request to the server.

[0911] Step 3:

[0912] The server receives the POST request and parses the entered selections.

[0913] Input: User selection information

[0914] Output: Parsed selection information

[0915] What happens: The server parses the POST request and extracts the selected dish and seasoning information.

[0916] Step 4:

[0917] The server sends the preprocessed information as prompts to the generative AI model, which calculates the types and amounts of seasonings and spices needed.

[0918] Input: Prompt text (e.g., "User selected dish: Mapo tofu\nPreferred spice level: Medium\nProvide the specific amounts and types of ingredients required.")

[0919] Output: Calculated seasoning and spice types and amounts

[0920] How it works: The server sends a prompt to the generative AI model (GPT-4) and receives the required amount of seasonings and spices in text format.

[0921] Step 5:

[0922] The server analyzes the data returned from the AI ​​model and sends it to the automatic mixer.

[0923] Input: AI model output data (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil")

[0924] Output: Ingredient and spice data sent to the automatic mixer

[0925] Specific operation: The server analyzes the output data of the AI ​​model, converts it into a format that the automatic mixer can understand, and sends it.

[0926] Step 6:

[0927] Based on the data received, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[0928] Input: Condiment and spice data sent from the server

[0929] Output: Mixed and packaged seasonings and spices

[0930] Specific operation: The automatic mixing machine uses motors and measuring devices to accurately measure and pack seasonings.

[0931] Step 7:

[0932] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[0933] Input: Finished seasoning and spice packaging information

[0934] Output: QR code displayed on the user's device

[0935] How it works: The automatic dispensing machine generates a QR code based on the package information, which is then immediately displayed on the user's device.

[0936] Step 8:

[0937] Users scan the generated QR code with a QR code reader installed in the automatic mixing machine to receive packaged seasonings and spices.

[0938] Input: QR code displayed on the user's device

[0939] Output: Condiments and spices received

[0940] How it works: The user scans the QR code with a QR code reader and receives packaged condiments and spices from the automatic dispensing machine.

[0941] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0942] The present invention combines a system including a user terminal, a server, and an automatic mixer with an emotion engine that recognizes the user's emotions, and mixes and packs seasonings and spices based on the user's selection. The specific program processing of the system is described below.

[0943] System configuration

[0944] 1. User Device

[0945] It is a smartphone or tablet operated by the user, and sends the emotion information recognized by the emotion engine along with the food and seasoning selection information to the server via the app.

[0946] 2. Server

[0947] The system analyzes the selection and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on a generative AI model.

[0948] The calculation results are sent to the automatic compounding machine.

[0949] 3. Automatic blending machine

[0950] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[0951] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[0952] 4. Emotion Engine

[0953] It is built into the user's device and recognizes the user's emotions by analyzing facial recognition data, voice data, and behavioral data.

[0954] The user's selection information is corrected based on the recognized emotion.

[0955] Program processing

[0956] User Device

[0957] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app's built-in emotion engine recognizes the user's emotion (e.g., happiness, anger, sadness, etc.) and sends that information to the server.

[0958] server

[0959] The server receives the selection information and emotion information sent from the user's device. The server then analyzes the selection information and emotion information and makes a request to the generative AI model. The AI ​​model calculates the type and amount of seasonings and spices needed based on the specified dish, seasoning, and the user's emotion. For example, if the user is angry, it will make adjustments such as increasing the spiciness. The calculation results are returned to the server, which then sends the data to the automatic mixer.

[0960] Automatic blending machine

[0961] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[0962] Specific examples

[0963] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app's built-in emotion engine recognizes the user's emotions and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the received information and calculates the required amount of seasonings and spices based on the generative AI model. For example, the server calculates "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil" to increase the spiciness. This calculation result is then sent from the server to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[0964] In this way, the present invention allows users to quickly and efficiently obtain seasonings and spices that suit their mood at the time, thereby reducing food waste and improving the user experience.

[0965] The processing flow will be explained below.

[0966] Step 1:

[0967] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[0968] Step 2:

[0969] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app recognizes this selection information.

[0970] Step 3:

[0971] The emotion engine built into the user device analyzes the user's facial recognition data, voice data, or behavioral data to detect whether the user is currently angry, and sends this emotion information along with the selection information to the server.

[0972] Step 4:

[0973] The server receives the selection and emotion information sent by the user, analyzes the received information, and makes a request to the generative AI model.

[0974] Step 5:

[0975] The server sends a request to the generative AI model, which receives the request and calculates the type and amount of seasonings and spices needed based on the selected dish (mapo tofu), seasoning (spicy), and the user's emotion (anger).

[0976] Step 6:

[0977] The generative AI model sends the calculated results (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil) to the server. The calculated results are spicier than the usual amounts.

[0978] Step 7:

[0979] The server receives the calculation results, packs the data, and sends the packed data to the automatic compounding machine.

[0980] Step 8:

[0981] The automatic mixing machine reads the data received from the server and prepares the mixing instructions. It accurately measures the specified amount of seasonings (25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil) and places them in the mixing container.

[0982] Step 9:

[0983] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[0984] Step 10:

[0985] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[0986] Step 11:

[0987] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[0988] Through this series of steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time, reducing food waste and improving the user experience.

[0989] Example 2

[0990] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0991] In modern society, detailed customization is required to meet the diverse seasoning and spice needs of users. However, conventional methods have had challenges in generating seasonings that take the user's feelings into account and in quickly packaging them. It has also been difficult to provide seasonings in the right amount without waste. For this reason, a system that can improve the user experience while reducing food waste is needed.

[0992] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0993] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for collecting user emotion information using an emotion engine built into the user terminal, means for generating the types and amounts of seasonings and spices required based on the selection information and emotion information, and automatic blending means for blending and packing the types and amounts of seasonings and spices generated by the generation means, thereby making it possible to provide customized seasonings and spices that take into account the user's emotion information.

[0994] A "user terminal" is a device operated by a user, such as a smartphone or tablet, that has the function of transmitting cooking and seasoning information, as well as emotional information, to a server.

[0995] An "emotion engine" is software built into the user's device that has the function of recognizing emotional information by analyzing the user's facial expressions and voice.

[0996] "Selection information" refers to information about the food and its seasonings selected by the user using the terminal.

[0997] "Emotional information" refers to information about the user's emotional state as recognized by the emotion engine.

[0998] The "generation means" is a function incorporated in the server, and has the function of calculating and generating the types and amounts of seasonings and spices based on the selection information and emotional information.

[0999] "Artificial intelligence model" refers to an algorithm or system that uses adaptive learning techniques to predict or generate results based on specified conditions.

[1000] "Automatic blending means" refers to an automated machine or the like that actually blends and packs the seasonings and spices produced by the production means.

[1001] "Retail store" refers to a facility where users ultimately purchase and receive products, and is a store that provides products to general consumers.

[1002] This invention incorporates an emotion engine that recognizes the user's emotions into a system that combines a user terminal, a server, and an automatic mixer. This system makes it possible to customize, mix, and pack seasonings and spices based on the user's selection information and emotional information.

[1003] System configuration

[1004] User Device

[1005] The user device is a smartphone or tablet that the user operates. A dedicated application and emotion engine are installed on this device, which analyzes the user's facial expressions and voice. Through the application, the user selects their preferred dish and seasoning, and sends this along with the emotion information recognized by the emotion engine to the server. Examples of hardware used include iPhones and Android smartphones. The software used is a dedicated application and emotion recognition engine (for example, Microsoft Azure Face API).

[1006] server

[1007] The server analyzes the selection information and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on the generative AI model. Python and TensorFlow are used as data analysis tools for this process. The calculation results are sent to the automatic blending machine. As a specific example of operation, the prompt "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the types and amounts of seasonings needed" is input into the generative AI model, which then outputs the appropriate types and amounts. The hardware used can be, for example, an EC2 instance from Amazon Web Services (AWS).

[1008] Automatic blending machine

[1009] An automatic mixer is a device that mixes the necessary seasonings and spices based on data received from a server and packs them into small packages. It is installed in retail stores and supermarkets, and users receive products using a set QR code. Based on data sent from the server, the automatic mixer measures out the appropriate amount of seasonings, such as chili bean paste, soy sauce, sugar, and chili oil, and seals them into packages. The hardware used includes dedicated automatic mixers. The software includes control software (e.g., embedded Linux) for executing instructions from the server.

[1010] Specific examples

[1011] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. After confirming this selection, the user's device's built-in emotion engine recognizes the user's emotion and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the information and calculates the appropriate amount of seasonings and spices based on a generative AI model. For example, the calculation results may be "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil." The calculation results are then sent from the server to an automatic blender, which blends and packs the necessary seasonings and spices. The user can receive the packaged product by scanning the QR code generated by the smartphone app with a QR code reader. In this way, users can quickly obtain the seasonings and spices that best suit their current emotion without waste. This reduces food waste and improves the user experience.

[1012] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1013] Program processing flow

[1014] Step 1: User selection and emotional information collection

[1015] User Device

[1016] Input: The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. The user specifically selects "Mapo tofu" and "spicy."

[1017] Processing: Once the selection is complete, the app's built-in emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice. The emotion engine recognizes the user's emotional state and records it as data.

[1018] Output: Selection information (food: mapo tofu, seasoning: spicy) and emotion information (emotion: anger)

[1019] Step 2: Sending selection and emotion information

[1020] User Device

[1021] Input: Choice information and emotion information collected in step 1

[1022] Processing: The user terminal sends the selected dish and seasoning, as well as the recognized emotion information, to the server.

[1023] Output: Selection information and emotion information sent to the server

[1024] Step 3: Analyze the information and generate instructions

[1025] server

[1026] Input: Selection information and emotion information sent from the user device

[1027] Processing: The server analyzes the selection and emotion information, then creates a prompt for the generative AI model and sends the request.

[1028] Prompt: "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the type and amount of seasoning needed."

[1029] A generative AI model calculates the appropriate types and amounts of seasonings and spices based on prompts.

[1030] Output: Calculation result (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil)

[1031] Step 4: Mixing and packaging seasonings and spices

[1032] Automatic blending machine

[1033] Input: Calculation results sent from the server (types and amounts of seasonings and spices)

[1034] Processing: The automatic mixer mixes the required amount of seasonings and spices.

[1035] Specifically, the condiments are measured and mixed in order according to the instructions, such as measuring 25g of chili bean paste, then 15ml of soy sauce.

[1036] The blended seasonings and spices are packaged in small portions.

[1037] Output: Mixed and packed small packages

[1038] Step 5: Receive your product via QR code

[1039] User terminal, user

[1040] Input: QR code generated by the user on their smartphone app

[1041] Process: The user scans the QR code with the QR code reader installed on the automatic dispensing machine.

[1042] Output: Mixed and packaged seasonings and spices provided by the automatic mixer

[1043] Through these steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time.

[1044] (Application example 2)

[1045] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1046] Currently, most automated mixing systems only mix seasonings and spices based on user-input selection information. As a result, they do not take into account the user's emotions or psychological state, making it impossible to provide a fully personalized dining experience. Furthermore, there is no mechanism for the system to automatically suggest seasonings that suit the user's emotions, leaving room for further improvements in user satisfaction.

[1047] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving selection information related to dishes and seasonings from a user terminal, emotion analysis means for analyzing the user's emotional information and correcting the selection information, generation means for generating the types and amounts of necessary seasonings and spices based on the selection information and emotional information, and means for transmitting the generated types and amounts of seasonings and spices to the automatic blending machine. This makes it possible to blend seasonings and spices individually according to the user's emotional state.

[1048] A "user terminal" is an electronic device operated by a user, including a smartphone, tablet, or PC.

[1049] "Selection information regarding dish and seasoning" is information regarding the type of dish and its seasoning selected by the user, and is data indicating the cooking result desired by the user.

[1050] The "emotion analysis means" is a function for recognizing the user's emotions by analyzing the user's facial recognition data, voice data, and behavioral data.

[1051] The "generation means" is a function for calculating and generating the types and amounts of seasonings and spices required based on the selection information and emotional information.

[1052] "Automatic blending means" refers to a mechanical device that actually mixes the types and quantities of seasonings and spices produced and fills them into appropriate packages.

[1053] An "artificial intelligence model" is a model that operates based on machine learning algorithms and is trained to perform a specific task (in this case, calculating quantities of seasonings and spices).

[1054] "Retail establishment" is a general term for places such as supermarkets, convenience stores, and shopping malls where consumers can purchase goods.

[1055] An embodiment of the present invention will now be described in detail.

[1056] System Configuration

[1057] This system mainly consists of a user terminal, a server, and an automatic mixing machine. It also includes an emotion analysis means for recognizing the user's emotions.

[1058] User Device

[1059] The user device is an electronic device such as a smartphone or tablet, on which a dedicated application is installed. The user launches the application and selects the dish and seasoning. Furthermore, the device's built-in camera and microphone are used to capture the user's facial recognition data and voice data.

[1060] server

[1061] The server performs the following functions:

[1062] 1. Information receiving means: receives selection information and emotion information transmitted from the user terminal.

[1063] 2. Emotion analysis means: Analyze the user's emotions based on the received emotional information.

[1064] 3. Generation method: Based on emotional and selection information, a generative AI model is used to calculate the types and amounts of seasonings and spices needed.

[1065] 4. Data transmission means: Transmits the calculation results to the automatic compounding machine.

[1066] Hardware and software used

[1067] User device: iOS or Android device

[1068] Server: Cloud server (e.g. AWS, Google Cloud)

[1069] Sentiment analysis methods: Microsoft Azure Cognitive Services, Amazon Rekognition, Google Cloud Vision, etc.

[1070] Generative AI models: OpenAI GPT-3, etc.

[1071] Automatic mixer: Automatic seasoning mixer, QR code reader, control microcomputer

[1072] Specific examples

[1073] For example, a user visits a brick-and-mortar store and launches the smartphone app. They select "special ramen" and "spicy" from the menu, then scan their face using the app's camera. The system uses emotion analysis to recognize that the user is smiling. This information is sent to a server, which uses a generative AI model to calculate the appropriate types and amounts of seasonings and spices. In this case, the calculated result is "400ml of ramen soup, 20ml of soy sauce, and 5ml of chili oil." The information is then sent to an automatic blending machine, which blends the ramen.

[1074] Prompt Sentence Examples

[1075] 1. Prompt for sentiment analysis program:

[1076] image_data: user_face_image

[1077] audio_data: user_voice_recording

[1078] environment: inside_store

[1079] 2. Prompt for the generative AI model:

[1080] selected_dish: Special Ramen

[1081] taste_preference: spicy

[1082] user_emotion: happiness

[1083] In this way, personalized seasoning and spice blends can be created that take into account the user's emotional state and preferences, resulting in increased user satisfaction and a more personalized dining experience.

[1084] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1085] Step 1:

[1086] A user visits a physical store and launches the smartphone app. The user selects a dish (e.g., special ramen) and its seasoning (e.g., spicy) from the app's menu and enters their selection information. This selection information is saved on the device and used in the next step.

[1087] Step 2:

[1088] After the user inputs the selection information, the terminal uses a built-in camera and microphone to acquire the user's facial recognition data and voice data, thereby obtaining input data for the emotion analysis means to analyze the user's emotions.

[1089] Step 3:

[1090] The emotion analysis means built into the device analyzes the facial recognition data and voice data to recognize the user's emotional state (e.g., happiness). The emotion analysis means obtains emotion data using APIs such as Microsoft Azure Cognitive Services and Amazon Rekognition. The analysis results (emotional state) are stored on the device.

[1091] Step 4:

[1092] The terminal transmits the selection information (food and seasoning) and the analysis result (emotional state) together to the server. This transmitted data is formatted as a prompt sentence including the selection information and the emotion information.

[1093] Step 5:

[1094] The server analyzes the received selection information and emotional information and generates a prompt for the generative AI model. The prompt is sent to the generative AI model (e.g., OpenAI GPT-3) to calculate the type and amount of seasonings and spices needed. The dish, seasoning, and emotional state are input into this calculation.

[1095] Step 6:

[1096] The generative AI model responds by returning the appropriate types and amounts of seasonings and spices (e.g., 400ml of ramen soup, 20ml of soy sauce, 5ml of chili oil). The server receives this calculation result.

[1097] Step 7:

[1098] The server transmits the received data on the types and amounts of seasonings and spices to the automatic blending unit, which then blends the actual seasonings and spices based on the data and fills them into appropriate packages. This blending process is managed by a control microcomputer.

[1099] Step 8:

[1100] Once the automated dispensing means has completed dispensing and packing, the generated package is ready for collection by the user, who can use the QR code to collect the generated package from a dedicated machine in the physical store.

[1101] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1103] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1104] [Fourth embodiment]

[1105] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1106] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1108] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1109] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1112] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1113] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1114] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1116] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1117] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1118] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. The specific program processing of the system is described below.

[1119] System configuration

[1120] 1. User Device

[1121] It is a smartphone or tablet operated by the user, and sends information about food and seasoning selections to a server via an app.

[1122] 2. Server

[1123] The system analyzes the selection information received from the user's device and calculates the types and amounts of seasonings and spices required based on the generated AI model.

[1124] The calculation results are sent to the automatic compounding machine.

[1125] 3. Automatic blending machine

[1126] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[1127] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[1128] Program processing

[1129] User Device

[1130] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app sends the selection information to the server.

[1131] server

[1132] The server receives the selection information sent from the user's device. The server then analyzes the selection information and sends a request to the generative AI model. The AI ​​model calculates the types and amounts of seasonings and spices needed based on the specified dish and flavor. The calculation results are sent back to the server, which then sends the data to the automatic mixer.

[1133] Automatic blending machine

[1134] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[1135] Specific examples

[1136] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[1137] In this way, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[1138] The processing flow will be explained below.

[1139] Step 1:

[1140] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[1141] Step 2:

[1142] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app then sends this selection information to the server.

[1143] Step 3:

[1144] The server receives the selection information (food and seasoning) sent by the user, analyzes the received information, and creates a request to the generative AI model.

[1145] Step 4:

[1146] The server sends a request to the generative AI model, which receives the request and calculates the types and amounts of seasonings and spices needed based on the selected dish and flavor.

[1147] Step 5:

[1148] The generative AI model sends the calculation results (e.g., 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil) to the server. The server receives the calculation results and packs the data.

[1149] Step 6:

[1150] The server sends data to the automatic compounding machine, which reads the data received from the server and prepares compounding instructions.

[1151] Step 7:

[1152] The automatic mixing machine accurately measures the specified amount of seasonings (20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil) and places them in a mixing container.

[1153] Step 8:

[1154] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[1155] Step 9:

[1156] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[1157] Step 10:

[1158] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[1159] Through this series of steps, users can obtain the amount of seasonings and spices they need without waste, allowing them to use up all the seasonings and reduce food waste.

[1160] Example 1

[1161] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1162] With conventional seasoning and spice blends, it is difficult to quickly and accurately blend according to individual user needs, and it is also difficult to provide them in portions that do not go to waste. As a result, many households and restaurants have problems with surpluses or shortages of seasonings, resulting in food waste and increased cooking effort.

[1163] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1164] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating prompt sentences based on the selection information using a generative AI model and receiving the calculation results, automatic mixing means for mixing and packing the types and amounts of seasonings and spices generated by the generating means, and means for providing seasonings and spices to the user using a QR code reader. This enables the fast and accurate provision of seasonings and spices according to the individual requests of the user, and ensures that amounts are provided without waste.

[1165] A "user terminal" is an electronic device such as a smartphone or tablet that allows a user to input information about food and seasoning selections.

[1166] "Selection information" refers to detailed information about the type of food and seasonings that the user inputs through the terminal.

[1167] The "generation means" is a mechanism for calculating the types and amounts of seasonings and spices required based on the selection information received from the user terminal.

[1168] A "generative AI model" is an artificial intelligence model used to calculate the types and amounts of seasonings and spices based on selected information.

[1169] A "prompt" is a document that allows a generative AI model to input selected information in a specific format.

[1170] The "automatic blending means" is a mechanism that blends specified seasonings and spices based on the calculation results from the server and packs them into small packages.

[1171] A "QR code reader" is a device that scans the generated QR code, allowing users to receive the seasonings and spices they have purchased.

[1172] "Commercial establishment" means a place where goods are generally sold, including supermarkets and other retail stores.

[1173] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. Specific embodiments of the system are described below.

[1174] System configuration

[1175] 1. User Device

[1176] The user operates a smartphone or tablet, which sends information about the food and seasoning selection to a server via an application. This application provides an interface for the user to select the name of the food, the strength of the seasoning, etc.

[1177] 2. Server

[1178] The server analyzes the selection information received from the user device and calculates the types and amounts of seasonings and spices required based on the generated AI model. Specifically, the server has the following functions:

[1179] Receiving selection information from the user device

[1180] Analyze the selection information and send a prompt to the generative AI model

[1181] Receiving and analyzing the results calculated by the generative AI model

[1182] Sending calculation results to the automatic compounding machine

[1183] 3. Automatic blending machine

[1184] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. The mixed and packed products can be smoothly collected by the user using a QR code. The automatic mixing machine is installed in retail stores such as commercial facilities.

[1185] Program processing flow

[1186] A user launches the app on their smartphone or tablet and selects their preferred dish and seasoning from the menu that appears. For example, the user selects mapo tofu and spicy. Once the user has completed their selection, the app sends the information to the server.

[1187] The server receives the selection information sent from the user device, then analyzes the data and sends a prompt to the generative AI model: "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasonings and spices needed."

[1188] Based on the prompts received, the generative AI model calculates the types and amounts of seasonings and spices that correspond to the specified dish and flavoring—for example, 20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil, etc.—and sends the calculation results back to the server.

[1189] The server receives the calculation results from the AI ​​model and sends the data to the automatic blending machine. The automatic blending machine uses the data from the server to extract the required amounts of seasonings and spices and begin blending. For example, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes all the seasonings together. Once blending is complete, it packs them into individual packages.

[1190] Finally, users can take a QR code generated by the smartphone app and scan it into a QR code reader on an automated dispensing machine installed in a commercial facility to receive the finished, packaged condiments and spices.

[1191] Specific examples

[1192] The user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app sends this information to the server. The server analyzes the received information and calculates the type and amount of seasonings and spices needed based on the generative AI model. For example, the calculation might be "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The server then sends this calculation result to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[1193] Prompt Sentence Examples

[1194] Examples of specific prompts to input to a generative AI model include:

[1195] The user selected "Mapo Tofu" and "Spicy." Please calculate the type and amount of seasonings needed.

[1196] As described above, the present invention allows users to quickly obtain the required amount of seasonings and spices without waste, thereby reducing food waste.

[1197] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1198] Step 1:

[1199] A user launches an app on their smartphone or tablet and selects their preferred dish and its seasoning from the menu. Specifically, the user opens the app, taps "Mapo Tofu" from the "Chinese Food" category, and then selects "Spicy." The entered information is the dish name "Mapo Tofu" and the seasoning "Spicy," and this selection information is sent from the app to the server.

[1200] Step 2:

[1201] The server receives the selection information sent from the user's device. The server analyzes the received information (dish name and seasoning) and generates a prompt for the generative AI model. For example, it creates a prompt such as, "The user selected 'Mapo Tofu' and 'Spicy'. Please calculate the type and amount of seasoning needed." After this prompt is generated, the server sends it to the generative AI model.

[1202] Step 3:

[1203] Based on the prompt received from the server, the generative AI model calculates the type and amount of seasonings and spices that correspond to the specified dish and flavor. For example, it generates a calculation result such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." This calculation result is then sent back to the server from the generative AI model.

[1204] Step 4:

[1205] The server receives the calculation results from the generative AI model and sends the data to the automatic blending machine. Specifically, the calculation results sent are "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil." The automatic blending machine is then prepared to operate based on this data.

[1206] Step 5:

[1207] Based on the data received from the server, the automatic mixing machine mixes the required amount of specified seasonings and spices and packs them into small packages. Specifically, it measures out 20g of chili bean paste, then 15ml of soy sauce. Similarly, it measures out 10g of sugar and 5ml of chili oil, and mixes them together. After mixing, the mixed seasoning is packed into small packages.

[1208] Step 6:

[1209] The user takes a QR code generated by the smartphone app and scans it with the QR code reader of the automatic blending machine installed in the commercial facility. If the QR code is scanned correctly, the automatic blending machine will provide the blending package to the user. At this point, the user can receive the packaged seasonings and spices.

[1210] Through the above process steps, the present invention allows for the rapid and accurate provision of seasonings and spices according to the user's individual needs, and allows the user to obtain seasonings in amounts that are not wasted.

[1211] (Application example 1)

[1212] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1213] In recent years, home cooking has become more diverse, and users need to be able to quickly obtain the right amount of seasonings and spices to suit their preferences. However, individually preparing many different seasonings and spices is time-consuming and labor-intensive, and can also result in food waste. Furthermore, there is a lack of automated methods for blending seasonings to suit specific dishes and flavors. Therefore, there is a need for a system that can quickly provide the right amount of seasonings and spices based on the user's selection, thereby reducing waste.

[1214] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1215] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for generating the types and amounts of required seasonings and spices based on the selection information, means for automatically mixing and packing the types and amounts of seasonings and spices generated by the means for mixing, means for generating and displaying an identification code for receiving the mixed and packed seasonings and spices, and means for identifying and providing the seasonings and spices using the identification code, thereby enabling users to quickly obtain the required amounts of seasonings and spices according to their preferences without waste.

[1216] A "user terminal" is a device operated by a user, including a smartphone or tablet.

[1217] "Selection information regarding dish and seasoning" is information about the type of dish and its seasoning selected by the user.

[1218] The "generation means" is a technical means for calculating the type and amount of required seasonings and spices based on the selected information.

[1219] An "artificial intelligence model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new data.

[1220] "Mixing and packing means" means equipment for mixing and packaging specified seasonings and spices in prescribed quantities.

[1221] "Automatic blending means" refers to a machine that automatically blends and packs the necessary seasonings and spices.

[1222] "Retail store" refers to any store that sells goods to the general public.

[1223] An "identification code" is a code used to identify a specific product or information, and includes QR codes and barcodes.

[1224] The present invention uses a system including a user terminal, a server, and an automatic mixer to mix and pack seasonings and spices based on user selection. As a specific program process of the system of the present invention, a method for linking the user terminal, the server, and the automatic mixer will be described below.

[1225] System configuration and functions

[1226] 1. User Device

[1227] The user terminal is a smartphone or tablet, and through a dedicated app, the user can input information about the food and seasonings they choose, which is then sent to the server.

[1228] 2. Server

[1229] The server analyzes the selection information received from the user's device and uses a generative AI model to calculate the types and amounts of seasonings and spices needed. The results are then sent to the automatic mixer.

[1230] 3. Automatic blending machine

[1231] The automatic mixing machine is a device that actually mixes and packs seasonings and spices based on the type and amount received from the server. This device is installed in retail stores, and users can receive their products using a QR code.

[1232] Hardware and software examples

[1233] Hardware

[1234] Smartphone (user device)

[1235] Cloud Server (Server)

[1236] Automatic mixing machine (equipped with a microcontroller such as Raspberry Pi)

[1237] QR Code Reader

[1238] software

[1239] Smartphone app development frameworks (React Native, Flutter, etc.)

[1240] Server-side frameworks (Node.js, Express)

[1241] Generative AI models (e.g., GPT-4)

[1242] Database systems (SQL, NoSQL)

[1243] Explanation of program processing

[1244] 1. User terminal processing

[1245] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[1246] The selected information is saved as form input in the app and a POST request is sent to the server.

[1247] 2. Server Processing

[1248] The server receives the POST request and parses the entered selections.

[1249] The preprocessed information is sent as prompts to a generative AI model (GPT-4) to calculate the types and amounts of seasonings and spices needed.

[1250] Example prompt sentence:

[1251] User selected dish: Mapo tofu

[1252] Preferred spice level: Medium

[1253] Provide the specific amounts and types of ingredients required.

[1254] The generative AI model responds by sending details of the seasonings and spices to the server, such as "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, and 5ml of chili oil."

[1255] 3. Automatic compounding machine processing

[1256] The server sends the data returned by the AI ​​model to the automatic compounding machine.

[1257] Based on the received data, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[1258] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[1259] Specific examples

[1260] For example, if a user wants to cook mapo tofu with a "medium" level of spiciness, the selection information "mapo tofu" and "medium" is sent to the server. The server then sends a prompt to the generative AI model to calculate the type and amount of seasonings and spices needed. The calculation results (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil") are sent to the automatic blending machine, which blends and packs the food. The user can then receive the blended seasonings and spices by scanning the QR code on the automatic blending machine.

[1261] Thus, by using the system of the present invention, the user can obtain seasonings and spices quickly and without waste.

[1262] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1263] Step 1:

[1264] The user launches the smartphone app and selects "Mapo Tofu" and its seasoning (for example, "medium spicy") from the list of dishes.

[1265] Input: Dish "Mapo Tofu" and seasoning "Medium spicy"

[1266] Output: Selection information ready to send

[1267] What happens: The user selects a menu in the app and presses the select button, which saves the input information and prepares it to be sent to the server.

[1268] Step 2:

[1269] The terminal sends the selected information to the server as a POST request.

[1270] Input: Selection information (dish "Mapo tofu", seasoning "medium spicy")

[1271] Output: Information sent to the server

[1272] What happens: Once the user confirms their selection, the app sends a POST request to the server.

[1273] Step 3:

[1274] The server receives the POST request and parses the entered selections.

[1275] Input: User selection information

[1276] Output: Parsed selection information

[1277] What happens: The server parses the POST request and extracts the selected dish and seasoning information.

[1278] Step 4:

[1279] The server sends the preprocessed information as prompts to the generative AI model, which calculates the types and amounts of seasonings and spices needed.

[1280] Input: Prompt text (e.g., "User selected dish: Mapo tofu\nPreferred spice level: Medium\nProvide the specific amounts and types of ingredients required.")

[1281] Output: Calculated seasoning and spice types and amounts

[1282] How it works: The server sends a prompt to the generative AI model (GPT-4) and receives the required amount of seasonings and spices in text format.

[1283] Step 5:

[1284] The server analyzes the data returned from the AI ​​model and sends it to the automatic mixer.

[1285] Input: AI model output data (e.g., "20g of chili bean paste, 15ml of soy sauce, 10g of sugar, 5ml of chili oil")

[1286] Output: Ingredient and spice data sent to the automatic mixer

[1287] Specific operation: The server analyzes the output data of the AI ​​model, converts it into a format that the automatic mixer can understand, and sends it.

[1288] Step 6:

[1289] Based on the data received, the automatic blending machine blends the specified amounts of seasonings and spices and packs them into small packages.

[1290] Input: Condiment and spice data sent from the server

[1291] Output: Mixed and packaged seasonings and spices

[1292] Specific operation: The automatic mixing machine uses motors and measuring devices to accurately measure and pack seasonings.

[1293] Step 7:

[1294] Once blending and packing is complete, the automatic blending machine generates an identification QR code that is displayed on the user's device.

[1295] Input: Finished seasoning and spice packaging information

[1296] Output: QR code displayed on the user's device

[1297] How it works: The automatic dispensing machine generates a QR code based on the package information, which is then immediately displayed on the user's device.

[1298] Step 8:

[1299] Users scan the generated QR code with a QR code reader installed in the automatic mixing machine to receive packaged seasonings and spices.

[1300] Input: QR code displayed on the user's device

[1301] Output: Condiments and spices received

[1302] How it works: The user scans the QR code with a QR code reader and receives packaged condiments and spices from the automatic dispensing machine.

[1303] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1304] The present invention combines a system including a user terminal, a server, and an automatic mixer with an emotion engine that recognizes the user's emotions, and mixes and packs seasonings and spices based on the user's selection. The specific program processing of the system is described below.

[1305] System configuration

[1306] 1. User Device

[1307] It is a smartphone or tablet operated by the user, and sends the emotion information recognized by the emotion engine along with the food and seasoning selection information to the server via the app.

[1308] 2. Server

[1309] The system analyzes the selection and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on a generative AI model.

[1310] The calculation results are sent to the automatic compounding machine.

[1311] 3. Automatic blending machine

[1312] This device actually mixes and packs the seasonings and spices based on the type and amount received from the server.

[1313] They are installed in supermarkets and other retail stores, and users can use QR codes to receive products.

[1314] 4. Emotion Engine

[1315] It is built into the user's device and recognizes the user's emotions by analyzing facial recognition data, voice data, and behavioral data.

[1316] The user's selection information is corrected based on the recognized emotion.

[1317] Program processing

[1318] User Device

[1319] The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. Once the selection is complete, the app's built-in emotion engine recognizes the user's emotion (e.g., happiness, anger, sadness, etc.) and sends that information to the server.

[1320] server

[1321] The server receives the selection information and emotion information sent from the user's device. The server then analyzes the selection information and emotion information and makes a request to the generative AI model. The AI ​​model calculates the type and amount of seasonings and spices needed based on the specified dish, seasoning, and the user's emotion. For example, if the user is angry, it will make adjustments such as increasing the spiciness. The calculation results are returned to the server, which then sends the data to the automatic mixer.

[1322] Automatic blending machine

[1323] Based on the data received from the server, the automatic mixing machine mixes the required amount of seasonings and spices and packs them into small packages. This provides the perfect seasonings and spices for the dish specified by the user. Finally, the automatic mixing machine stores the generated packages and makes them available for purchase by the user by scanning the QR code.

[1324] Specific examples

[1325] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. Once the user confirms their selection, the app's built-in emotion engine recognizes the user's emotions and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the received information and calculates the required amount of seasonings and spices based on the generative AI model. For example, the server calculates "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil" to increase the spiciness. This calculation result is then sent from the server to the automatic blending machine, which blends and packs the seasonings and spices. At this point, the user can receive the packaged seasonings and spices by taking the QR code generated by the smartphone app and scanning it with the QR code reader installed in the automatic blending machine.

[1326] In this way, the present invention allows users to quickly and efficiently obtain seasonings and spices that suit their mood at the time, thereby reducing food waste and improving the user experience.

[1327] The processing flow will be explained below.

[1328] Step 1:

[1329] The user launches the smartphone app, which displays a menu of food and seasoning options for the user.

[1330] Step 2:

[1331] The user selects their preferred dish "Mapo Tofu" and the taste "Spicy" from the menu within the app and presses the "OK" button. The app recognizes this selection information.

[1332] Step 3:

[1333] The emotion engine built into the user device analyzes the user's facial recognition data, voice data, or behavioral data to detect whether the user is currently angry, and sends this emotion information along with the selection information to the server.

[1334] Step 4:

[1335] The server receives the selection and emotion information sent by the user, analyzes the received information, and makes a request to the generative AI model.

[1336] Step 5:

[1337] The server sends a request to the generative AI model, which receives the request and calculates the type and amount of seasonings and spices needed based on the selected dish (mapo tofu), seasoning (spicy), and the user's emotion (anger).

[1338] Step 6:

[1339] The generative AI model sends the calculated results (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil) to the server. The calculated results are spicier than the usual amounts.

[1340] Step 7:

[1341] The server receives the calculation results, packs the data, and sends the packed data to the automatic compounding machine.

[1342] Step 8:

[1343] The automatic mixing machine reads the data received from the server and prepares the mixing instructions. It accurately measures the specified amount of seasonings (25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil) and places them in the mixing container.

[1344] Step 9:

[1345] The automatic mixing machine mixes the seasonings into the appropriate small packages, and once the packing is complete, the products are moved to the outlet.

[1346] Step 10:

[1347] The user obtains the QR code displayed on the smartphone app and scans it with the QR code reader installed in the automatic dispensing machine.

[1348] Step 11:

[1349] The automatic mixing machine recognizes the QR code and outputs the packaged seasonings and spices to the dispenser, where the user can collect the product.

[1350] Through this series of steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time, reducing food waste and improving the user experience.

[1351] Example 2

[1352] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1353] In modern society, detailed customization is required to meet the diverse seasoning and spice needs of users. However, conventional methods have had challenges in generating seasonings that take the user's feelings into account and in quickly packaging them. It has also been difficult to provide seasonings in the right amount without waste. For this reason, a system that can improve the user experience while reducing food waste is needed.

[1354] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1355] In this invention, the server includes means for receiving selection information regarding dishes and seasonings from a user terminal, means for collecting user emotion information using an emotion engine built into the user terminal, means for generating the types and amounts of seasonings and spices required based on the selection information and emotion information, and automatic blending means for blending and packing the types and amounts of seasonings and spices generated by the generation means, thereby making it possible to provide customized seasonings and spices that take into account the user's emotion information.

[1356] A "user terminal" is a device operated by a user, such as a smartphone or tablet, that has the function of transmitting cooking and seasoning information, as well as emotional information, to a server.

[1357] An "emotion engine" is software built into the user's device that has the function of recognizing emotional information by analyzing the user's facial expressions and voice.

[1358] "Selection information" refers to information about the food and its seasonings selected by the user using the terminal.

[1359] "Emotional information" refers to information about the user's emotional state as recognized by the emotion engine.

[1360] The "generation means" is a function incorporated in the server, and has the function of calculating and generating the types and amounts of seasonings and spices based on the selection information and emotional information.

[1361] "Artificial intelligence model" refers to an algorithm or system that uses adaptive learning techniques to predict or generate results based on specified conditions.

[1362] "Automatic blending means" refers to an automated machine or the like that actually blends and packs the seasonings and spices produced by the production means.

[1363] "Retail store" refers to a facility where users ultimately purchase and receive products, and is a store that provides products to general consumers.

[1364] This invention incorporates an emotion engine that recognizes the user's emotions into a system that combines a user terminal, a server, and an automatic mixer. This system makes it possible to customize, mix, and pack seasonings and spices based on the user's selection information and emotional information.

[1365] System configuration

[1366] User Device

[1367] The user device is a smartphone or tablet that the user operates. A dedicated application and emotion engine are installed on this device, which analyzes the user's facial expressions and voice. Through the application, the user selects their preferred dish and seasoning, and sends this along with the emotion information recognized by the emotion engine to the server. Examples of hardware used include iPhones and Android smartphones. The software used is a dedicated application and emotion recognition engine (for example, Microsoft Azure Face API).

[1368] server

[1369] The server analyzes the selection information and emotional information received from the user's device and calculates the types and amounts of seasonings and spices needed based on the generative AI model. Python and TensorFlow are used as data analysis tools for this process. The calculation results are sent to the automatic blending machine. As a specific example of operation, the prompt "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the types and amounts of seasonings needed" is input into the generative AI model, which then outputs the appropriate types and amounts. The hardware used can be, for example, an EC2 instance from Amazon Web Services (AWS).

[1370] Automatic blending machine

[1371] An automatic mixer is a device that mixes the necessary seasonings and spices based on data received from a server and packs them into small packages. It is installed in retail stores and supermarkets, and users receive products using a set QR code. Based on data sent from the server, the automatic mixer measures out the appropriate amount of seasonings, such as chili bean paste, soy sauce, sugar, and chili oil, and seals them into packages. The hardware used includes dedicated automatic mixers. The software includes control software (e.g., embedded Linux) for executing instructions from the server.

[1372] Specific examples

[1373] A user launches the smartphone app and selects "Mapo Tofu" and "Spicy" from the menu. After confirming this selection, the user's device's built-in emotion engine recognizes the user's emotion and detects that the user is angry. This information (dish, seasoning, and emotion information) is sent to the server. The server analyzes the information and calculates the appropriate amount of seasonings and spices based on a generative AI model. For example, the calculation results may be "25g of chili bean paste, 15ml of soy sauce, 5g of sugar, and 10ml of chili oil." The calculation results are then sent from the server to an automatic blender, which blends and packs the necessary seasonings and spices. The user can receive the packaged product by scanning the QR code generated by the smartphone app with a QR code reader. In this way, users can quickly obtain the seasonings and spices that best suit their current emotion without waste. This reduces food waste and improves the user experience.

[1374] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1375] Program processing flow

[1376] Step 1: User selection and emotional information collection

[1377] User Device

[1378] Input: The user launches the smartphone app and selects their preferred dish and seasoning from the displayed menu. The user specifically selects "Mapo tofu" and "spicy."

[1379] Processing: Once the selection is complete, the app's built-in emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice. The emotion engine recognizes the user's emotional state and records it as data.

[1380] Output: Selection information (food: mapo tofu, seasoning: spicy) and emotion information (emotion: anger)

[1381] Step 2: Sending selection and emotion information

[1382] User Device

[1383] Input: Choice information and emotion information collected in step 1

[1384] Processing: The user terminal sends the selected dish and seasoning, as well as the recognized emotion information, to the server.

[1385] Output: Selection information and emotion information sent to the server

[1386] Step 3: Analyze the information and generate instructions

[1387] server

[1388] Input: Selection information and emotion information sent from the user device

[1389] Processing: The server analyzes the selection and emotion information, then creates a prompt for the generative AI model and sends the request.

[1390] Prompt: "The user selected mapo tofu and spicy, and the emotion engine detected anger. In this situation, please calculate the type and amount of seasoning needed."

[1391] A generative AI model calculates the appropriate types and amounts of seasonings and spices based on prompts.

[1392] Output: Calculation result (e.g., 25g of chili bean paste, 15ml of soy sauce, 5g of sugar, 10ml of chili oil)

[1393] Step 4: Mixing and packaging seasonings and spices

[1394] Automatic blending machine

[1395] Input: Calculation results sent from the server (types and amounts of seasonings and spices)

[1396] Processing: The automatic mixer mixes the required amount of seasonings and spices.

[1397] Specifically, the condiments are measured and mixed in order according to the instructions, such as measuring 25g of chili bean paste, then 15ml of soy sauce.

[1398] The blended seasonings and spices are packaged in small portions.

[1399] Output: Mixed and packed small packages

[1400] Step 5: Receive your product via QR code

[1401] User terminal, user

[1402] Input: QR code generated by the user on their smartphone app

[1403] Process: The user scans the QR code with the QR code reader installed on the automatic dispensing machine.

[1404] Output: Mixed and packaged seasonings and spices provided by the automatic mixer

[1405] Through these steps, users can quickly and efficiently obtain the seasonings and spices that best suit their mood at the time.

[1406] (Application example 2)

[1407] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1408] Currently, most automated mixing systems only mix seasonings and spices based on user-input selection information. As a result, they do not take into account the user's emotions or psychological state, making it impossible to provide a fully personalized dining experience. Furthermore, there is no mechanism for the system to automatically suggest seasonings that suit the user's emotions, leaving room for further improvements in user satisfaction.

[1409] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving selection information related to dishes and seasonings from a user terminal, emotion analysis means for analyzing the user's emotional information and correcting the selection information, generation means for generating the types and amounts of necessary seasonings and spices based on the selection information and emotional information, and means for transmitting the generated types and amounts of seasonings and spices to the automatic blending machine. This makes it possible to blend seasonings and spices individually according to the user's emotional state.

[1410] A "user terminal" is an electronic device operated by a user, including a smartphone, tablet, or PC.

[1411] "Selection information regarding dish and seasoning" is information regarding the type of dish and its seasoning selected by the user, and is data indicating the cooking result desired by the user.

[1412] The "emotion analysis means" is a function for recognizing the user's emotions by analyzing the user's facial recognition data, voice data, and behavioral data.

[1413] The "generation means" is a function for calculating and generating the types and amounts of seasonings and spices required based on the selection information and emotional information.

[1414] "Automatic blending means" refers to a mechanical device that actually mixes the types and quantities of seasonings and spices produced and fills them into appropriate packages.

[1415] An "artificial intelligence model" is a model that operates based on machine learning algorithms and is trained to perform a specific task (in this case, calculating quantities of seasonings and spices).

[1416] "Retail establishment" is a general term for places such as supermarkets, convenience stores, and shopping malls where consumers can purchase goods.

[1417] An embodiment of the present invention will now be described in detail.

[1418] System Configuration

[1419] This system mainly consists of a user terminal, a server, and an automatic mixing machine. It also includes an emotion analysis means for recognizing the user's emotions.

[1420] User Device

[1421] The user device is an electronic device such as a smartphone or tablet, on which a dedicated application is installed. The user launches the application and selects the dish and seasoning. Furthermore, the device's built-in camera and microphone are used to capture the user's facial recognition data and voice data.

[1422] server

[1423] The server performs the following functions:

[1424] 1. Information receiving means: receives selection information and emotion information transmitted from the user terminal.

[1425] 2. Emotion analysis means: Analyze the user's emotions based on the received emotional information.

[1426] 3. Generation method: Based on emotional and selection information, a generative AI model is used to calculate the types and amounts of seasonings and spices needed.

[1427] 4. Data transmission means: Transmits the calculation results to the automatic compounding machine.

[1428] Hardware and software used

[1429] User device: iOS or Android device

[1430] Server: Cloud server (e.g. AWS, Google Cloud)

[1431] Sentiment analysis methods: Microsoft Azure Cognitive Services, Amazon Rekognition, Google Cloud Vision, etc.

[1432] Generative AI models: OpenAI GPT-3, etc.

[1433] Automatic mixer: Automatic seasoning mixer, QR code reader, control microcomputer

[1434] Specific examples

[1435] For example, a user visits a brick-and-mortar store and launches the smartphone app. They select "special ramen" and "spicy" from the menu, then scan their face using the app's camera. The system uses emotion analysis to recognize that the user is smiling. This information is sent to a server, which uses a generative AI model to calculate the appropriate types and amounts of seasonings and spices. In this case, the calculated result is "400ml of ramen soup, 20ml of soy sauce, and 5ml of chili oil." The information is then sent to an automatic blending machine, which blends the ramen.

[1436] Prompt Sentence Examples

[1437] 1. Prompt for sentiment analysis program:

[1438] image_data: user_face_image

[1439] audio_data: user_voice_recording

[1440] environment: inside_store

[1441] 2. Prompt for the generative AI model:

[1442] selected_dish: Special Ramen

[1443] taste_preference: spicy

[1444] user_emotion: happiness

[1445] In this way, personalized seasoning and spice blends can be created that take into account the user's emotional state and preferences, resulting in increased user satisfaction and a more personalized dining experience.

[1446] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1447] Step 1:

[1448] A user visits a physical store and launches the smartphone app. The user selects a dish (e.g., special ramen) and its seasoning (e.g., spicy) from the app's menu and enters their selection information. This selection information is saved on the device and used in the next step.

[1449] Step 2:

[1450] After the user inputs the selection information, the terminal uses a built-in camera and microphone to acquire the user's facial recognition data and voice data, thereby obtaining input data for the emotion analysis means to analyze the user's emotions.

[1451] Step 3:

[1452] The emotion analysis means built into the device analyzes the facial recognition data and voice data to recognize the user's emotional state (e.g., happiness). The emotion analysis means obtains emotion data using APIs such as Microsoft Azure Cognitive Services and Amazon Rekognition. The analysis results (emotional state) are stored on the device.

[1453] Step 4:

[1454] The terminal transmits the selection information (food and seasoning) and the analysis result (emotional state) together to the server. This transmitted data is formatted as a prompt sentence including the selection information and the emotion information.

[1455] Step 5:

[1456] The server analyzes the received selection information and emotional information and generates a prompt for the generative AI model. The prompt is sent to the generative AI model (e.g., OpenAI GPT-3) to calculate the type and amount of seasonings and spices needed. The dish, seasoning, and emotional state are input into this calculation.

[1457] Step 6:

[1458] The generative AI model responds by returning the appropriate types and amounts of seasonings and spices (e.g., 400ml of ramen soup, 20ml of soy sauce, 5ml of chili oil). The server receives this calculation result.

[1459] Step 7:

[1460] The server transmits the received data on the types and amounts of seasonings and spices to the automatic blending unit, which then blends the actual seasonings and spices based on the data and fills them into appropriate packages. This blending process is managed by a control microcomputer.

[1461] Step 8:

[1462] Once the automated dispensing means has completed dispensing and packing, the generated package is ready for collection by the user, who can use the QR code to collect the generated package from a dedicated machine in the physical store.

[1463] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1464] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1465] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1466] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1467] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1468] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1469] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1470] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1471] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1472] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1473] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1474] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1475] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1476] 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.

[1477] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1478] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1479] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1480] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1481] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1482] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1483] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1484] The following is further disclosed regarding the above embodiment.

[1485] (Claim 1)

[1486] means for receiving selection information regarding dishes and seasonings from a user terminal;

[1487] a generating means for generating the types and amounts of seasonings and spices required based on the selection information;

[1488] an automatic blending means for blending and packing the types and amounts of seasonings and spices produced by the producing means;

[1489] A system including:

[1490] (Claim 2)

[1491] 2. The system of claim 1, wherein the generating means includes means for calculating types and amounts of seasonings and spices using an artificial intelligence model.

[1492] (Claim 3)

[1493] 2. The system according to claim 1, wherein the automatic blending means is installed in a supermarket and includes a means for providing the blended and packaged seasonings and spices.

[1494] "Example 1"

[1495] (Claim 1)

[1496] means for receiving selection information regarding dishes and seasonings from a user terminal;

[1497] a generating means for generating the types and amounts of seasonings and spices required based on the selection information;

[1498] a means for transmitting a prompt sentence based on the selection information using a generative AI model generated by the generating means, and receiving the calculation result;

[1499] an automatic blending means for blending and packing the types and amounts of seasonings and spices produced by the producing means;

[1500] The automatic mixing means provides seasonings and spices to the user through a QR code reader;

[1501] A system including:

[1502] (Claim 2)

[1503] 2. The system according to claim 1, wherein the generating means includes means for calculating the types and amounts of seasonings and spices using an artificial intelligence model and returning the results to the server.

[1504] (Claim 3)

[1505] 2. The system of claim 1, wherein the automated blending means is installed in a commercial facility and includes a means for providing the blended and packaged seasonings and spices.

[1506] "Application Example 1"

[1507] (Claim 1)

[1508] means for receiving selection information regarding dishes and seasonings from a user terminal;

[1509] a generating means for generating the types and amounts of seasonings and spices required based on the selection information;

[1510] an automatic blending means for blending and packing the types and amounts of seasonings and spices produced by the producing means;

[1511] means for generating and displaying an identification code for receiving the prepared and packaged seasonings and spices;

[1512] means for identifying and providing seasonings and spices using said identification codes;

[1513] A system including:

[1514] (Claim 2)

[1515] 2. The system of claim 1, wherein the generating means includes means for calculating types and amounts of seasonings and spices using an artificial intelligence model.

[1516] (Claim 3)

[1517] 2. The system of claim 1, wherein the automated blending means is installed in a retail store and includes means for providing the blended and packaged seasonings and spices.

[1518] "Example 2: Combining Emotion Engines"

[1519] (Claim 1)

[1520] means for receiving selection information regarding dishes and seasonings from a user terminal;

[1521] means for collecting user emotion information using an emotion engine built into the user terminal;

[1522] a generating means for generating the types and amounts of seasonings and spices required based on the selection information and emotion information;

[1523] an automatic blending means for blending and packing the types and amounts of seasonings and spices produced by the producing means;

[1524] A system including:

[1525] (Claim 2)

[1526] 2. The system of claim 1, wherein the generating means includes means for calculating types and amounts of seasonings and spices using an artificial intelligence model.

[1527] (Claim 3)

[1528] 2. The system of claim 1, wherein the automated blending means is installed in a retail store and includes means for providing the blended and packaged seasonings and spices.

[1529] "Application example 2 when combining emotion engines"

[1530] New Claims

[1531] (Claim 1)

[1532] means for receiving selection information regarding dishes and seasonings from a user terminal;

[1533] emotion analysis means for analyzing emotion information of a user and correcting the selection information;

[1534] a generating means for generating the types and amounts of seasonings and spices required based on the selection information and emotion information;

[1535] an automatic blending means for blending and packing the types and amounts of seasonings and spices produced by the producing means;

[1536] A system including:

[1537] (Claim 2)

[1538] 2. The system of claim 1, wherein the generating means includes means for calculating types and amounts of seasonings and spices using an artificial intelligence model.

[1539] (Claim 3)

[1540] 10. The system of claim 1, wherein the automated blending means is installed in a retail facility and includes means for providing the blended and packaged condiments and spices. [Explanation of symbols]

[1541] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving selection information regarding dishes and seasonings from a user terminal; a generating means for generating the types and amounts of seasonings and spices required based on the selection information; an automatic blending means for blending and packing the types and amounts of seasonings and spices produced by the production means; A system including:

2. 2. The system of claim 1, wherein the generating means includes means for calculating types and amounts of seasonings and spices using an artificial intelligence model.

3. 2. The system of claim 1, wherein the automated blending means is installed in a supermarket and includes means for providing the blended and packaged seasonings and spices.

Images