system

Abstract

We provide the system. [Solution] A means for users to input taste information about nostalgic dishes, A means of obtaining information on ingredients, seasonings, and regional taste trends, This includes means such as an artificial intelligence model for analyzing this information and generating healthy recipes, A means of presenting the generated recipe to the user, A means of receiving user feedback and improving the model, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention aims to solve the problem that in a situation where it is difficult to reproduce the taste of nostalgic dishes, the conventional method relying only on personal memories and the feelings of others cannot accurately reproduce the taste. It also includes the problem that people who need dietary restrictions tend to neglect their health when enjoying past tastes.

Means for Solving the Problems

[0005] This invention provides a means for users to input taste information related to nostalgic dishes, and further includes means for acquiring ingredient information, seasoning information, and regional taste trend information. Based on this information, it performs analysis and enables accurate taste reproduction by including an artificial intelligence model for generating healthy recipes. Furthermore, by presenting the generated recipes to the user, receiving feedback, and incorporating it into the model, the invention provides a system that balances taste reproduction accuracy with health management.

[0006] A "user" is an individual or group that provides the system with taste information about nostalgic dishes and uses those recipes.

[0007] "Taste information" refers to information that users input about the taste characteristics, memories, and anecdotes related to the dish they want to recreate.

[0008] "Ingredient information" refers to detailed data about each ingredient, such as its seasonality, flavor characteristics, and nutritional value.

[0009] "Seasoning information" refers to detailed data such as the strength of the flavor of the seasonings used and any health precautions.

[0010] "Regional taste trend information" refers to data on tastes specific to a particular region.

[0011] An "artificial intelligence model" is a collection of algorithms and programs used to analyze collected information and generate healthy recipes.

[0012] A "recipe" is a detailed instruction manual for cooking, including cooking procedures and ingredient selection, designed to recreate the nostalgic flavors that users desire.

[0013] "Feedback" refers to the evaluations and comments that users provide after actually trying a recipe, and this information is used for future improvements. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of this invention consists of a user, a terminal, and a server. The user inputs taste information about a nostalgic dish through the terminal. This information includes the taste characteristics of the dish to be recreated and related anecdotes. The terminal confirms this information and transmits it to the server.

[0036] The server retrieves ingredient and seasoning information from an ingredient database along with the received taste information. It also simultaneously collects regional taste trend information and combines this data. An artificial intelligence model within the server analyzes this information to generate a taste profile of the dish the user desires. This taste profile includes a numerical representation of each taste element and takes into account the overall balance of flavors.

[0037] Next, the server uses the generated flavor profile to consider healthy and appropriate combinations of ingredients and seasonings and creates a recipe. This recipe is adjusted with health in mind, paying particular attention to the balance of salt and fat. It also details the cooking procedure and the timing of adding seasonings.

[0038] The server sends the completed recipe to the device. The device displays the received recipe in an easy-to-read format for the user. The user tries the recipe and inputs the results and feedback into the device. The feedback is returned to the server and used by the artificial intelligence model for further improvement.

[0039] For example, if a user wants to recreate the curry from a cafeteria they frequented during their student days, they input detailed taste information about that curry into a terminal. The server analyzes this information to determine the specific spice blends and ingredient combinations, and generates a recipe that recreates the nostalgic taste in a healthy way. This system allows users to enjoy the taste of the past while experiencing it without sacrificing their health.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users input information about the taste of nostalgic dishes through their devices. This information includes specific taste characteristics and memories associated with the dish they want to recreate.

[0043] Step 2:

[0044] The terminal converts the input taste information into an appropriate format and sends it to the server. During the conversion process, it checks for any missing or incorrect information.

[0045] Step 3:

[0046] Based on the taste information it receives, the server accesses the ingredient database to retrieve relevant ingredient and seasoning information. During this process, it also queries for data necessary for recreating the dish, such as seasonality and nutritional value.

[0047] Step 4:

[0048] The server collects information on regional taste trends and gathers data that reflects the regional characteristics of the flavors that users want to recreate.

[0049] Step 5:

[0050] An artificial intelligence model on the server analyzes the collected information and generates a taste profile of the dish the user desires. The profile includes a numerical representation of the balance of each taste element.

[0051] Step 6:

[0052] Based on the generated flavor profile, the server creates a recipe that combines appropriate ingredients and seasonings, taking health into consideration. The recipe includes cooking instructions and points to note.

[0053] Step 7:

[0054] The server sends the completed recipe to the terminal. The terminal displays the received recipe to the user in an easy-to-understand visual format.

[0055] Step 8:

[0056] Users try cooking using the provided recipes via their devices and input their results and feedback. The interface is designed to easily reflect what they felt through their experience.

[0057] Step 9:

[0058] The device sends user feedback back to the server. The server feeds this feedback into an artificial intelligence model, which is then used to improve recipes for future use.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Recreating the nostalgic flavors of dishes that many people have experienced in the past usually relies on individual taste perception, which is time-consuming and laborious, and often lacks sufficient consideration for health. In particular, there is a need to automatically generate healthy meal plans that effectively utilize memory-based taste information.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for acquiring information on ingredients, seasonings, and regional taste trends; means for combining and analyzing this information to generate a healthy meal plan, and means for improving the intelligent model by utilizing input feedback. This makes it possible to provide healthy meals while recreating past flavors and to optimize recipes to meet the user's taste needs.

[0064] A "user" is the entity that uses the system to recreate nostalgic dishes.

[0065] "Nostalgic cuisine" refers to the taste sensations experienced by users when eating specific dishes in the past.

[0066] "Taste information" refers to information entered by the user, such as the specific taste characteristics of a dish or associated memories.

[0067] A "terminal" is a device used by users to input taste information and receive the results of a dish's reproduction.

[0068] A "server" is a computer system that processes taste information and generates cooking plans using intelligent models.

[0069] "Ingredient information" refers to detailed information about the substances and components needed to recreate a particular dish.

[0070] "Seasoning information" refers to detailed information about the ingredients used to adjust the flavor of a dish.

[0071] "Regional taste trend information" refers to data that shows the general characteristics and trends of taste in a specific region.

[0072] An "intelligent model" is a computational model that analyzes input information and reproduces the taste of the dish the user desires.

[0073] A "meal plan" is a list of specific steps and necessary ingredients for a dish that the user wants to recreate.

[0074] "Feedback" refers to the input of results and impressions from users who have actually tried making the dishes.

[0075] This invention is a system that recreates the taste of a nostalgic dish a user has experienced in the past, and consists of a user, a terminal, and a server. The user can use the terminal to input detailed taste information about the dish they wish to recreate. This input is provided to the terminal as text information, including the characteristics of the dish's taste and associated memories. The terminal is responsible for transmitting the input information to the server.

[0076] The server receives taste information sent by the user and, based on this, collects information on ingredients, seasonings, and regional taste trends from a database system. SQL or similar database query languages ​​are used for this data collection. The collected data is analyzed by a generative AI model on the server to generate a taste profile of the dish the user is looking for. This profile is generated by a neural network using machine learning frameworks such as TENSORFLOW® or PyTorch.

[0077] Based on the generated taste profile, the server creates a healthy and nutritious meal plan. A Python script automatically calculates nutritional balance and generates health-conscious recipes, including adjustments to salt and fat content. The server then sends this recipe to the terminal, which presents it to the user in a user-friendly format.

[0078] Users attempt to cook dishes based on the provided recipes and input the results as feedback into their devices. The devices send this feedback back to the server, which uses the data to further improve the AI ​​model. Through this process, the taste reproduction and health aspects of the provided recipes continue to improve.

[0079] As a concrete example, if a user wants to recreate a curry they frequented during their student days, they input their taste information into the terminal. An example of a prompt message might be, "I want to recreate the fragrant spices and sweetness of the curry I ate at the cafeteria during my student days." Based on this information, the server's AI model generates a recipe with the appropriate spice blend and health considerations. This allows the user to enjoy a nostalgic taste again while also protecting their health.

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

[0081] Step 1:

[0082] Users use a terminal to input taste information about nostalgic dishes. In this process, users provide the dish name, taste characteristics, and specific memories in text format. This input data is stored on the terminal as information necessary for subsequent analysis.

[0083] Step 2:

[0084] The terminal verifies the taste information entered by the user and sends it to the server via a data communication protocol. Secure communication methods such as HTTPS are used for this transmission. At this time, the terminal converts the input data into the correct format before handing it over to the server.

[0085] Step 3:

[0086] The server receives taste information transmitted from the terminal. Based on the received data, it retrieves information on ingredients, seasonings, and regional taste trends from the database. This data collection is performed using SQL queries and stored on the server as datasets for the corresponding ingredients and seasonings.

[0087] Step 4:

[0088] The AI ​​model on the server analyzes the collected ingredient data and the user's taste preferences. This analysis process uses machine learning algorithms to quantify the taste profile desired by the user. Based on this taste profile, the AI ​​model outputs the optimal combination of ingredients and seasonings, which serves as reference data for the next processing step.

[0089] Step 5:

[0090] The server designs a healthy meal plan based on the generated taste profile and AI analysis results. At this stage, nutritional calculations are performed, and the salt and fat content is adjusted to meet health standards. The completed recipe is then generated as output and sent to the terminal.

[0091] Step 6:

[0092] The terminal displays recipe information received from the server in a user-friendly format. This display includes necessary ingredients, cooking instructions, and timing for using seasonings, providing an interface that allows users to cook smoothly.

[0093] Step 7:

[0094] Users attempt to prepare a dish based on a recipe presented through their device. They then input their results and impressions as feedback into the device. This feedback is recorded on the device as text information, including the quality of the dish, how well the taste was reproduced, and any other improvements made.

[0095] Step 8:

[0096] The device sends user feedback to the server. The server uses this feedback information to update the AI ​​model's training data and execute a process to improve the accuracy of subsequent analyses. This continuously improves the overall system performance.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In today's lifestyle, recreating nostalgic dishes from the past is important for providing emotional satisfaction and a healthy eating experience. However, it is difficult for users to provide accurate and easy-to-understand information and, based on that information, obtain appropriate recipes that take health into consideration. In particular, there is a need for a voice-enabled interface, but since its implementation is advanced, a system that solves these challenges is necessary.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for inputting taste information about nostalgic dishes from the user, means for acquiring ingredient information, seasoning information, and regional taste trend information, and means for acquiring the user's taste information using speech recognition technology. This makes it possible for users to easily register taste information about past dishes and be provided with highly personalized and healthy recipes.

[0102] A "user" is an individual who wishes to use this system to recreate nostalgic dishes.

[0103] "Taste information" refers to information based on the characteristics of the flavor of the dish the user wants to recreate, as well as personal anecdotes.

[0104] "Ingredient information" refers to detailed information about the components used as ingredients in a dish.

[0105] "Seasoning information" refers to detailed information about the seasonings used to flavor a dish.

[0106] "Regional taste trend information" refers to information about the general taste preferences and trends in a particular region.

[0107] An "artificial intelligence model" is an algorithm that analyzes user input information and generates healthy recipes.

[0108] "Voice recognition technology" is a technology that converts a user's voice into digital information and makes it usable in a system.

[0109] A "display device" is a device that provides the generated recipe to the user visually.

[0110] "Feedback" refers to the evaluations and opinions that users give regarding the output results of a system.

[0111] A "recipe" is a combination of ingredients and steps necessary to create a specific dish.

[0112] The system implementing this invention consists of a user, a terminal, and a server. The user can use a terminal, such as a household robot, to input taste information about nostalgic dishes through voice recognition. This terminal is equipped with voice recognition software and has technology for converting voice input into text (e.g., Google® Speech-to-Text API).

[0113] The server receives taste information sent by the user and retrieves necessary information from a database (e.g., MongoDB) containing information on ingredients, seasonings, and regional taste trends. Furthermore, it uses a generative AI model (e.g., PyTorch) within the server to analyze this information and create a taste profile that the user is seeking. Based on this profile, the server automatically generates health-conscious recipes.

[0114] The generated recipes are presented to the user visually and audibly by the device. The display device shows the recipes in a list format, and an audio output device (e.g., Google Text-to-Speech) provides feedback in an easy-to-understand format.

[0115] For example, if a user voice-inputs, "I want to make the creamy curry I ate in college," the robot can use that information to communicate with a server and generate the appropriate spice combination and cooking procedure. In this case, the prompt would be, "I want to make creamy curry, what spices do I need?"

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user verbally inputs taste information about nostalgic dishes through the voice recognition function of a home robot. This input is converted into text format by the voice recognition software. The input is the user's voice, and the output is text data.

[0119] Step 2:

[0120] The terminal sends transcribed taste information to the server. The input is text data generated by the user, and the output is data sent to the server. This includes prompts such as, "I want to make a creamy curry, what spices do I need?"

[0121] Step 3:

[0122] The server analyzes the received text data and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The input is text data, and the output is a dataset containing various types of information. Here, data extraction is performed using database queries.

[0123] Step 4:

[0124] The server inputs the acquired data into a generating AI model to create a taste profile desired by the user. The input is a dataset, and the output is a quantified taste profile. Here, data processing is performed by a machine learning model.

[0125] Step 5:

[0126] The server considers healthy ingredient and seasoning combinations based on the generated taste profile and creates a recipe. The input is the taste profile, and the output is a detailed recipe. Optimization is performed using a combination algorithm.

[0127] Step 6:

[0128] The generated recipe is sent to the terminal and presented to the user visually and audibly. The input is recipe data, and the output is information display and audio guidance for the user. The terminal provides information to the user using a display device and an audio output device.

[0129] Step 7:

[0130] Users try cooking using the provided recipes and input the results and their impressions into their device. This feedback is sent back to the server and used to improve the model. The input is the user's feedback, and the output is the revised model. The server updates the machine learning model to improve accuracy for the next attempt.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] The system of the present invention includes a user, a terminal, a server, and an emotion engine. The user inputs taste information about a nostalgic dish through the terminal. The input information includes the specific taste characteristics of the dish to be recreated, related episodes, and emotions. Based on this information, the terminal uses the emotion engine to analyze the user's emotional state.

[0133] The emotion engine recognizes the user's emotions from their facial expressions and tone of voice, and sends this information, along with taste information, to the server. The server uses the received emotion and taste information to retrieve ingredient and seasoning information from the ingredient database. It also collects regional taste trend information to reflect the regional characteristics of the flavor the user wants to recreate.

[0134] An artificial intelligence model on the server comprehensively analyzes all information and generates a taste profile of the dish the user desires. This generated profile, taking into account the user's emotional state, contributes to creating a more personalized recipe. This recipe takes into account healthy ingredient selection and seasoning combinations, resulting in a health-conscious approach.

[0135] The server then sends the generated recipe to the terminal, which displays it to the user in an easy-to-understand visual format. The user tries the recipe and inputs the results and any new emotions into the terminal. The terminal sends the emotion information along with the feedback to the server, which helps to further improve the artificial intelligence model.

[0136] For example, if a user has been away from their hometown for a long time and wants to recreate a special dish they used to eat there, they input information including their feelings and memories associated with that dish. Based on this emotional information, the server generates a recipe that provides a taste that enhances feelings of relaxation and happiness, and provides it to the user. This system allows users to go beyond simply recreating a physical taste and gain a dining experience that is tailored to their personal emotions.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] Users input information about the taste of nostalgic dishes and the emotions associated with those dishes through their device. The device also features emotion recognition capabilities to analyze the user's facial expressions and tone of voice.

[0140] Step 2:

[0141] The device analyzes the user's emotional state, obtained through an emotion engine, along with the user's taste information, and sends this information to the server.

[0142] Step 3:

[0143] The server uses the received taste and emotion information to access the ingredient database and retrieve relevant ingredient and seasoning information. At this stage, it also collects data on regionally specific tastes.

[0144] Step 4:

[0145] An artificial intelligence model on the server integrates and analyzes taste and emotional information. From this information, the model generates a desired taste profile and designs a recipe optimized according to the user's emotions.

[0146] Step 5:

[0147] The server sends the generated recipe to the device. The device displays the recipe in a format that is easy for the user to follow. This display includes adjustment points that take into account the user's emotional state, as well as recommended ingredient variations tailored to specific situations.

[0148] Step 6:

[0149] Users try out recipes provided by their devices and input the results, their impressions of the experience, and any changes in their emotions.

[0150] Step 7:

[0151] The device then sends user feedback and changed emotional information back to the server. This allows the server to update its artificial intelligence model and improve the accuracy of its analysis for the next recipe suggestion.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] In modern society, people often leave their hometowns for various reasons, and it can become difficult to recreate familiar flavors as time passes. As a result, individuals have fewer opportunities to experience the emotional satisfaction derived from flavors they once enjoyed, leading to a decline in their emotional well-being. This invention aims to enable users to rediscover emotional satisfaction through nostalgic food experiences by analyzing their individual emotions and taste preferences in detail and providing individually optimized recipes.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for inputting emotional and taste information related to food from the user, means for recognizing the emotional state by analyzing facial expressions and voice, and means for acquiring taste tendencies of ingredients, seasonings, and region. This makes it possible to generate an optimal taste profile that is tailored to the user's emotional state, thereby providing a nostalgic dining experience and improving the individual's emotional satisfaction.

[0157] A "user" refers to an individual who uses the system to provide information about nostalgic dishes and receives personalized recipes.

[0158] "Emotions" refers to information that indicates a user's mental state or mood, analyzed from their facial expressions, tone of voice, and other factors.

[0159] "Taste information" refers to information that includes the specific flavor characteristics of the dish you want to recreate, as well as personal anecdotes related to that dish.

[0160] "Analyzing facial expressions and voice" refers to the process of using technical means to analyze the user's facial expressions and voice tone to recognize their emotional state.

[0161] "Ingredients" refers to the individual substances or materials that make up a cooking recipe.

[0162] "Seasonings" refer to substances used to improve or alter the taste of food ingredients.

[0163] "Regional taste trends" refer to information that indicates the taste preferences and cultural characteristics generally accepted in a particular geographical area.

[0164] An "artificial intelligence model" refers to an algorithm or program that analyzes user input and automatically generates optimized recipes.

[0165] "Feedback" refers to the reactions and opinions based on the user's experience after trying out a provided recipe.

[0166] The system of the present invention includes a user, a terminal, a server, and an emotion engine as its main components. The user uses the terminal to input taste information and associated emotions about the dish they wish to recreate. The information input by the user includes the name of the dish, its flavor characteristics, a memorable anecdote, and the emotions associated with that dish. An example of a prompt statement is, "The sweet and spicy curry my mother made, its aroma fills me with happiness."

[0167] The terminal analyzes the user's emotional state based on the input information using its built-in emotion engine. The hardware used here includes a camera and microphone, while the software is a program equipped with facial recognition and voice analysis technologies. The emotional data and taste information obtained from this analysis are transmitted to a server using wireless or wired communication.

[0168] The server searches for relevant information from its database of ingredients and seasonings based on the received emotional and gustatory data, and also references regional taste trend information. This allows it to reflect regional flavor characteristics in the dish the user desires. The server incorporates a generative AI model that enables complex data analysis, thereby generating an optimized taste profile. In this process, the AI ​​model takes the user's emotions into consideration and generates the optimal recipe to enhance feelings of relaxation and happiness.

[0169] Ultimately, the server sends the generated recipe to the device, which displays it to the user in a visually easy-to-understand format. The user tries the recipe and provides feedback to the server's AI model by inputting the results and any newly arising emotions back into the device. This feedback is analyzed by the server and used to further improve the model.

[0170] This allows users to not only recreate flavors but also gain a rich dining experience that resonates with their own emotions.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user uses a terminal to input taste information of the dish they want to recreate, along with related personal feelings, in the form of prompt sentences. The information input includes the name of the dish, its flavor characteristics, related anecdotes, and emotional state. As a concrete example, the user inputs the prompt sentence, "The sweet and spicy curry my mother made for me; its aroma envelops me in happiness."

[0174] Step 2:

[0175] The device receives the input prompt and uses an emotion engine to analyze the user's emotional state. The input here consists of the user's facial expressions and tone of voice, which the emotion engine processes and converts into digital information. Through data analysis, the device generates emotional data and sends it to the server along with taste information. In this process, the camera and microphone play a specific role.

[0176] Step 3:

[0177] The server uses taste and emotional data received from the terminal as input to search for relevant information in its database of ingredients and seasonings. It also refers to a regional taste preference database to reflect regional flavor characteristics. Based on these inputs, the server performs data processing called database matching and outputs candidate ingredient and seasoning information.

[0178] Step 4:

[0179] A generative AI model on the server analyzes the collected information to generate a taste profile. At this stage, the input consists of information on ingredients, seasonings, and regional taste tendencies obtained in previous steps. Based on the data analysis, the generative AI model outputs an optimized recipe. The specific operation is the execution of this analysis process.

[0180] Step 5:

[0181] The server sends the generated recipe to the terminal. The output information is a visualized recipe for the user. The terminal receives this recipe and displays it in a way that is easy for the user to understand visually. Specifically, the display handles the visual presentation.

[0182] Step 6:

[0183] The user tries out a recipe on their device and actually cooks the dish. After trying the dish, the user re-enters the results and any new emotions that arise towards the dish into the device. This input is feedback and the associated emotional state.

[0184] Step 7:

[0185] The device sends new emotional information back to the server based on user feedback. The server analyzes this feedback as input and processes the data to improve the AI ​​model. Specifically, this involves feedback analysis and AI model adjustment.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0188] In modern society, where many people lead busy lives, recreating nostalgic dishes is often cumbersome and difficult. Furthermore, while taste is important, emotions are also crucial, and there is a lack of services that cater to these needs. Additionally, there is a need for convenient ways to enjoy individually optimized, health-conscious meals.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes means for inputting taste and emotional information related to nostalgic dishes from the user, means for using an emotion engine to analyze the emotional state, and means for acquiring ingredient information, seasoning information, and regional taste trend information. This makes it possible to generate healthy and optimized recipes that take into account the user's emotional state and to order delivery of those dishes.

[0191] "Taste information related to nostalgic dishes" refers to data that indicates characteristics related to the taste of a specific past dish that the user wishes to recreate.

[0192] "Emotional information" refers to data that reflects the user's emotional state, such as their facial expressions and voice.

[0193] An "emotion engine" refers to a function or program used to analyze a user's emotional state.

[0194] "Ingredient information" refers to data about ingredients that may be used in a particular dish.

[0195] "Seasoning information" refers to data about the ingredients used to flavor a dish.

[0196] "Regional taste trend information" refers to data that shows the general taste preferences in a particular region or culture.

[0197] An "artificial intelligence model" refers to an algorithm or program used to analyze information obtained from users and generate optimal results.

[0198] "Visual presentation" refers to representing the generated recipe visually in a way that is easy for the user to understand.

[0199] "Delivery order" refers to the process of delivering food requested by a user to a specified location.

[0200] "Feedback" refers to the act of users providing opinions and comments on generated recipes and dishes.

[0201] An "emotional profile" is data that quantifies or identifies a specific user's emotional state, generated based on the user's emotional information.

[0202] The system for implementing this invention primarily utilizes a user terminal, a server including an emotion engine, and a food ingredient database. Users input information about nostalgic dishes via their smartphones or computers. This input includes specific taste characteristics of the dish they wish to recreate and anecdotes related to their emotions. This ensures that the system accurately reflects the user's individual emotional state.

[0203] The device transmits this information to the emotion engine, which analyzes the user's emotional state. The emotion engine recognizes the user's facial expressions and tone of voice, and collects emotional information based on this. Subsequently, the server comprehensively analyzes the received taste and emotional information.

[0204] An artificial intelligence model (e.g., TensorFlow or PyTorch) on the server generates the optimal recipe based on this information. This process involves building a healthy and personalized taste profile and retrieving appropriate ingredient and seasoning information from a database. Furthermore, by considering regional taste trends, it becomes possible to create recipes that meet user preferences.

[0205] The generated recipe is presented to the user visually through their device. The user can review the recipe and order the food via delivery. At this time, they can also input more detailed feedback and new emotional states, and this data is sent to the server and used to improve the AI ​​model.

[0206] For example, if a user wants to recreate a dish that evokes memories shared with someone they were close to in the past, they would comprehensively input their feelings about that dish. Based on that emotional state, the system would make suggestions that would bring about feelings of happiness or comfort. An example of a prompt might be, "I want to recreate the taste of my grandmother's nikujaga (meat and potato stew) that makes me feel happy. Please give me a recipe that emphasizes specific aromas and flavors that will bring back happy memories from the past."

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The user uses a device to input taste and emotional information about nostalgic dishes. The user describes the taste characteristics and emotions associated with a specific dish, and the device collects this data. The input data includes the dish name, taste details, and emotional anecdotes. This data forms the basis for the next processing step.

[0210] Step 2:

[0211] The device sends user-input data to the emotion engine, which then analyzes the user's emotional state. The emotion engine utilizes facial recognition and voice analysis technologies to analyze the user's facial expressions and voice tone. Here, the input is the user's emotional characteristics, and the output is a profile that quantifies the user's emotional state.

[0212] Step 3:

[0213] The server receives emotional profiles and taste information, and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The server uses this data to collect basic recipe information. The output aggregates all the information necessary for recipe generation.

[0214] Step 4:

[0215] Based on the collected information, the server utilizes a generative AI model to construct a recipe profile optimized for the user's requests. Here, the input is the user's taste information and emotional profile, and the output is a healthy and personalized recipe suggestion. The model performs multidimensional analysis to reproduce appropriate flavors based on the user's nasalgia.

[0216] Step 5:

[0217] The server sends the generated recipe to the terminal, which then visually presents it to the user. The user can review the displayed recipe and make adjustments or place a delivery order as needed. The output is recipe information that the user can visually understand.

[0218] Step 6:

[0219] After trying a recipe, users enter and submit feedback on their device. This feedback includes the quality of the dish and their overall satisfaction. Based on this feedback, the system collects data to make further improvements.

[0220] Step 7:

[0221] The server uses user feedback and newly entered sentiment information to update and improve the generating AI model. This lays the foundation for providing a more refined service for future recipe generation. The model learns autonomously and improves its accuracy over time.

[0222] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0223] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0224] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0228] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0229] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0230] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0231] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0232] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0233] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0234] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0235] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0236] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0237] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0238] The system of this invention consists of a user, a terminal, and a server. The user inputs taste information about a nostalgic dish through the terminal. This information includes the taste characteristics of the dish to be recreated and related anecdotes. The terminal confirms this information and transmits it to the server.

[0239] The server retrieves ingredient and seasoning information from an ingredient database along with the received taste information. It also simultaneously collects regional taste trend information and combines this data. An artificial intelligence model within the server analyzes this information to generate a taste profile of the dish the user desires. This taste profile includes a numerical representation of each taste element and takes into account the overall balance of flavors.

[0240] Next, the server uses the generated flavor profile to consider healthy and appropriate combinations of ingredients and seasonings and creates a recipe. This recipe is adjusted with health in mind, paying particular attention to the balance of salt and fat. It also details the cooking procedure and the timing of adding seasonings.

[0241] The server sends the completed recipe to the device. The device displays the received recipe in an easy-to-read format for the user. The user tries the recipe and inputs the results and feedback into the device. The feedback is returned to the server and used by the artificial intelligence model for further improvement.

[0242] For example, if a user wants to recreate the curry from a cafeteria they frequented during their student days, they input detailed taste information about that curry into a terminal. The server analyzes this information to determine the specific spice blends and ingredient combinations, and generates a recipe that recreates the nostalgic taste in a healthy way. This system allows users to enjoy the taste of the past while experiencing it without sacrificing their health.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] Users input information about the taste of nostalgic dishes through their devices. This information includes specific taste characteristics and memories associated with the dish they want to recreate.

[0246] Step 2:

[0247] The terminal converts the input taste information into an appropriate format and sends it to the server. During the conversion process, it checks for any missing or incorrect information.

[0248] Step 3:

[0249] Based on the taste information it receives, the server accesses the ingredient database to retrieve relevant ingredient and seasoning information. During this process, it also queries for data necessary for recreating the dish, such as seasonality and nutritional value.

[0250] Step 4:

[0251] The server collects information on regional taste trends and gathers data that reflects the regional characteristics of the flavors that users want to recreate.

[0252] Step 5:

[0253] An artificial intelligence model on the server analyzes the collected information and generates a taste profile of the dish the user desires. The profile includes a numerical representation of the balance of each taste element.

[0254] Step 6:

[0255] Based on the generated flavor profile, the server creates a recipe that combines appropriate ingredients and seasonings, taking health into consideration. The recipe includes cooking instructions and points to note.

[0256] Step 7:

[0257] The server sends the completed recipe to the terminal. The terminal displays the received recipe to the user in an easy-to-understand visual format.

[0258] Step 8:

[0259] Users try cooking using the provided recipes via their devices and input their results and feedback. The interface is designed to easily reflect what they felt through their experience.

[0260] Step 9:

[0261] The device sends user feedback back to the server. The server feeds this feedback into an artificial intelligence model, which is then used to improve recipes for future use.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] Recreating the nostalgic flavors of dishes that many people have experienced in the past usually relies on individual taste perception, which is time-consuming and laborious, and often lacks sufficient consideration for health. In particular, there is a need to automatically generate healthy meal plans that effectively utilize memory-based taste information.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes means for acquiring information on ingredients, seasonings, and regional taste trends; means for combining and analyzing this information to generate a healthy meal plan, and means for improving the intelligent model by utilizing input feedback. This makes it possible to provide healthy meals while recreating past flavors and to optimize recipes to meet the user's taste needs.

[0267] A "user" is the entity that uses the system to recreate nostalgic dishes.

[0268] "Nostalgic cuisine" refers to the taste sensations experienced by users when eating specific dishes in the past.

[0269] "Taste information" refers to information entered by the user, such as the specific taste characteristics of a dish or associated memories.

[0270] A "terminal" is a device used by users to input taste information and receive the results of a dish's reproduction.

[0271] A "server" is a computer system that processes taste information and generates cooking plans using intelligent models.

[0272] "Ingredient information" refers to detailed information about the substances and components needed to recreate a particular dish.

[0273] "Seasoning information" refers to detailed information about the ingredients used to adjust the flavor of a dish.

[0274] "Regional taste trend information" refers to data that shows the general characteristics and trends of taste in a specific region.

[0275] An "intelligent model" is a computational model that analyzes input information and reproduces the taste of the dish the user desires.

[0276] A "meal plan" is a list of specific steps and necessary ingredients for a dish that the user wants to recreate.

[0277] "Feedback" refers to the input of results and impressions from users who have actually tried making the dishes.

[0278] This invention is a system that recreates the taste of a nostalgic dish a user has experienced in the past, and consists of a user, a terminal, and a server. The user can use the terminal to input detailed taste information about the dish they wish to recreate. This input is provided to the terminal as text information, including the characteristics of the dish's taste and associated memories. The terminal is responsible for transmitting the input information to the server.

[0279] The server receives the taste information sent by the user and collects ingredient information, seasoning information, and regional taste trend information from the database system based on this. SQL or similar database query languages are used for this data collection. The collected data is analyzed by the generative AI model in the server, and a taste profile of the dish desired by the user is generated. This profile is generated by a neural network using machine learning frameworks such as TensorFlow or PyTorch.

[0280] Based on the generated taste profile, the server creates a healthy and nutritious meal plan. Here, a Python script is used to automatically calculate the nutritional balance, and recipes considering health aspects are generated. This includes adjustments to salt and fat content in particular. The server finally sends this recipe to the terminal, and the terminal presents the recipe to the user in an easy-to-view format.

[0281] The user tries to cook the dish based on the provided recipe and inputs the result as feedback to the terminal. The terminal sends this feedback back to the server, and the server uses this data to further improve the AI model. Through this process, the taste reproducibility and health aspects of the provided recipes continue to improve.

[0282] As a specific example, when the user wants to reproduce the curry that was a regular in their student days, they input the taste information into the terminal. An example of the prompt text could be something like "I want to reproduce the fragrant spices and sweetness of the curry in the school cafeteria I used to eat in during my student days". Based on this information, the server's AI model generates an appropriate spice blend and a recipe considering health. This allows the user to enjoy the nostalgic taste again and also maintain their health.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user uses the terminal to input taste information about nostalgic dishes. At this time, the user provides the dish name, taste characteristics, specific memories, etc. in text form. This input data is stored in the terminal as information necessary for subsequent analysis.

[0286] Step 2:

[0287] The terminal checks the taste information input by the user and sends it to the server via the data communication protocol. A secure communication method such as HTTPS is used for this transmission. At this time, the terminal converts the input data into the correct format and passes it to the server.

[0288] Step 3:

[0289] The server receives the taste information sent from the terminal. Based on the received data, it retrieves ingredient information, seasoning information, and regional taste preference information from the database. This data collection is executed by an SQL query and is stored in the server as a dataset of the corresponding ingredients and seasonings.

[0290] Step 4:

[0291] The generation AI model in the server analyzes the collected ingredient data and the user's taste information. In this analysis process, a machine learning algorithm is used to quantify the taste profile required by the user. The AI model outputs the optimal combination of ingredients and seasonings based on this taste profile and uses it as reference data for the next processing step. [[ID=2​​​​​​​​​​​​ The terminal displays recipe information received from the server in a user-friendly format. This display includes necessary ingredients, cooking instructions, and timing for using seasonings, providing an interface that allows users to cook smoothly.

[0296] Step 7:

[0297] Users attempt to prepare a dish based on a recipe presented through their device. They then input their results and impressions as feedback into the device. This feedback is recorded on the device as text information, including the quality of the dish, how well the taste was reproduced, and any other improvements made.

[0298] Step 8:

[0299] The device sends user feedback to the server. The server uses this feedback information to update the AI ​​model's training data and execute a process to improve the accuracy of subsequent analyses. This continuously improves the overall system performance.

[0300] (Application Example 1)

[0301] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0302] In today's lifestyle, recreating nostalgic dishes from the past is important for providing emotional satisfaction and a healthy eating experience. However, it is difficult for users to provide accurate and easy-to-understand information and, based on that information, obtain appropriate recipes that take health into consideration. In particular, there is a need for a voice-enabled interface, but since its implementation is advanced, a system that solves these challenges is necessary.

[0303] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0304] In this invention, the server includes means for the user to input taste information regarding nostalgic dishes, means for obtaining ingredient information, seasoning information, and taste trend information of the region, and means for obtaining the user's taste information using speech recognition technology. As a result, it becomes possible for the user to easily register the taste information of past dishes and be provided with highly personalized and healthy recipes.

[0305] A "user" is an individual who wishes to reproduce nostalgic dishes using this system.

[0306] "Taste information" is information based on the taste characteristics of the dishes the user wishes to reproduce and personal episodes.

[0307] "Ingredient information" is detailed information on the ingredients used as materials for dishes.

[0308] "Seasoning information" is detailed information on the seasonings used for flavoring dishes.

[0309] "Taste trend information of the region" is information regarding the general taste preferences and trends in a specific region.

[0310] An "artificial intelligence model" is an algorithm for analyzing the user's input information and generating healthy recipes.

[0311] "Speech recognition technology" is technology for converting the user's voice into digital information and making it available in a format that can be used by the system.

[0312] A "display device" is a device for visually providing the generated recipe to the user.

[0313] "Feedback" is an evaluation or opinion given by the user regarding the output result of the system.

[0314] A "recipe" is a combination of the materials and procedures necessary to create a specific dish.

[0315] The system implementing this invention consists of a user, a terminal, and a server. The user can use a terminal, such as a household robot, to input taste information about nostalgic dishes through voice recognition. This terminal is equipped with voice recognition software and has technology for converting voice input into text (e.g., Google Speech-to-Text API).

[0316] The server receives taste information sent by the user and retrieves necessary information from a database (e.g., MongoDB) containing information on ingredients, seasonings, and regional taste trends. Furthermore, it uses a generative AI model (e.g., PyTorch) within the server to analyze this information and create a taste profile that the user is seeking. Based on this profile, the server automatically generates health-conscious recipes.

[0317] The generated recipes are presented to the user visually and audibly by the device. The display device shows the recipes in a list format, and an audio output device (e.g., Google Text-to-Speech) provides feedback in an easy-to-understand format.

[0318] For example, if a user voice-inputs, "I want to make the creamy curry I ate in college," the robot can use that information to communicate with a server and generate the appropriate spice combination and cooking procedure. In this case, the prompt would be, "I want to make creamy curry, what spices do I need?"

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user verbally inputs taste information about nostalgic dishes through the voice recognition function of a home robot. This input is converted into text format by the voice recognition software. The input is the user's voice, and the output is text data.

[0322] Step 2:

[0323] The terminal sends transcribed taste information to the server. The input is text data generated by the user, and the output is data sent to the server. This includes prompts such as, "I want to make a creamy curry, what spices do I need?"

[0324] Step 3:

[0325] The server analyzes the received text data and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The input is text data, and the output is a dataset containing various types of information. Here, data extraction is performed using database queries.

[0326] Step 4:

[0327] The server inputs the acquired data into a generating AI model to create a taste profile desired by the user. The input is a dataset, and the output is a quantified taste profile. Here, data processing is performed by a machine learning model.

[0328] Step 5:

[0329] The server considers healthy ingredient and seasoning combinations based on the generated taste profile and creates a recipe. The input is the taste profile, and the output is a detailed recipe. Optimization is performed using a combination algorithm.

[0330] Step 6:

[0331] The generated recipe is sent to the terminal and presented to the user visually and audibly. The input is recipe data, and the output is information display and audio guidance for the user. The terminal provides information to the user using a display device and an audio output device.

[0332] Step 7:

[0333] Users try cooking using the provided recipes and input the results and their impressions into their device. This feedback is sent back to the server and used to improve the model. The input is the user's feedback, and the output is the revised model. The server updates the machine learning model to improve accuracy for the next attempt.

[0334] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0335] The system of the present invention includes a user, a terminal, a server, and an emotion engine. The user inputs taste information about a nostalgic dish through the terminal. The input information includes the specific taste characteristics of the dish to be recreated, related episodes, and emotions. Based on this information, the terminal uses the emotion engine to analyze the user's emotional state.

[0336] The emotion engine recognizes the user's emotions from their facial expressions and tone of voice, and sends this information, along with taste information, to the server. The server uses the received emotion and taste information to retrieve ingredient and seasoning information from the ingredient database. It also collects regional taste trend information to reflect the regional characteristics of the flavor the user wants to recreate.

[0337] An artificial intelligence model on the server comprehensively analyzes all information and generates a taste profile of the dish the user desires. This generated profile, taking into account the user's emotional state, contributes to creating a more personalized recipe. This recipe takes into account healthy ingredient selection and seasoning combinations, resulting in a health-conscious approach.

[0338] The server then sends the generated recipe to the terminal, which displays it to the user in an easy-to-understand visual format. The user tries the recipe and inputs the results and any new emotions into the terminal. The terminal sends the emotion information along with the feedback to the server, which helps to further improve the artificial intelligence model.

[0339] For example, if a user has been away from their hometown for a long time and wants to recreate a special dish they used to eat there, they input information including their feelings and memories associated with that dish. Based on this emotional information, the server generates a recipe that provides a taste that enhances feelings of relaxation and happiness, and provides it to the user. This system allows users to go beyond simply recreating a physical taste and gain a dining experience that is tailored to their personal emotions.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] Users input information about the taste of nostalgic dishes and the emotions associated with those dishes through their device. The device also features emotion recognition capabilities to analyze the user's facial expressions and tone of voice.

[0343] Step 2:

[0344] The device analyzes the user's emotional state, obtained through an emotion engine, along with the user's taste information, and sends this information to the server.

[0345] Step 3:

[0346] The server uses the received taste and emotion information to access the ingredient database and retrieve relevant ingredient and seasoning information. At this stage, it also collects data on regionally specific tastes.

[0347] Step 4:

[0348] An artificial intelligence model on the server integrates and analyzes taste and emotional information. From this information, the model generates a desired taste profile and designs a recipe optimized according to the user's emotions.

[0349] Step 5:

[0350] The server sends the generated recipe to the device. The device displays the recipe in a format that is easy for the user to follow. This display includes adjustment points that take into account the user's emotional state, as well as recommended ingredient variations tailored to specific situations.

[0351] Step 6:

[0352] Users try out recipes provided by their devices and input the results, their impressions of the experience, and any changes in their emotions.

[0353] Step 7:

[0354] The device then sends user feedback and changed emotional information back to the server. This allows the server to update its artificial intelligence model and improve the accuracy of its analysis for the next recipe suggestion.

[0355] (Example 2)

[0356] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0357] In modern society, people often leave their hometowns for various reasons, and it can become difficult to recreate familiar flavors as time passes. As a result, individuals have fewer opportunities to experience the emotional satisfaction derived from flavors they once enjoyed, leading to a decline in their emotional well-being. This invention aims to enable users to rediscover emotional satisfaction through nostalgic food experiences by analyzing their individual emotions and taste preferences in detail and providing individually optimized recipes.

[0358] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0359] In this invention, the server includes means for inputting emotional and taste information related to food from the user, means for recognizing the emotional state by analyzing facial expressions and voice, and means for acquiring taste tendencies of ingredients, seasonings, and region. This makes it possible to generate an optimal taste profile that is tailored to the user's emotional state, thereby providing a nostalgic dining experience and improving the individual's emotional satisfaction.

[0360] A "user" refers to an individual who uses the system to provide information about nostalgic dishes and receives personalized recipes.

[0361] "Emotions" refers to information that indicates a user's mental state or mood, analyzed from their facial expressions, tone of voice, and other factors.

[0362] "Taste information" refers to information that includes the specific flavor characteristics of the dish you want to recreate, as well as personal anecdotes related to that dish.

[0363] "Analyzing facial expressions and voice" refers to the process of using technical means to analyze the user's facial expressions and voice tone to recognize their emotional state.

[0364] "Ingredients" refers to the individual substances or materials that make up a cooking recipe.

[0365] "Seasonings" refer to substances used to improve or alter the taste of food ingredients.

[0366] "Regional taste trends" refer to information that indicates the taste preferences and cultural characteristics generally accepted in a particular geographical area.

[0367] An "artificial intelligence model" refers to an algorithm or program that analyzes user input and automatically generates optimized recipes.

[0368] "Feedback" refers to the reactions and opinions based on the user's experience after trying out a provided recipe.

[0369] The system of the present invention includes a user, a terminal, a server, and an emotion engine as its main components. The user uses the terminal to input taste information and associated emotions about the dish they wish to recreate. The information input by the user includes the name of the dish, its flavor characteristics, a memorable anecdote, and the emotions associated with that dish. An example of a prompt statement is, "The sweet and spicy curry my mother made, its aroma fills me with happiness."

[0370] The terminal analyzes the user's emotional state based on the input information using its built-in emotion engine. The hardware used here includes a camera and microphone, while the software is a program equipped with facial recognition and voice analysis technologies. The emotional data and taste information obtained from this analysis are transmitted to a server using wireless or wired communication.

[0371] The server searches for relevant information from its database of ingredients and seasonings based on the received emotional and gustatory data, and also references regional taste trend information. This allows it to reflect regional flavor characteristics in the dish the user desires. The server incorporates a generative AI model that enables complex data analysis, thereby generating an optimized taste profile. In this process, the AI ​​model takes the user's emotions into consideration and generates the optimal recipe to enhance feelings of relaxation and happiness.

[0372] Ultimately, the server sends the generated recipe to the device, which displays it to the user in a visually easy-to-understand format. The user tries the recipe and provides feedback to the server's AI model by inputting the results and any newly arising emotions back into the device. This feedback is analyzed by the server and used to further improve the model.

[0373] This allows users to not only recreate flavors but also gain a rich dining experience that resonates with their own emotions.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The user uses a terminal to input taste information of the dish they want to recreate, along with related personal feelings, in the form of prompt sentences. The information input includes the name of the dish, its flavor characteristics, related anecdotes, and emotional state. As a concrete example, the user inputs the prompt sentence, "The sweet and spicy curry my mother made for me; its aroma envelops me in happiness."

[0377] Step 2:

[0378] The device receives the input prompt and uses an emotion engine to analyze the user's emotional state. The input here consists of the user's facial expressions and tone of voice, which the emotion engine processes and converts into digital information. Through data analysis, the device generates emotional data and sends it to the server along with taste information. In this process, the camera and microphone play a specific role.

[0379] Step 3:

[0380] The server uses taste and emotional data received from the terminal as input to search for relevant information in its database of ingredients and seasonings. It also refers to a regional taste preference database to reflect regional flavor characteristics. Based on these inputs, the server performs data processing called database matching and outputs candidate ingredient and seasoning information.

[0381] Step 4:

[0382] A generative AI model on the server analyzes the collected information to generate a taste profile. At this stage, the input consists of information on ingredients, seasonings, and regional taste tendencies obtained in previous steps. Based on the data analysis, the generative AI model outputs an optimized recipe. The specific operation is the execution of this analysis process.

[0383] Step 5:

[0384] The server sends the generated recipe to the terminal. The output information is a visualized recipe for the user. The terminal receives this recipe and displays it in a way that is easy for the user to understand visually. Specifically, the display handles the visual presentation.

[0385] Step 6:

[0386] The user tries out a recipe on their device and actually cooks the dish. After trying the dish, the user re-enters the results and any new emotions that arise towards the dish into the device. This input is feedback and the associated emotional state.

[0387] Step 7:

[0388] The device sends new emotional information back to the server based on user feedback. The server analyzes this feedback as input and processes the data to improve the AI ​​model. Specifically, this involves feedback analysis and AI model adjustment.

[0389] (Application Example 2)

[0390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0391] In modern society, where many people lead busy lives, recreating nostalgic dishes is often cumbersome and difficult. Furthermore, while taste is important, emotions are also crucial, and there is a lack of services that cater to these needs. Additionally, there is a need for convenient ways to enjoy individually optimized, health-conscious meals.

[0392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0393] In this invention, the server includes means for inputting taste and emotional information related to nostalgic dishes from the user, means for using an emotion engine to analyze the emotional state, and means for acquiring ingredient information, seasoning information, and regional taste trend information. This makes it possible to generate healthy and optimized recipes that take into account the user's emotional state and to order delivery of those dishes.

[0394] "Taste information related to nostalgic dishes" refers to data that indicates characteristics related to the taste of a specific past dish that the user wishes to recreate.

[0395] "Emotional information" refers to data that reflects the user's emotional state, such as their facial expressions and voice.

[0396] An "emotion engine" refers to a function or program used to analyze a user's emotional state.

[0397] "Ingredient information" refers to data about ingredients that may be used in a particular dish.

[0398] "Seasoning information" refers to data about the ingredients used to flavor a dish.

[0399] "Regional taste trend information" refers to data that shows the general taste preferences in a particular region or culture.

[0400] An "artificial intelligence model" refers to an algorithm or program used to analyze information obtained from users and generate optimal results.

[0401] "Visual presentation" refers to representing the generated recipe visually in a way that is easy for the user to understand.

[0402] "Delivery order" refers to the process of delivering food requested by a user to a specified location.

[0403] "Feedback" refers to the act of users providing opinions and comments on generated recipes and dishes.

[0404] An "emotional profile" is data that quantifies or identifies a specific user's emotional state, generated based on the user's emotional information.

[0405] The system for implementing this invention primarily utilizes a user terminal, a server including an emotion engine, and a food ingredient database. Users input information about nostalgic dishes via their smartphones or computers. This input includes specific taste characteristics of the dish they wish to recreate and anecdotes related to their emotions. This ensures that the system accurately reflects the user's individual emotional state.

[0406] The device transmits this information to the emotion engine, which analyzes the user's emotional state. The emotion engine recognizes the user's facial expressions and tone of voice, and collects emotional information based on this. Subsequently, the server comprehensively analyzes the received taste and emotional information.

[0407] An artificial intelligence model (e.g., TensorFlow or PyTorch) on the server generates the optimal recipe based on this information. This process involves building a healthy and personalized taste profile and retrieving appropriate ingredient and seasoning information from a database. Furthermore, by considering regional taste trends, it becomes possible to create recipes that meet user preferences.

[0408] The generated recipe is presented to the user visually through their device. The user can review the recipe and order the food via delivery. At this time, they can also input more detailed feedback and new emotional states, and this data is sent to the server and used to improve the AI ​​model.

[0409] For example, if a user wants to recreate a dish that evokes memories shared with someone they were close to in the past, they would comprehensively input their feelings about that dish. Based on that emotional state, the system would make suggestions that would bring about feelings of happiness or comfort. An example of a prompt might be, "I want to recreate the taste of my grandmother's nikujaga (meat and potato stew) that makes me feel happy. Please give me a recipe that emphasizes specific aromas and flavors that will bring back happy memories from the past."

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The user uses a device to input taste and emotional information about nostalgic dishes. The user describes the taste characteristics and emotions associated with a specific dish, and the device collects this data. The input data includes the dish name, taste details, and emotional anecdotes. This data forms the basis for the next processing step.

[0413] Step 2:

[0414] The device sends user-input data to the emotion engine, which then analyzes the user's emotional state. The emotion engine utilizes facial recognition and voice analysis technologies to analyze the user's facial expressions and voice tone. Here, the input is the user's emotional characteristics, and the output is a profile that quantifies the user's emotional state.

[0415] Step 3:

[0416] The server receives emotional profiles and taste information, and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The server uses this data to collect basic recipe information. The output aggregates all the information necessary for recipe generation.

[0417] Step 4:

[0418] Based on the collected information, the server utilizes a generative AI model to construct a recipe profile optimized for the user's requests. Here, the input is the user's taste information and emotional profile, and the output is a healthy and personalized recipe suggestion. The model performs multidimensional analysis to reproduce appropriate flavors based on the user's nasalgia.

[0419] Step 5:

[0420] The server sends the generated recipe to the terminal, which then visually presents it to the user. The user can review the displayed recipe and make adjustments or place a delivery order as needed. The output is recipe information that the user can visually understand.

[0421] Step 6:

[0422] After trying a recipe, users enter and submit feedback on their device. This feedback includes the quality of the dish and their overall satisfaction. Based on this feedback, the system collects data to make further improvements.

[0423] Step 7:

[0424] The server uses user feedback and newly entered sentiment information to update and improve the generating AI model. This lays the foundation for providing a more refined service for future recipe generation. The model learns autonomously and improves its accuracy over time.

[0425] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0426] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0427] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0431] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0432] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0433] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0434] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0435] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0436] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0437] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0438] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0439] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0440] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0441] The system of this invention consists of a user, a terminal, and a server. The user inputs taste information about a nostalgic dish through the terminal. This information includes the taste characteristics of the dish to be recreated and related anecdotes. The terminal confirms this information and transmits it to the server.

[0442] The server retrieves ingredient and seasoning information from an ingredient database along with the received taste information. It also simultaneously collects regional taste trend information and combines this data. An artificial intelligence model within the server analyzes this information to generate a taste profile of the dish the user desires. This taste profile includes a numerical representation of each taste element and takes into account the overall balance of flavors.

[0443] Next, the server uses the generated flavor profile to consider healthy and appropriate combinations of ingredients and seasonings and creates a recipe. This recipe is adjusted with health in mind, paying particular attention to the balance of salt and fat. It also details the cooking procedure and the timing of adding seasonings.

[0444] The server sends the completed recipe to the device. The device displays the received recipe in an easy-to-read format for the user. The user tries the recipe and inputs the results and feedback into the device. The feedback is returned to the server and used by the artificial intelligence model for further improvement.

[0445] For example, if a user wants to recreate the curry from a cafeteria they frequented during their student days, they input detailed taste information about that curry into a terminal. The server analyzes this information to determine the specific spice blends and ingredient combinations, and generates a recipe that recreates the nostalgic taste in a healthy way. This system allows users to enjoy the taste of the past while experiencing it without sacrificing their health.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] Users input information about the taste of nostalgic dishes through their devices. This information includes specific taste characteristics and memories associated with the dish they want to recreate.

[0449] Step 2:

[0450] The terminal converts the input taste information into an appropriate format and sends it to the server. During the conversion process, it checks for any missing or incorrect information.

[0451] Step 3:

[0452] Based on the taste information it receives, the server accesses the ingredient database to retrieve relevant ingredient and seasoning information. During this process, it also queries for data necessary for recreating the dish, such as seasonality and nutritional value.

[0453] Step 4:

[0454] The server collects information on regional taste trends and gathers data that reflects the regional characteristics of the flavors that users want to recreate.

[0455] Step 5:

[0456] An artificial intelligence model on the server analyzes the collected information and generates a taste profile of the dish the user desires. The profile includes a numerical representation of the balance of each taste element.

[0457] Step 6:

[0458] Based on the generated flavor profile, the server creates a recipe that combines appropriate ingredients and seasonings, taking health into consideration. The recipe includes cooking instructions and points to note.

[0459] Step 7:

[0460] The server sends the completed recipe to the terminal. The terminal displays the received recipe to the user in an easy-to-understand visual format.

[0461] Step 8:

[0462] Users try cooking using the provided recipes via their devices and input their results and feedback. The interface is designed to easily reflect what they felt through their experience.

[0463] Step 9:

[0464] The device sends user feedback back to the server. The server feeds this feedback into an artificial intelligence model, which is then used to improve recipes for future use.

[0465] (Example 1)

[0466] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0467] Recreating the nostalgic flavors of dishes that many people have experienced in the past usually relies on individual taste perception, which is time-consuming and laborious, and often lacks sufficient consideration for health. In particular, there is a need to automatically generate healthy meal plans that effectively utilize memory-based taste information.

[0468] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0469] In this invention, the server includes means for acquiring information on ingredients, seasonings, and regional taste trends; means for combining and analyzing this information to generate a healthy meal plan, and means for improving the intelligent model by utilizing input feedback. This makes it possible to provide healthy meals while recreating past flavors and to optimize recipes to meet the user's taste needs.

[0470] A "user" is the entity that uses the system to recreate nostalgic dishes.

[0471] "Nostalgic cuisine" refers to the taste sensations experienced by users when eating specific dishes in the past.

[0472] "Taste information" refers to information entered by the user, such as the specific taste characteristics of a dish or associated memories.

[0473] A "terminal" is a device used by users to input taste information and receive the results of a dish's reproduction.

[0474] A "server" is a computer system that processes taste information and generates cooking plans using intelligent models.

[0475] "Ingredient information" refers to detailed information about the substances and components needed to recreate a particular dish.

[0476] "Seasoning information" refers to detailed information about the ingredients used to adjust the flavor of a dish.

[0477] "Regional taste trend information" refers to data that shows the general characteristics and trends of taste in a specific region.

[0478] An "intelligent model" is a computational model that analyzes input information and reproduces the taste of the dish the user desires.

[0479] A "meal plan" is a list of specific steps and necessary ingredients for a dish that the user wants to recreate.

[0480] "Feedback" refers to the input of results and impressions from users who have actually tried making the dishes.

[0481] This invention is a system that recreates the taste of a nostalgic dish a user has experienced in the past, and consists of a user, a terminal, and a server. The user can use the terminal to input detailed taste information about the dish they wish to recreate. This input is provided to the terminal as text information, including the characteristics of the dish's taste and associated memories. The terminal is responsible for transmitting the input information to the server.

[0482] The server receives taste information sent by the user and, based on this, collects information on ingredients, seasonings, and regional taste trends from a database system. SQL or similar database query languages ​​are used for this data collection. The collected data is analyzed by a generative AI model on the server to generate a taste profile of the dish the user is requesting. This profile is generated by a neural network using machine learning frameworks such as TensorFlow or PyTorch.

[0483] Based on the generated taste profile, the server creates a healthy and nutritious meal plan. A Python script automatically calculates nutritional balance and generates health-conscious recipes, including adjustments to salt and fat content. The server then sends this recipe to the terminal, which presents it to the user in a user-friendly format.

[0484] Users attempt to cook dishes based on the provided recipes and input the results as feedback into their devices. The devices send this feedback back to the server, which uses the data to further improve the AI ​​model. Through this process, the taste reproduction and health aspects of the provided recipes continue to improve.

[0485] As a concrete example, if a user wants to recreate a curry they frequented during their student days, they input their taste information into the terminal. An example of a prompt message might be, "I want to recreate the fragrant spices and sweetness of the curry I ate at the cafeteria during my student days." Based on this information, the server's AI model generates a recipe with the appropriate spice blend and health considerations. This allows the user to enjoy a nostalgic taste again while also protecting their health.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] Users use a terminal to input taste information about nostalgic dishes. In this process, users provide the dish name, taste characteristics, and specific memories in text format. This input data is stored on the terminal as information necessary for subsequent analysis.

[0489] Step 2:

[0490] The terminal verifies the taste information entered by the user and sends it to the server via a data communication protocol. Secure communication methods such as HTTPS are used for this transmission. At this time, the terminal converts the input data into the correct format before handing it over to the server.

[0491] Step 3:

[0492] The server receives taste information transmitted from the terminal. Based on the received data, it retrieves information on ingredients, seasonings, and regional taste trends from the database. This data collection is performed using SQL queries and stored on the server as datasets for the corresponding ingredients and seasonings.

[0493] Step 4:

[0494] The AI ​​model on the server analyzes the collected ingredient data and the user's taste preferences. This analysis process uses machine learning algorithms to quantify the taste profile desired by the user. Based on this taste profile, the AI ​​model outputs the optimal combination of ingredients and seasonings, which serves as reference data for the next processing step.

[0495] Step 5:

[0496] The server designs a healthy meal plan based on the generated taste profile and AI analysis results. At this stage, nutritional calculations are performed, and the salt and fat content is adjusted to meet health standards. The completed recipe is then generated as output and sent to the terminal.

[0497] Step 6:

[0498] The terminal displays recipe information received from the server in a user-friendly format. This display includes necessary ingredients, cooking instructions, and timing for using seasonings, providing an interface that allows users to cook smoothly.

[0499] Step 7:

[0500] Users attempt to prepare a dish based on a recipe presented through their device. They then input their results and impressions as feedback into the device. This feedback is recorded on the device as text information, including the quality of the dish, how well the taste was reproduced, and any other improvements made.

[0501] Step 8:

[0502] The device sends user feedback to the server. The server uses this feedback information to update the AI ​​model's training data and execute a process to improve the accuracy of subsequent analyses. This continuously improves the overall system performance.

[0503] (Application Example 1)

[0504] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0505] In today's lifestyle, recreating nostalgic dishes from the past is important for providing emotional satisfaction and a healthy eating experience. However, it is difficult for users to provide accurate and easy-to-understand information and, based on that information, obtain appropriate recipes that take health into consideration. In particular, there is a need for a voice-enabled interface, but since its implementation is advanced, a system that solves these challenges is necessary.

[0506] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0507] In this invention, the server includes means for inputting taste information about nostalgic dishes from the user, means for acquiring ingredient information, seasoning information, and regional taste trend information, and means for acquiring the user's taste information using speech recognition technology. This makes it possible for users to easily register taste information about past dishes and be provided with highly personalized and healthy recipes.

[0508] A "user" is an individual who wishes to use this system to recreate nostalgic dishes.

[0509] "Taste information" refers to information based on the characteristics of the flavor of the dish the user wants to recreate, as well as personal anecdotes.

[0510] "Ingredient information" refers to detailed information about the components used as ingredients in a dish.

[0511] "Seasoning information" refers to detailed information about the seasonings used to flavor a dish.

[0512] "Regional taste trend information" refers to information about the general taste preferences and trends in a particular region.

[0513] An "artificial intelligence model" is an algorithm that analyzes user input information and generates healthy recipes.

[0514] "Voice recognition technology" is a technology that converts a user's voice into digital information and makes it usable in a system.

[0515] A "display device" is a device that provides the generated recipe to the user visually.

[0516] "Feedback" refers to the evaluations and opinions that users give regarding the output results of a system.

[0517] A "recipe" is a combination of ingredients and steps necessary to create a specific dish.

[0518] The system implementing this invention consists of a user, a terminal, and a server. The user can use a terminal, such as a household robot, to input taste information about nostalgic dishes through voice recognition. This terminal is equipped with voice recognition software and has technology for converting voice input into text (e.g., Google Speech-to-Text API).

[0519] The server receives taste information sent by the user and retrieves necessary information from a database (e.g., MongoDB) containing information on ingredients, seasonings, and regional taste trends. Furthermore, it uses a generative AI model (e.g., PyTorch) within the server to analyze this information and create a taste profile that the user is seeking. Based on this profile, the server automatically generates health-conscious recipes.

[0520] The generated recipes are presented to the user visually and audibly by the device. The display device shows the recipes in a list format, and an audio output device (e.g., Google Text-to-Speech) provides feedback in an easy-to-understand format.

[0521] For example, if a user voice-inputs, "I want to make the creamy curry I ate in college," the robot can use that information to communicate with a server and generate the appropriate spice combination and cooking procedure. In this case, the prompt would be, "I want to make creamy curry, what spices do I need?"

[0522] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0523] Step 1:

[0524] The user verbally inputs taste information about nostalgic dishes through the voice recognition function of a home robot. This input is converted into text format by the voice recognition software. The input is the user's voice, and the output is text data.

[0525] Step 2:

[0526] The terminal sends transcribed taste information to the server. The input is text data generated by the user, and the output is data sent to the server. This includes prompts such as, "I want to make a creamy curry, what spices do I need?"

[0527] Step 3:

[0528] The server analyzes the received text data and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The input is text data, and the output is a dataset containing various types of information. Here, data extraction is performed using database queries.

[0529] Step 4:

[0530] The server inputs the acquired data into a generating AI model to create a taste profile desired by the user. The input is a dataset, and the output is a quantified taste profile. Here, data processing is performed by a machine learning model.

[0531] Step 5:

[0532] The server considers healthy ingredient and seasoning combinations based on the generated taste profile and creates a recipe. The input is the taste profile, and the output is a detailed recipe. Optimization is performed using a combination algorithm.

[0533] Step 6:

[0534] The generated recipe is sent to the terminal and presented to the user visually and audibly. The input is recipe data, and the output is information display and audio guidance for the user. The terminal provides information to the user using a display device and an audio output device.

[0535] Step 7:

[0536] Users try cooking using the provided recipes and input the results and their impressions into their device. This feedback is sent back to the server and used to improve the model. The input is the user's feedback, and the output is the revised model. The server updates the machine learning model to improve accuracy for the next attempt.

[0537] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0538] The system of the present invention includes a user, a terminal, a server, and an emotion engine. The user inputs taste information about a nostalgic dish through the terminal. The input information includes the specific taste characteristics of the dish to be recreated, related episodes, and emotions. Based on this information, the terminal uses the emotion engine to analyze the user's emotional state.

[0539] The emotion engine recognizes the user's emotions from their facial expressions and tone of voice, and sends this information, along with taste information, to the server. The server uses the received emotion and taste information to retrieve ingredient and seasoning information from the ingredient database. It also collects regional taste trend information to reflect the regional characteristics of the flavor the user wants to recreate.

[0540] An artificial intelligence model on the server comprehensively analyzes all information and generates a taste profile of the dish the user desires. This generated profile, taking into account the user's emotional state, contributes to creating a more personalized recipe. This recipe takes into account healthy ingredient selection and seasoning combinations, resulting in a health-conscious approach.

[0541] The server then sends the generated recipe to the terminal, which displays it to the user in an easy-to-understand visual format. The user tries the recipe and inputs the results and any new emotions into the terminal. The terminal sends the emotion information along with the feedback to the server, which helps to further improve the artificial intelligence model.

[0542] For example, if a user has been away from their hometown for a long time and wants to recreate a special dish they used to eat there, they input information including their feelings and memories associated with that dish. Based on this emotional information, the server generates a recipe that provides a taste that enhances feelings of relaxation and happiness, and provides it to the user. This system allows users to go beyond simply recreating a physical taste and gain a dining experience that is tailored to their personal emotions.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] Users input information about the taste of nostalgic dishes and the emotions associated with those dishes through their device. The device also features emotion recognition capabilities to analyze the user's facial expressions and tone of voice.

[0546] Step 2:

[0547] The device analyzes the user's emotional state, obtained through an emotion engine, along with the user's taste information, and sends this information to the server.

[0548] Step 3:

[0549] The server uses the received taste and emotion information to access the ingredient database and retrieve relevant ingredient and seasoning information. At this stage, it also collects data on regionally specific tastes.

[0550] Step 4:

[0551] An artificial intelligence model on the server integrates and analyzes taste and emotional information. From this information, the model generates a desired taste profile and designs a recipe optimized according to the user's emotions.

[0552] Step 5:

[0553] The server sends the generated recipe to the device. The device displays the recipe in a format that is easy for the user to follow. This display includes adjustment points that take into account the user's emotional state, as well as recommended ingredient variations tailored to specific situations.

[0554] Step 6:

[0555] Users try out recipes provided by their devices and input the results, their impressions of the experience, and any changes in their emotions.

[0556] Step 7:

[0557] The device then sends user feedback and changed emotional information back to the server. This allows the server to update its artificial intelligence model and improve the accuracy of its analysis for the next recipe suggestion.

[0558] (Example 2)

[0559] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0560] In modern society, people often leave their hometowns for various reasons, and it can become difficult to recreate familiar flavors as time passes. As a result, individuals have fewer opportunities to experience the emotional satisfaction derived from flavors they once enjoyed, leading to a decline in their emotional well-being. This invention aims to enable users to rediscover emotional satisfaction through nostalgic food experiences by analyzing their individual emotions and taste preferences in detail and providing individually optimized recipes.

[0561] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0562] In this invention, the server includes means for inputting emotional and taste information related to food from the user, means for recognizing the emotional state by analyzing facial expressions and voice, and means for acquiring taste tendencies of ingredients, seasonings, and region. This makes it possible to generate an optimal taste profile that is tailored to the user's emotional state, thereby providing a nostalgic dining experience and improving the individual's emotional satisfaction.

[0563] A "user" refers to an individual who uses the system to provide information about nostalgic dishes and receives personalized recipes.

[0564] "Emotions" refers to information that indicates a user's mental state or mood, analyzed from their facial expressions, tone of voice, and other factors.

[0565] "Taste information" refers to information that includes the specific flavor characteristics of the dish you want to recreate, as well as personal anecdotes related to that dish.

[0566] "Analyzing facial expressions and voice" refers to the process of using technical means to analyze the user's facial expressions and voice tone to recognize their emotional state.

[0567] "Ingredients" refers to the individual substances or materials that make up a cooking recipe.

[0568] "Seasonings" refer to substances used to improve or alter the taste of food ingredients.

[0569] "Regional taste trends" refer to information that indicates the taste preferences and cultural characteristics generally accepted in a particular geographical area.

[0570] An "artificial intelligence model" refers to an algorithm or program that analyzes user input and automatically generates optimized recipes.

[0571] "Feedback" refers to the reactions and opinions based on the user's experience after trying out a provided recipe.

[0572] The system of the present invention includes a user, a terminal, a server, and an emotion engine as its main components. The user uses the terminal to input taste information and associated emotions about the dish they wish to recreate. The information input by the user includes the name of the dish, its flavor characteristics, a memorable anecdote, and the emotions associated with that dish. An example of a prompt statement is, "The sweet and spicy curry my mother made, its aroma fills me with happiness."

[0573] The terminal analyzes the user's emotional state based on the input information using its built-in emotion engine. The hardware used here includes a camera and microphone, while the software is a program equipped with facial recognition and voice analysis technologies. The emotional data and taste information obtained from this analysis are transmitted to a server using wireless or wired communication.

[0574] The server searches for relevant information from its database of ingredients and seasonings based on the received emotional and gustatory data, and also references regional taste trend information. This allows it to reflect regional flavor characteristics in the dish the user desires. The server incorporates a generative AI model that enables complex data analysis, thereby generating an optimized taste profile. In this process, the AI ​​model takes the user's emotions into consideration and generates the optimal recipe to enhance feelings of relaxation and happiness.

[0575] Ultimately, the server sends the generated recipe to the device, which displays it to the user in a visually easy-to-understand format. The user tries the recipe and provides feedback to the server's AI model by inputting the results and any newly arising emotions back into the device. This feedback is analyzed by the server and used to further improve the model.

[0576] This allows users to not only recreate flavors but also gain a rich dining experience that resonates with their own emotions.

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] The user uses a terminal to input taste information of the dish they want to recreate, along with related personal feelings, in the form of prompt sentences. The information input includes the name of the dish, its flavor characteristics, related anecdotes, and emotional state. As a concrete example, the user inputs the prompt sentence, "The sweet and spicy curry my mother made for me; its aroma envelops me in happiness."

[0580] Step 2:

[0581] The device receives the input prompt and uses an emotion engine to analyze the user's emotional state. The input here consists of the user's facial expressions and tone of voice, which the emotion engine processes and converts into digital information. Through data analysis, the device generates emotional data and sends it to the server along with taste information. In this process, the camera and microphone play a specific role.

[0582] Step 3:

[0583] The server uses taste and emotional data received from the terminal as input to search for relevant information in its database of ingredients and seasonings. It also refers to a regional taste preference database to reflect regional flavor characteristics. Based on these inputs, the server performs data processing called database matching and outputs candidate ingredient and seasoning information.

[0584] Step 4:

[0585] A generative AI model on the server analyzes the collected information to generate a taste profile. At this stage, the input consists of information on ingredients, seasonings, and regional taste tendencies obtained in previous steps. Based on the data analysis, the generative AI model outputs an optimized recipe. The specific operation is the execution of this analysis process.

[0586] Step 5:

[0587] The server sends the generated recipe to the terminal. The output information is a visualized recipe for the user. The terminal receives this recipe and displays it in a way that is easy for the user to understand visually. Specifically, the display handles the visual presentation.

[0588] Step 6:

[0589] The user tries out a recipe on their device and actually cooks the dish. After trying the dish, the user re-enters the results and any new emotions that arise towards the dish into the device. This input is feedback and the associated emotional state.

[0590] Step 7:

[0591] The device sends new emotional information back to the server based on user feedback. The server analyzes this feedback as input and processes the data to improve the AI ​​model. Specifically, this involves feedback analysis and AI model adjustment.

[0592] (Application Example 2)

[0593] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0594] In modern society, where many people lead busy lives, recreating nostalgic dishes is often cumbersome and difficult. Furthermore, while taste is important, emotions are also crucial, and there is a lack of services that cater to these needs. Additionally, there is a need for convenient ways to enjoy individually optimized, health-conscious meals.

[0595] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0596] In this invention, the server includes means for inputting taste and emotional information related to nostalgic dishes from the user, means for using an emotion engine to analyze the emotional state, and means for acquiring ingredient information, seasoning information, and regional taste trend information. This makes it possible to generate healthy and optimized recipes that take into account the user's emotional state and to order delivery of those dishes.

[0597] "Taste information related to nostalgic dishes" refers to data that indicates characteristics related to the taste of a specific past dish that the user wishes to recreate.

[0598] "Emotional information" refers to data that reflects the user's emotional state, such as their facial expressions and voice.

[0599] An "emotion engine" refers to a function or program used to analyze a user's emotional state.

[0600] "Ingredient information" refers to data about ingredients that may be used in a particular dish.

[0601] "Seasoning information" refers to data about the ingredients used to flavor a dish.

[0602] "Regional taste trend information" refers to data that shows the general taste preferences in a particular region or culture.

[0603] An "artificial intelligence model" refers to an algorithm or program used to analyze information obtained from users and generate optimal results.

[0604] "Visual presentation" refers to representing the generated recipe visually in a way that is easy for the user to understand.

[0605] "Delivery order" refers to the process of delivering food requested by a user to a specified location.

[0606] "Feedback" refers to the act of users providing opinions and comments on generated recipes and dishes.

[0607] An "emotional profile" is data that quantifies or identifies a specific user's emotional state, generated based on the user's emotional information.

[0608] The system for implementing this invention primarily utilizes a user terminal, a server including an emotion engine, and a food ingredient database. Users input information about nostalgic dishes via their smartphones or computers. This input includes specific taste characteristics of the dish they wish to recreate and anecdotes related to their emotions. This ensures that the system accurately reflects the user's individual emotional state.

[0609] The device transmits this information to the emotion engine, which analyzes the user's emotional state. The emotion engine recognizes the user's facial expressions and tone of voice, and collects emotional information based on this. Subsequently, the server comprehensively analyzes the received taste and emotional information.

[0610] An artificial intelligence model (e.g., TensorFlow or PyTorch) on the server generates the optimal recipe based on this information. This process involves building a healthy and personalized taste profile and retrieving appropriate ingredient and seasoning information from a database. Furthermore, by considering regional taste trends, it becomes possible to create recipes that meet user preferences.

[0611] The generated recipe is presented to the user visually through their device. The user can review the recipe and order the food via delivery. At this time, they can also input more detailed feedback and new emotional states, and this data is sent to the server and used to improve the AI ​​model.

[0612] For example, if a user wants to recreate a dish that evokes memories shared with someone they were close to in the past, they would comprehensively input their feelings about that dish. Based on that emotional state, the system would make suggestions that would bring about feelings of happiness or comfort. An example of a prompt might be, "I want to recreate the taste of my grandmother's nikujaga (meat and potato stew) that makes me feel happy. Please give me a recipe that emphasizes specific aromas and flavors that will bring back happy memories from the past."

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The user uses a device to input taste and emotional information about nostalgic dishes. The user describes the taste characteristics and emotions associated with a specific dish, and the device collects this data. The input data includes the dish name, taste details, and emotional anecdotes. This data forms the basis for the next processing step.

[0616] Step 2:

[0617] The device sends user-input data to the emotion engine, which then analyzes the user's emotional state. The emotion engine utilizes facial recognition and voice analysis technologies to analyze the user's facial expressions and voice tone. Here, the input is the user's emotional characteristics, and the output is a profile that quantifies the user's emotional state.

[0618] Step 3:

[0619] The server receives emotional profiles and taste information, and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The server uses this data to collect basic recipe information. The output aggregates all the information necessary for recipe generation.

[0620] Step 4:

[0621] Based on the collected information, the server utilizes a generative AI model to construct a recipe profile optimized for the user's requests. Here, the input is the user's taste information and emotional profile, and the output is a healthy and personalized recipe suggestion. The model performs multidimensional analysis to reproduce appropriate flavors based on the user's nasalgia.

[0622] Step 5:

[0623] The server sends the generated recipe to the terminal, which then visually presents it to the user. The user can review the displayed recipe and make adjustments or place a delivery order as needed. The output is recipe information that the user can visually understand.

[0624] Step 6:

[0625] After trying a recipe, users enter and submit feedback on their device. This feedback includes the quality of the dish and their overall satisfaction. Based on this feedback, the system collects data to make further improvements.

[0626] Step 7:

[0627] The server uses user feedback and newly entered sentiment information to update and improve the generating AI model. This lays the foundation for providing a more refined service for future recipe generation. The model learns autonomously and improves its accuracy over time.

[0628] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0629] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0630] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0631] [Fourth Embodiment]

[0632] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0633] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0634] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0635] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0636] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0637] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0638] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0639] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0640] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0641] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0642] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0643] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0644] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0645] The system of this invention consists of a user, a terminal, and a server. The user inputs taste information about a nostalgic dish through the terminal. This information includes the taste characteristics of the dish to be recreated and related anecdotes. The terminal confirms this information and transmits it to the server.

[0646] The server retrieves ingredient and seasoning information from an ingredient database along with the received taste information. It also simultaneously collects regional taste trend information and combines this data. An artificial intelligence model within the server analyzes this information to generate a taste profile of the dish the user desires. This taste profile includes a numerical representation of each taste element and takes into account the overall balance of flavors.

[0647] Next, the server uses the generated flavor profile to consider healthy and appropriate combinations of ingredients and seasonings and creates a recipe. This recipe is adjusted with health in mind, paying particular attention to the balance of salt and fat. It also details the cooking procedure and the timing of adding seasonings.

[0648] The server sends the completed recipe to the device. The device displays the received recipe in an easy-to-read format for the user. The user tries the recipe and inputs the results and feedback into the device. The feedback is returned to the server and used by the artificial intelligence model for further improvement.

[0649] For example, if a user wants to recreate the curry from a cafeteria they frequented during their student days, they input detailed taste information about that curry into a terminal. The server analyzes this information to determine the specific spice blends and ingredient combinations, and generates a recipe that recreates the nostalgic taste in a healthy way. This system allows users to enjoy the taste of the past while experiencing it without sacrificing their health.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] Users input information about the taste of nostalgic dishes through their devices. This information includes specific taste characteristics and memories associated with the dish they want to recreate.

[0653] Step 2:

[0654] The terminal converts the input taste information into an appropriate format and sends it to the server. During the conversion process, it checks for any missing or incorrect information.

[0655] Step 3:

[0656] Based on the taste information it receives, the server accesses the ingredient database to retrieve relevant ingredient and seasoning information. During this process, it also queries for data necessary for recreating the dish, such as seasonality and nutritional value.

[0657] Step 4:

[0658] The server collects information on regional taste trends and gathers data that reflects the regional characteristics of the flavors that users want to recreate.

[0659] Step 5:

[0660] An artificial intelligence model on the server analyzes the collected information and generates a taste profile of the dish the user desires. The profile includes a numerical representation of the balance of each taste element.

[0661] Step 6:

[0662] Based on the generated flavor profile, the server creates a recipe that combines appropriate ingredients and seasonings, taking health into consideration. The recipe includes cooking instructions and points to note.

[0663] Step 7:

[0664] The server sends the completed recipe to the terminal. The terminal displays the received recipe to the user in an easy-to-understand visual format.

[0665] Step 8:

[0666] Users try cooking using the provided recipes via their devices and input their results and feedback. The interface is designed to easily reflect what they felt through their experience.

[0667] Step 9:

[0668] The device sends user feedback back to the server. The server feeds this feedback into an artificial intelligence model, which is then used to improve recipes for future use.

[0669] (Example 1)

[0670] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] Recreating the nostalgic flavors of dishes that many people have experienced in the past usually relies on individual taste perception, which is time-consuming and laborious, and often lacks sufficient consideration for health. In particular, there is a need to automatically generate healthy meal plans that effectively utilize memory-based taste information.

[0672] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0673] In this invention, the server includes means for acquiring information on ingredients, seasonings, and regional taste trends; means for combining and analyzing this information to generate a healthy meal plan, and means for improving the intelligent model by utilizing input feedback. This makes it possible to provide healthy meals while recreating past flavors and to optimize recipes to meet the user's taste needs.

[0674] A "user" is the entity that uses the system to recreate nostalgic dishes.

[0675] "Nostalgic cuisine" refers to the taste sensations experienced by users when eating specific dishes in the past.

[0676] "Taste information" refers to information entered by the user, such as the specific taste characteristics of a dish or associated memories.

[0677] A "terminal" is a device used by users to input taste information and receive the results of a dish's reproduction.

[0678] A "server" is a computer system that processes taste information and generates cooking plans using intelligent models.

[0679] "Ingredient information" refers to detailed information about the substances and components needed to recreate a particular dish.

[0680] "Seasoning information" refers to detailed information about the ingredients used to adjust the flavor of a dish.

[0681] "Regional taste trend information" refers to data that shows the general characteristics and trends of taste in a specific region.

[0682] An "intelligent model" is a computational model that analyzes input information and reproduces the taste of the dish the user desires.

[0683] A "meal plan" is a list of specific steps and necessary ingredients for a dish that the user wants to recreate.

[0684] "Feedback" refers to the input of results and impressions from users who have actually tried making the dishes.

[0685] This invention is a system that recreates the taste of a nostalgic dish a user has experienced in the past, and consists of a user, a terminal, and a server. The user can use the terminal to input detailed taste information about the dish they wish to recreate. This input is provided to the terminal as text information, including the characteristics of the dish's taste and associated memories. The terminal is responsible for transmitting the input information to the server.

[0686] The server receives taste information sent by the user and, based on this, collects information on ingredients, seasonings, and regional taste trends from a database system. SQL or similar database query languages ​​are used for this data collection. The collected data is analyzed by a generative AI model on the server to generate a taste profile of the dish the user is requesting. This profile is generated by a neural network using machine learning frameworks such as TensorFlow or PyTorch.

[0687] Based on the generated taste profile, the server creates a healthy and nutritious meal plan. A Python script automatically calculates nutritional balance and generates health-conscious recipes, including adjustments to salt and fat content. The server then sends this recipe to the terminal, which presents it to the user in a user-friendly format.

[0688] Users attempt to cook dishes based on the provided recipes and input the results as feedback into their devices. The devices send this feedback back to the server, which uses the data to further improve the AI ​​model. Through this process, the taste reproduction and health aspects of the provided recipes continue to improve.

[0689] As a concrete example, if a user wants to recreate a curry they frequented during their student days, they input their taste information into the terminal. An example of a prompt message might be, "I want to recreate the fragrant spices and sweetness of the curry I ate at the cafeteria during my student days." Based on this information, the server's AI model generates a recipe with the appropriate spice blend and health considerations. This allows the user to enjoy a nostalgic taste again while also protecting their health.

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1:

[0692] Users use a terminal to input taste information about nostalgic dishes. In this process, users provide the dish name, taste characteristics, and specific memories in text format. This input data is stored on the terminal as information necessary for subsequent analysis.

[0693] Step 2:

[0694] The terminal verifies the taste information entered by the user and sends it to the server via a data communication protocol. Secure communication methods such as HTTPS are used for this transmission. At this time, the terminal converts the input data into the correct format before handing it over to the server.

[0695] Step 3:

[0696] The server receives taste information transmitted from the terminal. Based on the received data, it retrieves information on ingredients, seasonings, and regional taste trends from the database. This data collection is performed using SQL queries and stored on the server as datasets for the corresponding ingredients and seasonings.

[0697] Step 4:

[0698] The AI ​​model on the server analyzes the collected ingredient data and the user's taste preferences. This analysis process uses machine learning algorithms to quantify the taste profile desired by the user. Based on this taste profile, the AI ​​model outputs the optimal combination of ingredients and seasonings, which serves as reference data for the next processing step.

[0699] Step 5:

[0700] The server designs a healthy meal plan based on the generated taste profile and AI analysis results. At this stage, nutritional calculations are performed, and the salt and fat content is adjusted to meet health standards. The completed recipe is then generated as output and sent to the terminal.

[0701] Step 6:

[0702] The terminal displays recipe information received from the server in a user-friendly format. This display includes necessary ingredients, cooking instructions, and timing for using seasonings, providing an interface that allows users to cook smoothly.

[0703] Step 7:

[0704] Users attempt to prepare a dish based on a recipe presented through their device. They then input their results and impressions as feedback into the device. This feedback is recorded on the device as text information, including the quality of the dish, how well the taste was reproduced, and any other improvements made.

[0705] Step 8:

[0706] The device sends user feedback to the server. The server uses this feedback information to update the AI ​​model's training data and execute a process to improve the accuracy of subsequent analyses. This continuously improves the overall system performance.

[0707] (Application Example 1)

[0708] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] In today's lifestyle, recreating nostalgic dishes from the past is important for providing emotional satisfaction and a healthy eating experience. However, it is difficult for users to provide accurate and easy-to-understand information and, based on that information, obtain appropriate recipes that take health into consideration. In particular, there is a need for a voice-enabled interface, but since its implementation is advanced, a system that solves these challenges is necessary.

[0710] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0711] In this invention, the server includes means for inputting taste information about nostalgic dishes from the user, means for acquiring ingredient information, seasoning information, and regional taste trend information, and means for acquiring the user's taste information using speech recognition technology. This makes it possible for users to easily register taste information about past dishes and be provided with highly personalized and healthy recipes.

[0712] A "user" is an individual who wishes to use this system to recreate nostalgic dishes.

[0713] "Taste information" refers to information based on the characteristics of the flavor of the dish the user wants to recreate, as well as personal anecdotes.

[0714] "Ingredient information" refers to detailed information about the components used as ingredients in a dish.

[0715] "Seasoning information" refers to detailed information about the seasonings used to flavor a dish.

[0716] "Regional taste trend information" refers to information about the general taste preferences and trends in a particular region.

[0717] An "artificial intelligence model" is an algorithm that analyzes user input information and generates healthy recipes.

[0718] "Voice recognition technology" is a technology that converts a user's voice into digital information and makes it usable in a system.

[0719] A "display device" is a device that provides the generated recipe to the user visually.

[0720] "Feedback" refers to the evaluations and opinions that users give regarding the output results of a system.

[0721] A "recipe" is a combination of ingredients and steps necessary to create a specific dish.

[0722] The system implementing this invention consists of a user, a terminal, and a server. The user can use a terminal, such as a household robot, to input taste information about nostalgic dishes through voice recognition. This terminal is equipped with voice recognition software and has technology for converting voice input into text (e.g., Google Speech-to-Text API).

[0723] The server receives taste information sent by the user and retrieves necessary information from a database (e.g., MongoDB) containing information on ingredients, seasonings, and regional taste trends. Furthermore, it uses a generative AI model (e.g., PyTorch) within the server to analyze this information and create a taste profile that the user is seeking. Based on this profile, the server automatically generates health-conscious recipes.

[0724] The generated recipes are presented to the user visually and audibly by the device. The display device shows the recipes in a list format, and an audio output device (e.g., Google Text-to-Speech) provides feedback in an easy-to-understand format.

[0725] For example, if a user voice-inputs, "I want to make the creamy curry I ate in college," the robot can use that information to communicate with a server and generate the appropriate spice combination and cooking procedure. In this case, the prompt would be, "I want to make creamy curry, what spices do I need?"

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The user verbally inputs taste information about nostalgic dishes through the voice recognition function of a home robot. This input is converted into text format by the voice recognition software. The input is the user's voice, and the output is text data.

[0729] Step 2:

[0730] The terminal sends transcribed taste information to the server. The input is text data generated by the user, and the output is data sent to the server. This includes prompts such as, "I want to make a creamy curry, what spices do I need?"

[0731] Step 3:

[0732] The server analyzes the received text data and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The input is text data, and the output is a dataset containing various types of information. Here, data extraction is performed using database queries.

[0733] Step 4:

[0734] The server inputs the acquired data into a generating AI model to create a taste profile desired by the user. The input is a dataset, and the output is a quantified taste profile. Here, data processing is performed by a machine learning model.

[0735] Step 5:

[0736] The server considers healthy ingredient and seasoning combinations based on the generated taste profile and creates a recipe. The input is the taste profile, and the output is a detailed recipe. Optimization is performed using a combination algorithm.

[0737] Step 6:

[0738] The generated recipe is sent to the terminal and presented to the user visually and audibly. The input is recipe data, and the output is information display and audio guidance for the user. The terminal provides information to the user using a display device and an audio output device.

[0739] Step 7:

[0740] Users try cooking using the provided recipes and input the results and their impressions into their device. This feedback is sent back to the server and used to improve the model. The input is the user's feedback, and the output is the revised model. The server updates the machine learning model to improve accuracy for the next attempt.

[0741] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0742] The system of the present invention includes a user, a terminal, a server, and an emotion engine. The user inputs taste information about a nostalgic dish through the terminal. The input information includes the specific taste characteristics of the dish to be recreated, related episodes, and emotions. Based on this information, the terminal uses the emotion engine to analyze the user's emotional state.

[0743] The emotion engine recognizes the user's emotions from their facial expressions and tone of voice, and sends this information, along with taste information, to the server. The server uses the received emotion and taste information to retrieve ingredient and seasoning information from the ingredient database. It also collects regional taste trend information to reflect the regional characteristics of the flavor the user wants to recreate.

[0744] An artificial intelligence model on the server comprehensively analyzes all information and generates a taste profile of the dish the user desires. This generated profile, taking into account the user's emotional state, contributes to creating a more personalized recipe. This recipe takes into account healthy ingredient selection and seasoning combinations, resulting in a health-conscious approach.

[0745] The server then sends the generated recipe to the terminal, which displays it to the user in an easy-to-understand visual format. The user tries the recipe and inputs the results and any new emotions into the terminal. The terminal sends the emotion information along with the feedback to the server, which helps to further improve the artificial intelligence model.

[0746] For example, if a user has been away from their hometown for a long time and wants to recreate a special dish they used to eat there, they input information including their feelings and memories associated with that dish. Based on this emotional information, the server generates a recipe that provides a taste that enhances feelings of relaxation and happiness, and provides it to the user. This system allows users to go beyond simply recreating a physical taste and gain a dining experience that is tailored to their personal emotions.

[0747] The following describes the processing flow.

[0748] Step 1:

[0749] Users input information about the taste of nostalgic dishes and the emotions associated with those dishes through their device. The device also features emotion recognition capabilities to analyze the user's facial expressions and tone of voice.

[0750] Step 2:

[0751] The device analyzes the user's emotional state, obtained through an emotion engine, along with the user's taste information, and sends this information to the server.

[0752] Step 3:

[0753] The server uses the received taste and emotion information to access the ingredient database and retrieve relevant ingredient and seasoning information. At this stage, it also collects data on regionally specific tastes.

[0754] Step 4:

[0755] An artificial intelligence model on the server integrates and analyzes taste and emotional information. From this information, the model generates a desired taste profile and designs a recipe optimized according to the user's emotions.

[0756] Step 5:

[0757] The server sends the generated recipe to the device. The device displays the recipe in a format that is easy for the user to follow. This display includes adjustment points that take into account the user's emotional state, as well as recommended ingredient variations tailored to specific situations.

[0758] Step 6:

[0759] Users try out recipes provided by their devices and input the results, their impressions of the experience, and any changes in their emotions.

[0760] Step 7:

[0761] The device then sends user feedback and changed emotional information back to the server. This allows the server to update its artificial intelligence model and improve the accuracy of its analysis for the next recipe suggestion.

[0762] (Example 2)

[0763] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0764] In modern society, people often leave their hometowns for various reasons, and it can become difficult to recreate familiar flavors as time passes. As a result, individuals have fewer opportunities to experience the emotional satisfaction derived from flavors they once enjoyed, leading to a decline in their emotional well-being. This invention aims to enable users to rediscover emotional satisfaction through nostalgic food experiences by analyzing their individual emotions and taste preferences in detail and providing individually optimized recipes.

[0765] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0766] In this invention, the server includes means for inputting emotional and taste information related to food from the user, means for recognizing the emotional state by analyzing facial expressions and voice, and means for acquiring taste tendencies of ingredients, seasonings, and region. This makes it possible to generate an optimal taste profile that is tailored to the user's emotional state, thereby providing a nostalgic dining experience and improving the individual's emotional satisfaction.

[0767] A "user" refers to an individual who uses the system to provide information about nostalgic dishes and receives personalized recipes.

[0768] "Emotions" refers to information that indicates a user's mental state or mood, analyzed from their facial expressions, tone of voice, and other factors.

[0769] "Taste information" refers to information that includes the specific flavor characteristics of the dish you want to recreate, as well as personal anecdotes related to that dish.

[0770] "Analyzing facial expressions and voice" refers to the process of using technical means to analyze the user's facial expressions and voice tone to recognize their emotional state.

[0771] "Ingredients" refers to the individual substances or materials that make up a cooking recipe.

[0772] "Seasonings" refer to substances used to improve or alter the taste of food ingredients.

[0773] "Regional taste trends" refer to information that indicates the taste preferences and cultural characteristics generally accepted in a particular geographical area.

[0774] An "artificial intelligence model" refers to an algorithm or program that analyzes user input and automatically generates optimized recipes.

[0775] "Feedback" refers to the reactions and opinions based on the user's experience after trying out a provided recipe.

[0776] The system of the present invention includes a user, a terminal, a server, and an emotion engine as its main components. The user uses the terminal to input taste information and associated emotions about the dish they wish to recreate. The information input by the user includes the name of the dish, its flavor characteristics, a memorable anecdote, and the emotions associated with that dish. An example of a prompt statement is, "The sweet and spicy curry my mother made, its aroma fills me with happiness."

[0777] The terminal analyzes the user's emotional state based on the input information using its built-in emotion engine. The hardware used here includes a camera and microphone, while the software is a program equipped with facial recognition and voice analysis technologies. The emotional data and taste information obtained from this analysis are transmitted to a server using wireless or wired communication.

[0778] The server searches for relevant information from its database of ingredients and seasonings based on the received emotional and gustatory data, and also references regional taste trend information. This allows it to reflect regional flavor characteristics in the dish the user desires. The server incorporates a generative AI model that enables complex data analysis, thereby generating an optimized taste profile. In this process, the AI ​​model takes the user's emotions into consideration and generates the optimal recipe to enhance feelings of relaxation and happiness.

[0779] Ultimately, the server sends the generated recipe to the device, which displays it to the user in a visually easy-to-understand format. The user tries the recipe and provides feedback to the server's AI model by inputting the results and any newly arising emotions back into the device. This feedback is analyzed by the server and used to further improve the model.

[0780] This allows users to not only recreate flavors but also gain a rich dining experience that resonates with their own emotions.

[0781] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0782] Step 1:

[0783] The user uses a terminal to input taste information of the dish they want to recreate, along with related personal feelings, in the form of prompt sentences. The information input includes the name of the dish, its flavor characteristics, related anecdotes, and emotional state. As a concrete example, the user inputs the prompt sentence, "The sweet and spicy curry my mother made for me; its aroma envelops me in happiness."

[0784] Step 2:

[0785] The device receives the input prompt and uses an emotion engine to analyze the user's emotional state. The input here consists of the user's facial expressions and tone of voice, which the emotion engine processes and converts into digital information. Through data analysis, the device generates emotional data and sends it to the server along with taste information. In this process, the camera and microphone play a specific role.

[0786] Step 3:

[0787] The server uses taste and emotional data received from the terminal as input to search for relevant information in its database of ingredients and seasonings. It also refers to a regional taste preference database to reflect regional flavor characteristics. Based on these inputs, the server performs data processing called database matching and outputs candidate ingredient and seasoning information.

[0788] Step 4:

[0789] A generative AI model on the server analyzes the collected information to generate a taste profile. At this stage, the input consists of information on ingredients, seasonings, and regional taste tendencies obtained in previous steps. Based on the data analysis, the generative AI model outputs an optimized recipe. The specific operation is the execution of this analysis process.

[0790] Step 5:

[0791] The server sends the generated recipe to the terminal. The output information is a visualized recipe for the user. The terminal receives this recipe and displays it in a way that is easy for the user to understand visually. Specifically, the display handles the visual presentation.

[0792] Step 6:

[0793] The user tries out a recipe on their device and actually cooks the dish. After trying the dish, the user re-enters the results and any new emotions that arise towards the dish into the device. This input is feedback and the associated emotional state.

[0794] Step 7:

[0795] The device sends new emotional information back to the server based on user feedback. The server analyzes this feedback as input and processes the data to improve the AI ​​model. Specifically, this involves feedback analysis and AI model adjustment.

[0796] (Application Example 2)

[0797] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0798] In modern society, where many people lead busy lives, recreating nostalgic dishes is often cumbersome and difficult. Furthermore, while taste is important, emotions are also crucial, and there is a lack of services that cater to these needs. Additionally, there is a need for convenient ways to enjoy individually optimized, health-conscious meals.

[0799] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0800] In this invention, the server includes means for inputting taste and emotional information related to nostalgic dishes from the user, means for using an emotion engine to analyze the emotional state, and means for acquiring ingredient information, seasoning information, and regional taste trend information. This makes it possible to generate healthy and optimized recipes that take into account the user's emotional state and to order delivery of those dishes.

[0801] "Taste information related to nostalgic dishes" refers to data that indicates characteristics related to the taste of a specific past dish that the user wishes to recreate.

[0802] "Emotional information" refers to data that reflects the user's emotional state, such as their facial expressions and voice.

[0803] An "emotion engine" refers to a function or program used to analyze a user's emotional state.

[0804] "Ingredient information" refers to data about ingredients that may be used in a particular dish.

[0805] "Seasoning information" refers to data about the ingredients used to flavor a dish.

[0806] "Regional taste trend information" refers to data that shows the general taste preferences in a particular region or culture.

[0807] An "artificial intelligence model" refers to an algorithm or program used to analyze information obtained from users and generate optimal results.

[0808] "Visual presentation" refers to representing the generated recipe visually in a way that is easy for the user to understand.

[0809] "Delivery order" refers to the process of delivering food requested by a user to a specified location.

[0810] "Feedback" refers to the act of users providing opinions and comments on generated recipes and dishes.

[0811] An "emotional profile" is data that quantifies or identifies a specific user's emotional state, generated based on the user's emotional information.

[0812] The system for implementing this invention primarily utilizes a user terminal, a server including an emotion engine, and a food ingredient database. Users input information about nostalgic dishes via their smartphones or computers. This input includes specific taste characteristics of the dish they wish to recreate and anecdotes related to their emotions. This ensures that the system accurately reflects the user's individual emotional state.

[0813] The device transmits this information to the emotion engine, which analyzes the user's emotional state. The emotion engine recognizes the user's facial expressions and tone of voice, and collects emotional information based on this. Subsequently, the server comprehensively analyzes the received taste and emotional information.

[0814] An artificial intelligence model (e.g., TensorFlow or PyTorch) on the server generates the optimal recipe based on this information. This process involves building a healthy and personalized taste profile and retrieving appropriate ingredient and seasoning information from a database. Furthermore, by considering regional taste trends, it becomes possible to create recipes that meet user preferences.

[0815] The generated recipe is presented to the user visually through their device. The user can review the recipe and order the food via delivery. At this time, they can also input more detailed feedback and new emotional states, and this data is sent to the server and used to improve the AI ​​model.

[0816] For example, if a user wants to recreate a dish that evokes memories shared with someone they were close to in the past, they would comprehensively input their feelings about that dish. Based on that emotional state, the system would make suggestions that would bring about feelings of happiness or comfort. An example of a prompt might be, "I want to recreate the taste of my grandmother's nikujaga (meat and potato stew) that makes me feel happy. Please give me a recipe that emphasizes specific aromas and flavors that will bring back happy memories from the past."

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The user uses a device to input taste and emotional information about nostalgic dishes. The user describes the taste characteristics and emotions associated with a specific dish, and the device collects this data. The input data includes the dish name, taste details, and emotional anecdotes. This data forms the basis for the next processing step.

[0820] Step 2:

[0821] The device sends user-input data to the emotion engine, which then analyzes the user's emotional state. The emotion engine utilizes facial recognition and voice analysis technologies to analyze the user's facial expressions and voice tone. Here, the input is the user's emotional characteristics, and the output is a profile that quantifies the user's emotional state.

[0822] Step 3:

[0823] The server receives emotional profiles and taste information, and retrieves relevant ingredient information, seasoning information, and regional taste trend information from the database. The server uses this data to collect basic recipe information. The output aggregates all the information necessary for recipe generation.

[0824] Step 4:

[0825] Based on the collected information, the server utilizes a generative AI model to construct a recipe profile optimized for the user's requests. Here, the input is the user's taste information and emotional profile, and the output is a healthy and personalized recipe suggestion. The model performs multidimensional analysis to reproduce appropriate flavors based on the user's nasalgia.

[0826] Step 5:

[0827] The server sends the generated recipe to the terminal, which then visually presents it to the user. The user can review the displayed recipe and make adjustments or place a delivery order as needed. The output is recipe information that the user can visually understand.

[0828] Step 6:

[0829] After trying a recipe, users enter and submit feedback on their device. This feedback includes the quality of the dish and their overall satisfaction. Based on this feedback, the system collects data to make further improvements.

[0830] Step 7:

[0831] The server uses user feedback and newly entered sentiment information to update and improve the generating AI model. This lays the foundation for providing a more refined service for future recipe generation. The model learns autonomously and improves its accuracy over time.

[0832] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0833] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0834] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0835] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0836] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0837] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0838] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0839] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0840] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0841] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0842] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0843] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0844] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0845] 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.

[0846] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0847] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0848] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0849] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0850] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0851] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0852] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0853] The following is further disclosed regarding the embodiments described above.

[0854] (Claim 1)

[0855] A means for users to input taste information about nostalgic dishes,

[0856] A means of obtaining information on ingredients, seasonings, and regional taste trends,

[0857] This includes means such as an artificial intelligence model for analyzing this information and generating healthy recipes,

[0858] A means of presenting the generated recipe to the user,

[0859] A means of receiving user feedback and improving the model,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein an artificial intelligence model quantifies a taste profile using taste information entered by a user.

[0863] (Claim 3)

[0864] The system according to claim 1, wherein the generated recipe takes into account the nutritional value of the ingredients and adjusts the amount of salt in particular.

[0865] "Example 1"

[0866] (Claim 1)

[0867] A means for users to input taste information about nostalgic dishes,

[0868] A means of checking the information on a terminal and sending it to a server,

[0869] A means of obtaining information on ingredients, seasonings, and regional taste trends,

[0870] This includes a means that combines and analyzes this information to generate a healthy meal plan, and an intelligent model for doing so.

[0871] A means of providing users with cooking plans generated by an intelligent model,

[0872] A means for users to input results and feedback,

[0873] A means of improving intelligent models by utilizing input feedback,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein an intelligent model uses taste information provided by the user to represent a taste profile as numerical information.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the generated meal plan takes nutritional value into consideration and adjusts the balance of salt and fat in particular.

[0879] "Application Example 1"

[0880] (Claim 1)

[0881] A means for users to input taste information about nostalgic dishes,

[0882] A means of obtaining information on ingredients, seasonings, and regional taste trends,

[0883] This includes means such as an artificial intelligence model for analyzing this information and generating healthy recipes,

[0884] A means of presenting the generated recipe to the user,

[0885] A means of receiving user feedback and improving the model,

[0886] A means of obtaining a user's taste information using speech recognition technology,

[0887] A means of visually providing a recipe through a display device based on the analysis results,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, wherein an artificial intelligence model quantifies a taste profile using taste information entered by a user.

[0891] (Claim 3)

[0892] The system according to claim 1, wherein the generated recipe takes into account the nutritional value of the ingredients and adjusts the amount of salt in particular.

[0893] "Example 2 of combining an emotion engine"

[0894] (Claim 1)

[0895] A means for users to input emotional and taste information about food,

[0896] A means of recognizing emotional states by analyzing facial expressions and voice,

[0897] Means for obtaining information on ingredients, seasonings, and regional taste trends,

[0898] A means including an artificial intelligence model that analyzes acquired information and generates a taste profile,

[0899] A means of visualizing and presenting the generated recipe to the user,

[0900] A means to receive feedback based on recipe trials and improve the artificial intelligence model,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, which integrates and analyzes user emotional information and taste information to generate an optimized taste profile.

[0904] (Claim 3)

[0905] The system according to claim 1, wherein the generated recipes take into account the selection of healthy ingredients, with particular attention to adjusting nutritional value and salt content.

[0906] "Application example 2 when combining with an emotional engine"

[0907] (Claim 1)

[0908] A means for users to input taste and emotional information about nostalgic dishes,

[0909] A method using an emotion engine to analyze emotional states,

[0910] A means of obtaining information on ingredients, seasonings, and regional taste trends,

[0911] This includes means for analyzing this information and generating healthy and optimized recipes based on individual emotional states, including an artificial intelligence model.

[0912] A means of visually presenting generated recipes to users and enabling emotion-based delivery orders,

[0913] A means of receiving user feedback and new emotional information to improve the model,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, wherein an artificial intelligence model quantifies a taste profile and an emotional profile using taste information and emotional information entered by the user.

[0917] (Claim 3)

[0918] The system according to claim 1, wherein the generated recipe takes into account the nutritional value of the ingredients and adjusts the amount of salt and sugar in particular. [Explanation of Symbols]

[0919] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input taste information about nostalgic dishes, A means of obtaining information on ingredients, seasonings, and regional taste trends, This includes means such as an artificial intelligence model for analyzing this information and generating healthy recipes, A means of presenting the generated recipe to the user, A means of receiving user feedback and improving the model, A system that includes this.

2. The system according to claim 1, wherein an artificial intelligence model quantifies a taste profile using taste information entered by a user.

3. The system according to claim 1, wherein the generated recipe takes into account the nutritional value of the ingredients and adjusts the amount of salt in particular.

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