system

Abstract

We provide the system. [Solution] A means of collecting memory data about users' tastes, A means for integrating regionally specific taste data, food ingredient information data, and seasoning information data based on the aforementioned memory data, A means for generating cooking ingredient data using a machine learning algorithm based on the aforementioned integrated data, A means of adjusting the generated formulation data based on the user's health information, The system provides users with cooking instructions based on the adjusted formulation data, and a means of directly purchasing ingredients via an electronic payment system. A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: 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 as a 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] It is currently difficult to reproduce the nostalgic flavors that people have experienced in the past, and it becomes even more complicated especially when there are dietary restrictions. Also, while taking into account individual health conditions, there is a demand to provide a satisfying taste, but a system corresponding to such needs does not yet exist. As a result, there is a demand for the development of an integrated cooking support technology that satisfies various individual preferences and at the same time supports health management.

Means for Solving the Problems

[0005] This invention collects memory data on tastes from users and integrates regional taste data, ingredient information data, and seasoning information data based on that data. Then, artificial intelligence is used to analyze this integrated data and generate formulation data for cooking. The generated formulation data is then adjusted based on the user's health information, and cooking procedures based on the adjusted formulation data are provided to the user. In this way, it is possible to reproduce past tastes and realize a healthy and satisfying eating experience even for those with dietary restrictions.

[0006] A "user" is an individual who uses this system and wishes to recreate past flavors.

[0007] "Taste-related memory data" refers to data that includes information about the characteristics of flavors and specific dishes that the user has experienced in the past.

[0008] "Regional taste data" refers to data that includes information about cooking methods and seasonings commonly used in a particular region.

[0009] "Food ingredient information data" refers to data that includes information about food ingredients such as their seasonality, nutritional content, and origin.

[0010] "Seasoning information data" refers to data that includes information about the types, characteristics, and combinations of available seasonings.

[0011] "Integration" is the process of combining multiple different data sets to form a single dataset.

[0012] "Artificial intelligence" is a technology that uses computers to imitate human intellectual behavior, and is a system that has the ability to analyze data and make judgments.

[0013] "Formulation data" refers to information about the combinations and proportions of ingredients and seasonings necessary to create a specific flavor.

[0014] "Health information" refers to information regarding the user's health status and includes data such as dietary restrictions and allergy information.

[0015] "Cooking procedure" refers to a description of the specific means and steps for cooking ingredients to complete a dish.

Brief Explanation of Drawings

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention constitutes a system that provides health-conscious meal recipes while reproducing the past taste experiences of individual users. The system is implemented as a user terminal, a server that processes data, and a program that coordinates them.

[0038] Users access the system from a terminal with a dedicated interface and input data about their taste memories, health status, and the dishes they wish to recreate. The terminal formats this data and sends it to the server.

[0039] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. The server integrates this information and analyzes it using an artificial intelligence model to generate the optimal combination of ingredients and seasonings.

[0040] The generated combinations are adjusted as needed, taking into account the user's health information (e.g., salt restrictions or allergy information). This adjustment process allows users to enjoy nostalgic flavors from the past without compromising their health.

[0041] Finally, the server generates a refined recipe, which includes specific ingredient proportions and cooking instructions, and sends it to the terminal. The terminal displays the received recipe to the user and provides guidance for cooking.

[0042] For example, if a user wants to recreate a ramen dish that takes health into consideration, they would input the characteristics of a ramen they previously enjoyed (e.g., soy sauce-based with less oil). The server would then analyze this information, combining it with a general recipe for that past soy sauce ramen and currently available ingredient information, to create a recipe for a healthier soup. In this way, users can safely enjoy their favorite flavors while maintaining their health.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user accesses the terminal and inputs data about their taste preferences, desired dishes, and health status through a dedicated interface. The terminal organizes this information into a form.

[0046] Step 2:

[0047] The device collects data entered by the user and sends the formatted dataset to the server. This includes information about specific taste characteristics and health restrictions.

[0048] Step 3:

[0049] The server stores the data received from the terminal in a data storage for analysis and compares it with a database containing regionally specific taste data, ingredient information, and seasoning information.

[0050] Step 4:

[0051] Based on information extracted from the database, the server uses an artificial intelligence model to calculate the optimal combination of ingredients and seasonings to reproduce the characteristics of the flavor.

[0052] Step 5:

[0053] The server generates combinations that are compared with the user's health information, adjusts the ingredient data as needed, and creates health-conscious recipes. For example, if the user needs to reduce salt intake, alternative seasonings will be used.

[0054] Step 6:

[0055] The server sends the adjusted recipe to the terminal, which then displays it to the user. The displayed recipe includes detailed instructions for cooking and information on the necessary ingredients.

[0056] Step 7:

[0057] Users view the recipe displayed on their device and begin cooking according to the guidance. This allows them to enjoy familiar flavors while maintaining a healthy diet.

[0058] (Example 1)

[0059] 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."

[0060] There is a need to faithfully reproduce each user's past taste experiences while also suggesting healthy meals. However, conventional systems have been unable to fully utilize individual taste memories and health information, and have been unable to provide optimal combinations of ingredients and seasonings. As a result, users have faced the challenge of finding it difficult to balance taste satisfaction with maintaining good health.

[0061] 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.

[0062] In this invention, the server includes means for collecting taste-related information from users, means for comparing the collected information with region-specific taste information, ingredient information, and seasoning information, means for generating optimal blend data using a generative AI model, means for adjusting the blend data based on health restriction information, and means for providing cooking instructions based on the adjusted blend data. This makes it possible for individual users to enjoy healthy meals while reminiscing about their rich past taste experiences.

[0063] "Users" refers to individuals who use the system to recreate their own taste experiences and obtain health-conscious meals.

[0064] "Information related to taste" refers to all information, including data on the user's past taste experiences and preferences, as well as data on dishes they wish to recreate.

[0065] An "information processing device" refers to a computer system that performs processing such as data matching, analysis, and generation based on received information.

[0066] "Regional taste information" refers to data on common ingredients and flavor harmonies in each region.

[0067] "Food ingredient information" refers to information that includes data on the nutritional value, availability, and cooking methods of various food ingredients.

[0068] "Seasoning information" refers to data on various seasonings, including their properties, suitable dishes to combine them with, and usage methods.

[0069] A "generative AI model" refers to artificial intelligence technology that analyzes input data and generates optimal blending data.

[0070] "Formulation data" refers to the optimal combination of ingredients and seasonings for a particular dish.

[0071] "Health restriction information" refers to individual health-related data such as the user's health status, dietary restrictions, and allergy information.

[0072] "Cooking instructions" refers to information that details the steps for preparing a dish based on the generated and adjusted formula data.

[0073] This invention is a system that uses terminals, servers, and a generative AI model that connects them to reproduce past taste experiences for individual users while suggesting health-conscious meals.

[0074] Users access the system through a terminal and input information about their past favorite flavors and dishes they wish to recreate. This terminal runs dedicated application software, formats the user data, and sends it to the server. The terminal operates on a standard operating system and communicates with the server via an internet connection.

[0075] The server processes the received data and matches it against a database of regional taste information, ingredients, and seasonings. The server possesses powerful processing capabilities and uses a generative AI model to analyze the received data and generate optimal ingredient and seasoning combinations to recreate each user's past taste experiences. This generation process takes into account the user's health-related information, such as salt intake restrictions and allergy information.

[0076] The generated combination of ingredients and seasonings is further adjusted on the server side to optimize it so as not to have any adverse health effects. This process allows users to safely obtain an ideal taste experience that goes beyond their individual health constraints. The server sends the adjusted recipe back to the terminal, which then presents it to the user, providing specific cooking instructions and guidance.

[0077] As a concrete example, consider a case where a user wants to recreate a healthy ramen. If the user inputs information that they previously preferred soy sauce-based ramen with less oil, the server analyzes this information and suggests a healthier alternative recipe. The generating AI model selects ingredients from the available ingredient database to create a soup recipe that is low in salt while maintaining the characteristics of the original flavor.

[0078] An example of a prompt message is, "Can you suggest a recipe that recreates the taste of a low-oil soy sauce ramen I used to enjoy, while also being health-conscious?" In this way, users can enjoy the taste of their past memories while maintaining their health.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1: Enter user data

[0081] The user accesses the terminal and inputs information about their past taste preferences, current health status, and the dishes they wish to recreate. This input data is formatted on the terminal and prepared for transmission to the server. Specifically, the user's selected taste characteristics and health constraints (e.g., low-sodium) are stored as input data.

[0082] Step 2: Sending and receiving data

[0083] The terminal sends the formatted data to the server via the internet. The server verifies this received data, converts it to the required format, and prepares it for matching against the database. Specifically, the terminal sends data to the server using an HTTP request.

[0084] Step 3: Matching the title

[0085] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. This comparison prepares the basic information necessary to satisfy the user's request. Inputs include regional information and ingredient types, and output is a list of matched ingredients.

[0086] Step 4: Analysis using a generative AI model

[0087] The server uses a generative AI model to analyze the matched data and derive the optimal combination of ingredients and seasonings based on the user's taste experience and health constraints. This process involves a large amount of data calculation, and specific ingredient / seasoning data is generated as output.

[0088] Step 5: Adjusting the recipe

[0089] The server adjusts the generated formula data based on the user's health information, particularly considering salt restrictions and allergy information. The specific output is a list of the adjusted ingredient formulations and seasonings.

[0090] Step 6: Generate and serve cooking instructions

[0091] The server generates specific cooking instructions based on the adjusted formula data. These instructions are sent to the terminal and provided to the user. The terminal acts as a guide, assisting with cooking visually or audibly. The generating AI model prepares user guidance using appropriate prompts to show the user exactly how to proceed with cooking.

[0092] (Application Example 1)

[0093] 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."

[0094] For today's busy consumers, easily preparing healthy meals while recreating past culinary experiences is difficult. Furthermore, purchasing the appropriate ingredients requires multiple methods and is time-consuming. In this situation, there is a need for a way to recreate past flavors while considering individual health conditions, and to quickly procure the necessary ingredients.

[0095] 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.

[0096] In this invention, the server includes means for collecting memory data related to the user's taste, means for integrating regionally specific taste data, information data on food ingredients, and information data on seasonings, means for generating formulation data for cooking using a machine learning algorithm, and means for adjusting the generated formulation data and purchasing ingredients directly via an electronic payment system. This enables the user to healthily recreate past taste experiences and purchase necessary ingredients quickly and smoothly.

[0097] A "user" is an individual who uses this system to recreate past taste experiences and prepare meals that are mindful of their health.

[0098] "Memory data" refers to information about a user's past taste experiences, including data on their preferred flavors and ingredients.

[0099] "Region-specific taste data" refers to information about the characteristics of taste and cooking styles in a specific geographical area.

[0100] "Food ingredient information data" refers to detailed information about ingredients, such as their type, nutritional value, and frequency of use.

[0101] "Seasoning information data" refers to data that includes information about the type of seasoning, its ingredients, and its flavor characteristics.

[0102] A "machine learning algorithm" is an artificial intelligence technology that analyzes data, identifies patterns and relationships, and then generates new insights and predictions.

[0103] "Formulation data" refers to information compiled about the appropriate combinations of ingredients and seasonings needed for cooking.

[0104] An "electronic payment system" is an online platform used to settle payments for goods and services via the internet.

[0105] This invention is a system that provides health-conscious meal recipes while taking into account the user's past taste experiences. The system consists of a user terminal, a server that processes data, and a program that coordinates them.

[0106] Users can access the system via smartphones or other computing devices and input data about their taste memories, health status, and dishes they wish to recreate. The user's device then formats this data and sends it to the server.

[0107] The server processes received data using Python and Flask. On the server, user input data is stored in a PostgreSQL database and compared against region-specific taste data, food ingredient data, and seasoning data. At this stage, by referring to the information stored in the database, basic information is obtained to recreate past taste experiences.

[0108] The server further analyzes the integrated data using machine learning algorithms to generate the optimal combination of ingredients and seasonings. Based on this, a generative AI model provides the optimal blend data. The generated blend data is then adjusted as needed based on the user's health information (e.g., salt restriction or allergy information).

[0109] The adjusted recipe is sent to the user's smartphone, where they can view it on their screen. Furthermore, they can purchase the necessary ingredients directly through an electronic payment system, utilizing existing online payment platforms such as the Stripe API. This seamless data processing and payment process allows users to easily obtain the necessary ingredients and recreate a healthy, nostalgic taste experience.

[0110] For example, if a user wants to recreate a classic soup from a long-established restaurant in a healthy way, they can input its characteristics, and the server will generate a corresponding recipe. The user can then immediately purchase the necessary ingredients within the app.

[0111] Examples of prompts to input into a generative AI model include the following:

[0112] "Based on the user's past taste experiences, generate health-conscious recipes and list the necessary ingredients."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] Users access the application using their smartphones and input data about their taste preferences, health status, and the dishes they wish to recreate. The input data is formatted by the user interface and temporarily stored on the device. This process yields tangible, raw data based on the user's requests.

[0116] Step 2:

[0117] The terminal sends the formatted data to the server. The server analyzes the received data, converts it to JSON format, and prepares it for processing. The input data includes the user's desired taste characteristics and health status, and the output provides information for database searching.

[0118] Step 3:

[0119] The server compares region-specific taste data, food ingredient data, and seasoning data stored in the database with data received from the user. Here, the server uses SQL queries to extract relevant data and compare it with the input. The output of this process is a list of potential recipes that match the user's request.

[0120] Step 4:

[0121] The server uses machine learning algorithms to generate optimal ingredient and seasoning combinations based on the matched data. The generative AI model uses prompts to analyze recipe candidates and output combinations suitable for the input. The output is a list of optimized ingredients and seasonings.

[0122] Step 5:

[0123] The server adjusts the recipe based on the output combinations, taking into account the user's health information. It filters for allergy information and nutritional restrictions to generate a customized recipe. The output consists of health-conscious cooking instructions.

[0124] Step 6:

[0125] The server sends the final recipe and required ingredient information to the terminal, which displays the data on the user's screen. The user can then review the recipe and it becomes ready to use. Furthermore, an option is provided to purchase the necessary ingredients directly using an electronic payment system. The output consists of user-accessible display information.

[0126] 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.

[0127] This invention incorporates an emotion engine into a system that reproduces a user's past taste experiences and provides health-conscious recipes, thereby offering cooking suggestions tailored to the user's emotional state. The system is implemented by combining the user's terminal, a data processing server, and an emotion recognition engine.

[0128] Users access the system from a terminal with a dedicated interface, inputting information about taste memories, dishes they want to recreate, and their health status, as well as providing their own emotional data through an emotion sensor built into the terminal. The terminal then compiles this information and sends it to the server.

[0129] Upon receiving this data, the server compares it against a database containing regionally specific taste data, ingredient information, and seasoning information, while also taking into account emotional data analyzed by the emotion engine. The server integrates this information and uses an artificial intelligence model to generate the optimal combination of ingredients and seasonings. Furthermore, the emotion engine further customizes the recipe to match the user's emotional state based on the emotional data.

[0130] For example, if the emotion engine detects that the user is feeling down, the server might suggest adding fragrant spices to improve their mood. The recipes provided in this way not only recreate memories but also offer an experience best suited to the user's current emotional state.

[0131] The server ultimately sends the adjusted recipe to the device, which then presents it to the user. The user cooks based on the recipe displayed on the device, enjoying the taste of the past while also gaining an emotionally stimulating dining experience.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user accesses the device and inputs data such as taste memory information, the dish they want to recreate, and their health status through a dedicated interface. The device also collects emotional data such as the user's facial expressions and voice tone.

[0135] Step 2:

[0136] The device formats the taste information entered by the user, health data, and emotional data collected through the emotion sensor, and then sends it to the server.

[0137] Step 3:

[0138] The server receives and stores data sent from the terminal. This includes data related to taste memories, health information, and emotional data generated using the emotion engine.

[0139] Step 4:

[0140] Based on the data received by the server, regionally specific taste data, ingredient information, and seasoning information are extracted from the database and integrated with emotional data.

[0141] Step 5:

[0142] The server uses an artificial intelligence model to analyze and integrate data, generating the optimal combination of ingredients and seasonings. The emotion engine adjusts the recipe based on emotional data. For example, if the user is stressed, it recommends ingredients with relaxing effects.

[0143] Step 6:

[0144] The server considers the generated combinations, adjusts recipes to suit the user's health condition, and optimizes the cooking procedure.

[0145] Step 7:

[0146] The server sends a refined and optimized recipe to the terminal, which then presents it to the user. The displayed recipe includes the ingredients used, their proportions, and cooking instructions. It also offers ingredient suggestions tailored to the user's mood.

[0147] Step 8:

[0148] Users cook according to the recipe displayed on their device. This allows for the recreation of past flavors, while also considering health and providing a dining experience tailored to their emotional state.

[0149] (Example 2)

[0150] 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".

[0151] Conventional cooking suggestion systems focused on recreating users' past taste experiences, but lacked recipe suggestions that took into account the user's health condition or temporary emotional state. As a result, users may gain satisfaction based on past memories, but struggle to obtain a dining experience optimized for their current physical and mental state.

[0152] 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.

[0153] In this invention, the server includes means for collecting the user's taste memories, means for integrating information on regionally specific tastes, ingredients, and seasonings, and means for adjusting the generated formula based on the user's health condition and further considering the user's emotional state. This makes it possible to reproduce the user's past taste experiences while providing an optimal recipe tailored to their health and emotional state.

[0154] "Users" refer to individuals or groups who utilize this system and are the entities that receive suggestions for recreating taste experiences and providing health-conscious recipes.

[0155] "Memory" refers to information about tastes that the user has experienced in the past, including personal memories of specific meals or dishes.

[0156] "Regionally specific tastes" refer to the taste profiles and eating habits that are generally recognized in a particular geographical area.

[0157] "Ingredients" refers to the basic building blocks or components of a dish, and generally includes both fresh and processed foods.

[0158] "Seasonings" are ingredients used to adjust the taste and aroma of food, and include spices, herbs, sauces, and other seasonings.

[0159] An "information processing device" refers to a computer system or electronic device that receives, analyzes, and integrates data to generate optimal output.

[0160] "Health status" refers to all health-related conditions, including the user's physical condition, pre-existing conditions, and nutrient needs, and is an important factor in optimizing recipes.

[0161] "Emotional state" refers to the user's mood and psychological state, which can influence their choice of dishes and suggestions for ingredients.

[0162] "Combination" refers to the combination and proportion of ingredients and seasonings, and is an important element that affects the final taste and quality of the dish.

[0163] "Cooking instructions" refer to the steps and processes for preparing a particular dish, and are a set of instructions provided to the user.

[0164] This invention aims to provide recipes based on a user's health and emotional state, while recreating their past taste experiences. The system operates by combining the user's terminal, a server responsible for data processing, and an engine for analyzing emotions.

[0165] Users access the system using a terminal with a dedicated interface. The terminal has the functionality to allow users to input data such as taste memory information, dishes they wish to recreate, and health status. Furthermore, the terminal is equipped with an emotion sensor, enabling it to acquire the user's emotional data in real time. This data is organized and integrated by the terminal and then transmitted to the server.

[0166] The server processes the received data. First, it compares the user's input information with a database containing information on regionally specific tastes, ingredients, and seasonings. Next, an emotion engine installed within the server analyzes the user's emotional data. The analysis results are integrated into the recipe generation process by a generative AI model.

[0167] The generative AI model integrates database matching results and emotion analysis results to derive an optimized combination of ingredients and seasonings for the user. This process considers the user's health condition and provides a taste experience that corresponds to their current emotional state.

[0168] As a concrete example, a prompt to a generative AI model might be used in the following form: "Please suggest a recipe suitable for a user who is feeling down." This model could then suggest specific spices or ingredients to alleviate feelings of sadness.

[0169] Finally, the server sends the adjusted recipe to the device. The device then displays the recipe, including specific cooking instructions, to the user, helping them to recreate past taste experiences while also considering their health and emotional state.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The device collects information from the user.

[0173] Specifically, the user inputs information about their past taste experiences, dishes they want to recreate, and their health status through the device's interface. For example, they might input "soup with lots of carrots and onions" or "I want to maintain my health with low-calorie meals." The device also uses an emotion sensor to measure the user's emotional data. The input taste and emotional data are then compiled on the device.

[0174] Step 2:

[0175] The terminal sends the organized data to the server.

[0176] The device organizes taste information, health-related requests, and emotional data collected from the user into a single data package. This data package is then transmitted to the server in digital format. Specifically, the output data package might contain information such as "Taste: Sweet, Ingredients: Carrots, Onions, Health Information: Low Calorie, Emotional State: Want to Relax."

[0177] Step 3:

[0178] The server analyzes the received data and compares it against the database.

[0179] When the server receives a data package, it first consults its internal database. It then performs a process of matching the entered data with regionally specific taste data, ingredient information, and seasoning information. As a result of this database matching, appropriate dish and ingredient options are listed. The output result is "Recommended ingredients: bay leaf, olive oil, related recipe: French soup."

[0180] Step 4:

[0181] The server uses an emotion engine to analyze the emotion data.

[0182] The server activates an emotion engine to analyze the user's emotional data. The data processing performed by the emotion engine includes analyzing the type and intensity of emotions. As a result of the analysis, suggestions such as "Ingredients effective for improving the user's mood: mint, honey" are output.

[0183] Step 5:

[0184] The server generates recipes using a generative AI model.

[0185] The server integrates the emotion analysis results and database matching results, and uses a generative AI model to generate the optimal recipe. This model suggests ingredients and seasonings according to specific emotional and health states. An example of a generated recipe would be "Relaxing Carrot and Onion Soup with Aromatic Steam."

[0186] Step 6:

[0187] The server sends the final generated recipe to the terminal.

[0188] The server sends the completed and generated recipe to the terminal in digital format. This allows the terminal to present the user with the latest recipe. The output will be "Soup Recipe Details: Ingredients, Instructions, Special Notes (Explanation of Emotional Improvement Effects)".

[0189] Step 7:

[0190] The device displays recipes customized for the user.

[0191] The terminal displays recipes received from the server to the user, providing detailed instructions for the necessary cooking steps. Specifically, the terminal displays the cooking steps step by step, along with information related to the recipe, such as emotional enhancement and health maintenance. The displayed result might be, "How to make carrot soup: Cut, sauté, and simmer vegetables; recommended for relaxation."

[0192] (Application Example 2)

[0193] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0194] Conventional technologies have been unable to effectively consider the user's emotional state or health condition when recreating their taste experience. In particular, there is no system in place where cooking robots automatically prepare meals using this information, making it difficult to optimize cooking for individual users. Therefore, providing a personalized dining experience that takes into account the user's health and emotions has been a challenge.

[0195] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0196] In this invention, the server includes means for collecting memory data related to the user's taste, means for adjusting the generated formula data based on the user's health information and emotional information, and means for providing the user with cooking procedures that correspond to their emotional state based on the adjusted formula data. This makes it possible for the cooking robot to automatically prepare a dish suitable for the user and provide a personalized dining experience that corresponds to the user's emotions and health state.

[0197] A "user" is a person who operates the system and is the subject of the dining experience.

[0198] "Taste-related memory data" refers to data that includes information about tastes that the user has experienced in the past.

[0199] "Region-specific taste data" refers to data that records the taste characteristics generally recognized in a particular region.

[0200] "Ingredient information data" refers to data that shows information such as attributes and nutritional value of ingredients used in cooking.

[0201] "Seasoning information data" refers to data that records information about seasonings used to flavor dishes.

[0202] Artificial intelligence is a technology that gives computers the ability to learn and make decisions like humans.

[0203] "Formulation data" refers to data that records the combinations of ingredients and seasonings necessary to make a specific dish.

[0204] "Health information" refers to data that is related to the user's physical health status.

[0205] "Emotional information" refers to data that indicates the user's current emotional state.

[0206] "Cooking instructions" refer to information that outlines the specific operations and steps required to prepare a particular dish.

[0207] A "robot" is a mechanical device that has the ability to perform specific tasks automatically.

[0208] A "server" is a central processing unit used for processing and storing data.

[0209] The system that realizes this invention exchanges data between the user and the robot to provide a personalized cooking experience.

[0210] The system primarily consists of a user terminal, a server for data processing, and a robot responsible for cooking. The user terminal has a dedicated interface and provides a means for inputting the user's taste memory data, health information, and emotional information. The terminal is also equipped with an emotion sensor that detects emotional information from facial expressions and voice.

[0211] The server receives and processes this data. The server is equipped with a database containing region-specific taste data, ingredient information, and seasoning information, which allows it to generate optimal recipes for users using an emotion engine and an AI model. The AI ​​model uses a generative AI model to suggest dishes based on the user's emotional state through prompt messages. These generated suggestions are then sent to the cooking robot.

[0212] The robot is equipped with the necessary hardware for cooking and automatically prepares ingredients and cooks based on recipes generated by an AI model. The robot uses the cooking process to create a meal tailored to the user's healthcare needs and emotional state.

[0213] For example, if an emotion sensor detects a user's emotional state (such as fatigue) after they return from work, the server will suggest a meal that helps reduce stress. For instance, a relaxing herbal tea or a highly nutritious meal might be prepared. An example of a prompt to the generative AI model would be: "The user has returned home from work and is tired. Please suggest a recipe that is ideal for relaxation and nutritional replenishment."

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

[0215] Step 1:

[0216] The user uses an interface installed on their device to input memory data about taste, their current emotional state, and health information. The input data is organized into structured data that reflects taste preferences and health status, and then sent from the device to the server.

[0217] Step 2:

[0218] The server receives data sent from the terminal and first uses an emotion recognition engine to analyze the user's emotional information. It converts the emotional information data, which is the input, into emotion parameters and outputs the user's current emotional state.

[0219] Step 3:

[0220] The server uses emotion parameters as prompts for the AI ​​model, generating an example prompt: "The user is tired. Please suggest a dish that will have a relaxing effect on this state." Next, the AI ​​model is executed based on this prompt to generate recipe data.

[0221] Step 4:

[0222] The server compares the generated recipe data with regionally specific taste data and ingredient information, and adjusts the recipes while taking into account the user's health information. It uses the generated recipe data and health status data as input and outputs the adjusted recipe data.

[0223] Step 5:

[0224] The adjusted recipe data is sent from the server to the robot. The robot follows the recipe steps and automatically performs cooking operations such as weighing, heating, and mixing the necessary ingredients. After cooking is complete, the dish is served to the user.

[0225] Step 6:

[0226] The user then provides feedback through their device regarding the provided dish. The server stores this feedback data and uses it as training data for future recipe suggestions.

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

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

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

[0230] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0243] This invention constitutes a system that provides health-conscious meal recipes while reproducing the past taste experiences of individual users. The system is implemented as a user terminal, a server that processes data, and a program that coordinates them.

[0244] Users access the system from a terminal with a dedicated interface and input data about their taste memories, health status, and the dishes they wish to recreate. The terminal formats this data and sends it to the server.

[0245] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. The server integrates this information and analyzes it using an artificial intelligence model to generate the optimal combination of ingredients and seasonings.

[0246] The generated combinations are adjusted as needed, taking into account the user's health information (e.g., salt restrictions or allergy information). This adjustment process allows users to enjoy nostalgic flavors from the past without compromising their health.

[0247] Finally, the server generates a refined recipe, which includes specific ingredient proportions and cooking instructions, and sends it to the terminal. The terminal displays the received recipe to the user and provides guidance for cooking.

[0248] For example, if a user wants to recreate a ramen dish that takes health into consideration, they would input the characteristics of a ramen they previously enjoyed (e.g., soy sauce-based with less oil). The server would then analyze this information, combining it with a general recipe for that past soy sauce ramen and currently available ingredient information, to create a recipe for a healthier soup. In this way, users can safely enjoy their favorite flavors while maintaining their health.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user accesses the terminal and inputs data about their taste preferences, desired dishes, and health status through a dedicated interface. The terminal organizes this information into a form.

[0252] Step 2:

[0253] The device collects data entered by the user and sends the formatted dataset to the server. This includes information about specific taste characteristics and health restrictions.

[0254] Step 3:

[0255] The server stores the data received from the terminal in a data storage for analysis and compares it with a database containing regionally specific taste data, ingredient information, and seasoning information.

[0256] Step 4:

[0257] Based on information extracted from the database, the server uses an artificial intelligence model to calculate the optimal combination of ingredients and seasonings to reproduce the characteristics of the flavor.

[0258] Step 5:

[0259] The server generates combinations that are compared with the user's health information, adjusts the ingredient data as needed, and creates health-conscious recipes. For example, if the user needs to reduce salt intake, alternative seasonings will be used.

[0260] Step 6:

[0261] The server sends the adjusted recipe to the terminal, which then displays it to the user. The displayed recipe includes detailed instructions for cooking and information on the necessary ingredients.

[0262] Step 7:

[0263] Users view the recipe displayed on their device and begin cooking according to the guidance. This allows them to enjoy familiar flavors while maintaining a healthy diet.

[0264] (Example 1)

[0265] 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."

[0266] There is a need to faithfully reproduce each user's past taste experiences while also suggesting healthy meals. However, conventional systems have been unable to fully utilize individual taste memories and health information, and have been unable to provide optimal combinations of ingredients and seasonings. As a result, users have faced the challenge of finding it difficult to balance taste satisfaction with maintaining good health.

[0267] 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.

[0268] In this invention, the server includes means for collecting taste-related information from users, means for comparing the collected information with region-specific taste information, ingredient information, and seasoning information, means for generating optimal blend data using a generative AI model, means for adjusting the blend data based on health restriction information, and means for providing cooking instructions based on the adjusted blend data. This makes it possible for individual users to enjoy healthy meals while reminiscing about their rich past taste experiences.

[0269] "Users" refers to individuals who use the system to recreate their own taste experiences and obtain health-conscious meals.

[0270] "Information related to taste" refers to all information, including data on the user's past taste experiences and preferences, as well as data on dishes they wish to recreate.

[0271] An "information processing device" refers to a computer system that performs processing such as data matching, analysis, and generation based on received information.

[0272] "Regional taste information" refers to data on common ingredients and flavor harmonies in each region.

[0273] "Food ingredient information" refers to information that includes data on the nutritional value, availability, and cooking methods of various food ingredients.

[0274] "Seasoning information" refers to data on various seasonings, including their properties, suitable dishes to combine them with, and usage methods.

[0275] A "generative AI model" refers to artificial intelligence technology that analyzes input data and generates optimal blending data.

[0276] "Formulation data" refers to the optimal combination of ingredients and seasonings for a particular dish.

[0277] "Health restriction information" refers to individual health-related data such as the user's health status, dietary restrictions, and allergy information.

[0278] "Cooking instructions" refers to information that details the steps for preparing a dish based on the generated and adjusted formula data.

[0279] This invention is a system that uses terminals, servers, and a generative AI model that connects them to reproduce past taste experiences for individual users while suggesting health-conscious meals.

[0280] Users access the system through a terminal and input information about their past favorite flavors and dishes they wish to recreate. This terminal runs dedicated application software, formats the user data, and sends it to the server. The terminal operates on a standard operating system and communicates with the server via an internet connection.

[0281] The server processes the received data and matches it against a database of regional taste information, ingredients, and seasonings. The server possesses powerful processing capabilities and uses a generative AI model to analyze the received data and generate optimal ingredient and seasoning combinations to recreate each user's past taste experiences. This generation process takes into account the user's health-related information, such as salt intake restrictions and allergy information.

[0282] The generated combinations of ingredients and seasonings are further adjusted on the server side and optimized to have no adverse effects on health. Through this process, users can safely obtain an ideal taste experience that exceeds their individual health constraints. The server sends the adjusted recipe back to the terminal, and the terminal presents it to the user and provides specific cooking procedures and guides.

[0283] As a specific example, consider the case where a user wants to reproduce a healthy ramen. When the user inputs information that they have previously liked to eat ramen with a soy sauce base and less oil, the server analyzes the data based on this information and proposes a healthy alternative recipe. The generative AI model selects from the available ingredient database while maintaining the taste characteristics to complete a soup recipe with reduced salt content.

[0284] As an example of a prompt sentence, there is an input such as "Can you propose a recipe that takes into account health while reproducing the taste of the less oily soy sauce ramen that I liked to eat in the past?" In this way, users can enjoy the taste of past memories while maintaining their health.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1: Input of user data

[0287] The user accesses the terminal and inputs information about the taste preferences they had in the past, their current health status, and the dish they want to reproduce. This input data is processed and formatted on the terminal in preparation for transmission to the server. Specifically, the taste characteristics and health constraints (e.g., less salt) selected by the user are stored as input data.

[0288] Step 2: Transmission and reception of data

[0289] The terminal sends the formatted data to the server via the internet. The server verifies this received data, converts it to the required format, and prepares it for matching against the database. Specifically, the terminal sends data to the server using an HTTP request.

[0290] Step 3: Matching the title

[0291] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. This comparison prepares the basic information necessary to satisfy the user's request. Inputs include regional information and ingredient types, and output is a list of matched ingredients.

[0292] Step 4: Analysis using a generative AI model

[0293] The server uses a generative AI model to analyze the matched data and derive the optimal combination of ingredients and seasonings based on the user's taste experience and health constraints. This process involves a large amount of data calculation, and specific ingredient / seasoning data is generated as output.

[0294] Step 5: Adjusting the recipe

[0295] The server adjusts the generated formula data based on the user's health information, particularly considering salt restrictions and allergy information. The specific output is a list of the adjusted ingredient formulations and seasonings.

[0296] Step 6: Generate and serve cooking instructions

[0297] The server generates specific cooking instructions based on the adjusted formula data. These instructions are sent to the terminal and provided to the user. The terminal acts as a guide, assisting with cooking visually or audibly. The generating AI model prepares user guidance using appropriate prompts to show the user exactly how to proceed with cooking.

[0298] (Application Example 1)

[0299] 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."

[0300] For today's busy consumers, easily preparing healthy meals while recreating past culinary experiences is difficult. Furthermore, purchasing the appropriate ingredients requires multiple methods and is time-consuming. In this situation, there is a need for a way to recreate past flavors while considering individual health conditions, and to quickly procure the necessary ingredients.

[0301] 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.

[0302] In this invention, the server includes means for collecting memory data related to the user's taste, means for integrating regionally specific taste data, information data on food ingredients, and information data on seasonings, means for generating formulation data for cooking using a machine learning algorithm, and means for adjusting the generated formulation data and purchasing ingredients directly via an electronic payment system. This enables the user to healthily recreate past taste experiences and purchase necessary ingredients quickly and smoothly.

[0303] A "user" is an individual who uses this system to recreate past taste experiences and prepare meals that are mindful of their health.

[0304] "Memory data" refers to information about a user's past taste experiences, including data on their preferred flavors and ingredients.

[0305] "Region-specific taste data" refers to information about the characteristics of taste and cooking styles in a specific geographical area.

[0306] "Food ingredient information data" refers to detailed information about ingredients, such as their type, nutritional value, and frequency of use.

[0307] "The information data of seasonings" refers to data containing information on the types, ingredients, taste characteristics, etc. of seasonings.

[0308] "Machine learning algorithm" refers to artificial intelligence technology that analyzes data, finds patterns and correlations, and makes new insights and predictions.

[0309] "Formulation data" refers to information summarizing the appropriate combinations of ingredients and seasonings required for cooking.

[0310] "Electronic payment system" refers to an online platform for settling the payment for goods and services via the Internet.

[0311] This invention is a system that provides a healthy diet recipe while considering the user's past taste experiences. This system is composed of a user terminal, a server that processes data, and a program that coordinates them.

[0312] The user can access the system via a smartphone or other computing device and input information regarding their taste memory, health condition, and data on the dish they want to reproduce. The user terminal formats these data and sends them to the server.

[0313] The server processes the received data using Python and Flask. In the server, the user's input data is stored in a PostgreSQL database and compared with region-specific taste data, food ingredient data, and seasoning data. At this stage, by referring to the information stored in the database, basic information for reproducing past taste experiences can be obtained.

[0314] The server further analyzes the integrated data using machine learning algorithms to generate the optimal combination of ingredients and seasonings. Based on this, a generative AI model provides the optimal blend data. The generated blend data is then adjusted as needed based on the user's health information (e.g., salt restriction or allergy information).

[0315] The adjusted recipe is sent to the user's smartphone, where they can view it on their screen. Furthermore, they can purchase the necessary ingredients directly through an electronic payment system, utilizing existing online payment platforms such as the Stripe API. This seamless data processing and payment process allows users to easily obtain the necessary ingredients and recreate a healthy, nostalgic taste experience.

[0316] For example, if a user wants to recreate a classic soup from a long-established restaurant in a healthy way, they can input its characteristics, and the server will generate a corresponding recipe. The user can then immediately purchase the necessary ingredients within the app.

[0317] Examples of prompts to input into a generative AI model include the following:

[0318] "Based on the user's past taste experiences, generate health-conscious recipes and list the necessary ingredients."

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

[0320] Step 1:

[0321] Users access the application using their smartphones and input data about their taste preferences, health status, and the dishes they wish to recreate. The input data is formatted by the user interface and temporarily stored on the device. This process yields tangible, raw data based on the user's requests.

[0322] Step 2:

[0323] The terminal sends the formatted data to the server. The server analyzes the received data, converts it to JSON format, and prepares it for processing. The input data includes the user's desired taste characteristics and health status, and the output provides information for database searching.

[0324] Step 3:

[0325] The server compares region-specific taste data, food ingredient data, and seasoning data stored in the database with data received from the user. Here, the server uses SQL queries to extract relevant data and compare it with the input. The output of this process is a list of potential recipes that match the user's request.

[0326] Step 4:

[0327] The server uses machine learning algorithms to generate optimal ingredient and seasoning combinations based on the matched data. The generative AI model uses prompts to analyze recipe candidates and output combinations suitable for the input. The output is a list of optimized ingredients and seasonings.

[0328] Step 5:

[0329] The server adjusts the recipe based on the output combinations, taking into account the user's health information. It filters for allergy information and nutritional restrictions to generate a customized recipe. The output consists of health-conscious cooking instructions.

[0330] Step 6:

[0331] The server sends the final recipe and required ingredient information to the terminal, which displays the data on the user's screen. The user can then review the recipe and it becomes ready to use. Furthermore, an option is provided to purchase the necessary ingredients directly using an electronic payment system. The output consists of user-accessible display information.

[0332] 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.

[0333] This invention incorporates an emotion engine into a system that reproduces a user's past taste experiences and provides health-conscious recipes, thereby offering cooking suggestions tailored to the user's emotional state. The system is implemented by combining the user's terminal, a data processing server, and an emotion recognition engine.

[0334] Users access the system from a terminal with a dedicated interface, inputting information about taste memories, dishes they want to recreate, and their health status, as well as providing their own emotional data through an emotion sensor built into the terminal. The terminal then compiles this information and sends it to the server.

[0335] Upon receiving this data, the server compares it against a database containing regionally specific taste data, ingredient information, and seasoning information, while also taking into account emotional data analyzed by the emotion engine. The server integrates this information and uses an artificial intelligence model to generate the optimal combination of ingredients and seasonings. Furthermore, the emotion engine further customizes the recipe to match the user's emotional state based on the emotional data.

[0336] For example, if the emotion engine detects that the user is feeling down, the server might suggest adding fragrant spices to improve their mood. The recipes provided in this way not only recreate memories but also offer an experience best suited to the user's current emotional state.

[0337] The server ultimately sends the adjusted recipe to the device, which then presents it to the user. The user cooks based on the recipe displayed on the device, enjoying the taste of the past while also gaining an emotionally stimulating dining experience.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The user accesses the device and inputs data such as taste memory information, the dish they want to recreate, and their health status through a dedicated interface. The device also collects emotional data such as the user's facial expressions and voice tone.

[0341] Step 2:

[0342] The device formats the taste information entered by the user, health data, and emotional data collected through the emotion sensor, and then sends it to the server.

[0343] Step 3:

[0344] The server receives and stores data sent from the terminal. This includes data related to taste memories, health information, and emotional data generated using the emotion engine.

[0345] Step 4:

[0346] Based on the data received by the server, regionally specific taste data, ingredient information, and seasoning information are extracted from the database and integrated with emotional data.

[0347] Step 5:

[0348] The server uses an artificial intelligence model to analyze and integrate data, generating the optimal combination of ingredients and seasonings. The emotion engine adjusts the recipe based on emotional data. For example, if the user is stressed, it recommends ingredients with relaxing effects.

[0349] Step 6:

[0350] The server considers the generated combinations, adjusts recipes to suit the user's health condition, and optimizes the cooking procedure.

[0351] Step 7:

[0352] The server sends a refined and optimized recipe to the terminal, which then presents it to the user. The displayed recipe includes the ingredients used, their proportions, and cooking instructions. It also offers ingredient suggestions tailored to the user's mood.

[0353] Step 8:

[0354] Users cook according to the recipe displayed on their device. This allows for the recreation of past flavors, while also considering health and providing a dining experience tailored to their emotional state.

[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] Conventional cooking suggestion systems focused on recreating users' past taste experiences, but lacked recipe suggestions that took into account the user's health condition or temporary emotional state. As a result, users may gain satisfaction based on past memories, but struggle to obtain a dining experience optimized for their current physical and mental state.

[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 collecting the user's taste memories, means for integrating information on regionally specific tastes, ingredients, and seasonings, and means for adjusting the generated formula based on the user's health condition and further considering the user's emotional state. This makes it possible to reproduce the user's past taste experiences while providing an optimal recipe tailored to their health and emotional state.

[0360] "Users" refer to individuals or groups who utilize this system and are the entities that receive suggestions for recreating taste experiences and providing health-conscious recipes.

[0361] "Memory" refers to information about tastes that the user has experienced in the past, including personal memories of specific meals or dishes.

[0362] "Regionally specific tastes" refer to the taste profiles and eating habits that are generally recognized in a particular geographical area.

[0363] "Ingredients" refers to the basic building blocks or components of a dish, and generally includes both fresh and processed foods.

[0364] "Seasonings" are ingredients used to adjust the taste and aroma of food, and include spices, herbs, sauces, and other seasonings.

[0365] An "information processing device" refers to a computer system or electronic device that receives, analyzes, and integrates data to generate optimal output.

[0366] "Health status" refers to all health-related conditions, including the user's physical condition, pre-existing conditions, and nutrient needs, and is an important factor in optimizing recipes.

[0367] "Emotional state" refers to the user's mood and psychological state, which can influence their choice of dishes and suggestions for ingredients.

[0368] "Combination" refers to the combination and proportion of ingredients and seasonings, and is an important element that affects the final taste and quality of the dish.

[0369] "Cooking instructions" refer to the steps and processes for preparing a particular dish, and are a set of instructions provided to the user.

[0370] This invention aims to provide recipes based on a user's health and emotional state, while recreating their past taste experiences. The system operates by combining the user's terminal, a server responsible for data processing, and an engine for analyzing emotions.

[0371] Users access the system using a terminal with a dedicated interface. The terminal has the functionality to allow users to input data such as taste memory information, dishes they wish to recreate, and health status. Furthermore, the terminal is equipped with an emotion sensor, enabling it to acquire the user's emotional data in real time. This data is organized and integrated by the terminal and then transmitted to the server.

[0372] The server processes the received data. First, it compares the user's input information with a database containing information on regionally specific tastes, ingredients, and seasonings. Next, an emotion engine installed within the server analyzes the user's emotional data. The analysis results are integrated into the recipe generation process by a generative AI model.

[0373] The generative AI model integrates database matching results and emotion analysis results to derive an optimized combination of ingredients and seasonings for the user. This process considers the user's health condition and provides a taste experience that corresponds to their current emotional state.

[0374] As a concrete example, a prompt to a generative AI model might be used in the following form: "Please suggest a recipe suitable for a user who is feeling down." This model could then suggest specific spices or ingredients to alleviate feelings of sadness.

[0375] Finally, the server sends the adjusted recipe to the device. The device then displays the recipe, including specific cooking instructions, to the user, helping them to recreate past taste experiences while also considering their health and emotional state.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] The device collects information from the user.

[0379] Specifically, the user inputs information about their past taste experiences, dishes they want to recreate, and their health status through the device's interface. For example, they might input "soup with lots of carrots and onions" or "I want to maintain my health with low-calorie meals." The device also uses an emotion sensor to measure the user's emotional data. The input taste and emotional data are then compiled on the device.

[0380] Step 2:

[0381] The terminal sends the organized data to the server.

[0382] The device organizes taste information, health-related requests, and emotional data collected from the user into a single data package. This data package is then transmitted to the server in digital format. Specifically, the output data package might contain information such as "Taste: Sweet, Ingredients: Carrots, Onions, Health Information: Low Calorie, Emotional State: Want to Relax."

[0383] Step 3:

[0384] The server analyzes the received data and compares it against the database.

[0385] When the server receives a data package, it first consults its internal database. It then performs a process of matching the entered data with regionally specific taste data, ingredient information, and seasoning information. As a result of this database matching, appropriate dish and ingredient options are listed. The output result is "Recommended ingredients: bay leaf, olive oil, related recipe: French soup."

[0386] Step 4:

[0387] The server uses an emotion engine to analyze the emotion data.

[0388] The server activates an emotion engine to analyze the user's emotional data. The data processing performed by the emotion engine includes analyzing the type and intensity of emotions. As a result of the analysis, suggestions such as "Ingredients effective for improving the user's mood: mint, honey" are output.

[0389] Step 5:

[0390] The server generates recipes using a generative AI model.

[0391] The server integrates the emotion analysis results and database matching results, and uses a generative AI model to generate the optimal recipe. This model suggests ingredients and seasonings according to specific emotional and health states. An example of a generated recipe would be "Relaxing Carrot and Onion Soup with Aromatic Steam."

[0392] Step 6:

[0393] The server sends the final generated recipe to the terminal.

[0394] The server sends the completed and generated recipe to the terminal in digital format. This allows the terminal to present the user with the latest recipe. The output will be "Soup Recipe Details: Ingredients, Instructions, Special Notes (Explanation of Emotional Improvement Effects)".

[0395] Step 7:

[0396] The device displays recipes customized for the user.

[0397] The terminal displays recipes received from the server to the user, providing detailed instructions for the necessary cooking steps. Specifically, the terminal displays the cooking steps step by step, along with information related to the recipe, such as emotional enhancement and health maintenance. The displayed result might be, "How to make carrot soup: Cut, sauté, and simmer vegetables; recommended for relaxation."

[0398] (Application Example 2)

[0399] 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."

[0400] Conventional technologies have been unable to effectively consider the user's emotional state or health condition when recreating their taste experience. In particular, there is no system in place where cooking robots automatically prepare meals using this information, making it difficult to optimize cooking for individual users. Therefore, providing a personalized dining experience that takes into account the user's health and emotions has been a challenge.

[0401] 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.

[0402] In this invention, the server includes means for collecting memory data related to the user's taste, means for adjusting the generated formula data based on the user's health information and emotional information, and means for providing the user with cooking procedures that correspond to their emotional state based on the adjusted formula data. This makes it possible for the cooking robot to automatically prepare a dish suitable for the user and provide a personalized dining experience that corresponds to the user's emotions and health state.

[0403] A "user" is a person who operates the system and is the subject of the dining experience.

[0404] "Taste-related memory data" refers to data that includes information about tastes that the user has experienced in the past.

[0405] "Region-specific taste data" refers to data that records the taste characteristics generally recognized in a particular region.

[0406] "Ingredient information data" refers to data that shows information such as attributes and nutritional value of ingredients used in cooking.

[0407] "Seasoning information data" refers to data that records information about seasonings used to flavor dishes.

[0408] Artificial intelligence is a technology that gives computers the ability to learn and make decisions like humans.

[0409] "Formulation data" refers to data that records the combinations of ingredients and seasonings necessary to make a specific dish.

[0410] "Health information" refers to data that is related to the user's physical health status.

[0411] "Emotional information" refers to data that indicates the user's current emotional state.

[0412] "Cooking instructions" refer to information that outlines the specific operations and steps required to prepare a particular dish.

[0413] A "robot" is a mechanical device that has the ability to perform specific tasks automatically.

[0414] A "server" is a central processing unit used for processing and storing data.

[0415] The system that realizes this invention exchanges data between the user and the robot to provide a personalized cooking experience.

[0416] The system primarily consists of a user terminal, a server for data processing, and a robot responsible for cooking. The user terminal has a dedicated interface and provides a means for inputting the user's taste memory data, health information, and emotional information. The terminal is also equipped with an emotion sensor that detects emotional information from facial expressions and voice.

[0417] The server receives and processes this data. The server is equipped with a database containing region-specific taste data, ingredient information, and seasoning information, which allows it to generate optimal recipes for users using an emotion engine and an AI model. The AI ​​model uses a generative AI model to suggest dishes based on the user's emotional state through prompt messages. These generated suggestions are then sent to the cooking robot.

[0418] The robot is equipped with the necessary hardware for cooking and automatically prepares ingredients and cooks based on recipes generated by an AI model. The robot uses the cooking process to create a meal tailored to the user's healthcare needs and emotional state.

[0419] For example, if an emotion sensor detects a user's emotional state (such as fatigue) after they return from work, the server will suggest a meal that helps reduce stress. For instance, a relaxing herbal tea or a highly nutritious meal might be prepared. An example of a prompt to the generative AI model would be: "The user has returned home from work and is tired. Please suggest a recipe that is ideal for relaxation and nutritional replenishment."

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

[0421] Step 1:

[0422] The user uses an interface installed on their device to input memory data about taste, their current emotional state, and health information. The input data is organized into structured data that reflects taste preferences and health status, and then sent from the device to the server.

[0423] Step 2:

[0424] The server receives data sent from the terminal and first uses an emotion recognition engine to analyze the user's emotional information. It converts the emotional information data, which is the input, into emotion parameters and outputs the user's current emotional state.

[0425] Step 3:

[0426] The server uses emotion parameters as prompts for the AI ​​model, generating an example prompt: "The user is tired. Please suggest a dish that will have a relaxing effect on this state." Next, the AI ​​model is executed based on this prompt to generate recipe data.

[0427] Step 4:

[0428] The server compares the generated recipe data with regionally specific taste data and ingredient information, and adjusts the recipes while taking into account the user's health information. It uses the generated recipe data and health status data as input, and outputs the adjusted recipe data.

[0429] Step 5:

[0430] The adjusted recipe data is sent from the server to the robot. The robot follows the recipe steps and automatically performs cooking operations such as weighing, heating, and mixing the necessary ingredients. After cooking is complete, the dish is served to the user.

[0431] Step 6:

[0432] The user then provides feedback through their device regarding the provided dish. The server stores this feedback data and uses it as training data for future recipe suggestions.

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

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

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

[0436] [Third Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0449] This invention constitutes a system that provides health-conscious meal recipes while reproducing the past taste experiences of individual users. The system is implemented as a user terminal, a server that processes data, and a program that coordinates them.

[0450] Users access the system from a terminal with a dedicated interface and input data about their taste memories, health status, and the dishes they wish to recreate. The terminal formats this data and sends it to the server.

[0451] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. The server integrates this information and analyzes it using an artificial intelligence model to generate the optimal combination of ingredients and seasonings.

[0452] The generated combinations are adjusted as needed, taking into account the user's health information (e.g., salt restrictions or allergy information). This adjustment process allows users to enjoy nostalgic flavors from the past without compromising their health.

[0453] Finally, the server generates a refined recipe, which includes specific ingredient proportions and cooking instructions, and sends it to the terminal. The terminal displays the received recipe to the user and provides guidance for cooking.

[0454] For example, if a user wants to recreate a ramen dish that takes health into consideration, they would input the characteristics of a ramen they previously enjoyed (e.g., soy sauce-based with less oil). The server would then analyze this information, combining it with a general recipe for that past soy sauce ramen and currently available ingredient information, to create a recipe for a healthier soup. In this way, users can safely enjoy their favorite flavors while maintaining their health.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The user accesses the terminal and inputs data about their taste preferences, desired dishes, and health status through a dedicated interface. The terminal organizes this information into a form.

[0458] Step 2:

[0459] The device collects data entered by the user and sends the formatted dataset to the server. This includes information about specific taste characteristics and health restrictions.

[0460] Step 3:

[0461] The server stores the data received from the terminal in a data storage for analysis and compares it with a database containing regionally specific taste data, ingredient information, and seasoning information.

[0462] Step 4:

[0463] Based on information extracted from the database, the server uses an artificial intelligence model to calculate the optimal combination of ingredients and seasonings to reproduce the characteristics of the flavor.

[0464] Step 5:

[0465] The server generates combinations that are compared with the user's health information, adjusts the ingredient data as needed, and creates health-conscious recipes. For example, if the user needs to reduce salt intake, alternative seasonings will be used.

[0466] Step 6:

[0467] The server sends the adjusted recipe to the terminal, which then displays it to the user. The displayed recipe includes detailed instructions for cooking and information on the necessary ingredients.

[0468] Step 7:

[0469] Users view the recipe displayed on their device and begin cooking according to the guidance. This allows them to enjoy familiar flavors while maintaining a healthy diet.

[0470] (Example 1)

[0471] 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."

[0472] There is a need to faithfully reproduce each user's past taste experiences while also suggesting healthy meals. However, conventional systems have been unable to fully utilize individual taste memories and health information, and have been unable to provide optimal combinations of ingredients and seasonings. As a result, users have faced the challenge of finding it difficult to balance taste satisfaction with maintaining good health.

[0473] 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.

[0474] In this invention, the server includes means for collecting taste-related information from users, means for comparing the collected information with region-specific taste information, ingredient information, and seasoning information, means for generating optimal blend data using a generative AI model, means for adjusting the blend data based on health restriction information, and means for providing cooking instructions based on the adjusted blend data. This makes it possible for individual users to enjoy healthy meals while reminiscing about their rich past taste experiences.

[0475] "Users" refers to individuals who use the system to recreate their own taste experiences and obtain health-conscious meals.

[0476] "Information related to taste" refers to all information, including data on the user's past taste experiences and preferences, as well as data on dishes they wish to recreate.

[0477] An "information processing device" refers to a computer system that performs processing such as data matching, analysis, and generation based on received information.

[0478] "Regional taste information" refers to data on common ingredients and flavor harmonies in each region.

[0479] "Food ingredient information" refers to information that includes data on the nutritional value, availability, and cooking methods of various food ingredients.

[0480] "Seasoning information" refers to data on various seasonings, including their properties, suitable dishes to combine them with, and usage methods.

[0481] A "generative AI model" refers to artificial intelligence technology that analyzes input data and generates optimal blending data.

[0482] "Formulation data" refers to the optimal combination of ingredients and seasonings for a particular dish.

[0483] "Health restriction information" refers to individual health-related data such as the user's health status, dietary restrictions, and allergy information.

[0484] "Cooking instructions" refers to information that details the steps for preparing a dish based on the generated and adjusted formula data.

[0485] This invention is a system that uses terminals, servers, and a generative AI model that connects them to reproduce past taste experiences for individual users while suggesting health-conscious meals.

[0486] Users access the system through a terminal and input information about their past favorite flavors and dishes they wish to recreate. This terminal runs dedicated application software, formats the user data, and sends it to the server. The terminal operates on a standard operating system and communicates with the server via an internet connection.

[0487] The server processes the received data and matches it against a database of regional taste information, ingredients, and seasonings. The server possesses powerful processing capabilities and uses a generative AI model to analyze the received data and generate optimal ingredient and seasoning combinations to recreate each user's past taste experiences. This generation process takes into account the user's health-related information, such as salt intake restrictions and allergy information.

[0488] The generated combination of ingredients and seasonings is further adjusted on the server side to optimize it so as not to have any adverse health effects. This process allows users to safely obtain an ideal taste experience that goes beyond their individual health constraints. The server sends the adjusted recipe back to the terminal, which then presents it to the user, providing specific cooking instructions and guidance.

[0489] As a concrete example, consider a case where a user wants to recreate a healthy ramen. If the user inputs information that they previously preferred soy sauce-based ramen with less oil, the server analyzes this information and suggests a healthier alternative recipe. The generating AI model selects ingredients from the available ingredient database to create a soup recipe that is low in salt while maintaining the characteristics of the original flavor.

[0490] An example of a prompt message is, "Can you suggest a recipe that recreates the taste of a low-oil soy sauce ramen I used to enjoy, while also being health-conscious?" In this way, users can enjoy the taste of their past memories while maintaining their health.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1: Enter user data

[0493] The user accesses the terminal and inputs information about their past taste preferences, current health status, and the dishes they wish to recreate. This input data is formatted on the terminal and prepared for transmission to the server. Specifically, the user's selected taste characteristics and health constraints (e.g., low-sodium) are stored as input data.

[0494] Step 2: Sending and receiving data

[0495] The terminal sends the formatted data to the server via the internet. The server verifies this received data, converts it to the required format, and prepares it for matching against the database. Specifically, the terminal sends data to the server using an HTTP request.

[0496] Step 3: Matching the title

[0497] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. This comparison prepares the basic information necessary to satisfy the user's request. Inputs include regional information and ingredient types, and output is a list of matched ingredients.

[0498] Step 4: Analysis using a generative AI model

[0499] The server uses a generative AI model to analyze the matched data and derive the optimal combination of ingredients and seasonings based on the user's taste experience and health constraints. This process involves a large amount of data calculation, and specific ingredient / seasoning data is generated as output.

[0500] Step 5: Adjusting the recipe

[0501] The server adjusts the generated formula data based on the user's health information, particularly considering salt restrictions and allergy information. The specific output is a list of the adjusted ingredient formulations and seasonings.

[0502] Step 6: Generate and serve cooking instructions

[0503] The server generates specific cooking instructions based on the adjusted formula data. These instructions are sent to the terminal and provided to the user. The terminal acts as a guide, assisting with cooking visually or audibly. The generating AI model prepares user guidance using appropriate prompts to show the user exactly how to proceed with cooking.

[0504] (Application Example 1)

[0505] 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."

[0506] For today's busy consumers, easily preparing healthy meals while recreating past culinary experiences is difficult. Furthermore, purchasing the appropriate ingredients requires multiple methods and is time-consuming. In this situation, there is a need for a way to recreate past flavors while considering individual health conditions, and to quickly procure the necessary ingredients.

[0507] 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.

[0508] In this invention, the server includes means for collecting memory data related to the user's taste, means for integrating regionally specific taste data, information data on food ingredients, and information data on seasonings, means for generating formulation data for cooking using a machine learning algorithm, and means for adjusting the generated formulation data and purchasing ingredients directly via an electronic payment system. This enables the user to healthily recreate past taste experiences and purchase necessary ingredients quickly and smoothly.

[0509] A "user" is an individual who uses this system to recreate past taste experiences and prepare meals that are mindful of their health.

[0510] "Memory data" refers to information about a user's past taste experiences, including data on their preferred flavors and ingredients.

[0511] "Region-specific taste data" refers to information about the characteristics of taste and cooking styles in a specific geographical area.

[0512] "Food ingredient information data" refers to detailed information about ingredients, such as their type, nutritional value, and frequency of use.

[0513] "Seasoning information data" refers to data that includes information about the type of seasoning, its ingredients, and its flavor characteristics.

[0514] A "machine learning algorithm" is an artificial intelligence technology that analyzes data, identifies patterns and relationships, and then generates new insights and predictions.

[0515] "Formulation data" refers to information compiled about the appropriate combinations of ingredients and seasonings needed for cooking.

[0516] An "electronic payment system" is an online platform used to settle payments for goods and services via the internet.

[0517] This invention is a system that provides health-conscious meal recipes while taking into account the user's past taste experiences. The system consists of a user terminal, a server that processes data, and a program that coordinates them.

[0518] Users can access the system via smartphones or other computing devices and input data about their taste memories, health status, and dishes they wish to recreate. The user's device then formats this data and sends it to the server.

[0519] The server processes received data using Python and Flask. On the server, user input data is stored in a PostgreSQL database and compared against region-specific taste data, food ingredient data, and seasoning data. At this stage, by referring to the information stored in the database, basic information is obtained to recreate past taste experiences.

[0520] The server further analyzes the integrated data using machine learning algorithms to generate the optimal combination of ingredients and seasonings. Based on this, a generative AI model provides the optimal blend data. The generated blend data is then adjusted as needed based on the user's health information (e.g., salt restriction or allergy information).

[0521] The adjusted recipe is sent to the user's smartphone, where they can view it on their screen. Furthermore, they can purchase the necessary ingredients directly through an electronic payment system, utilizing existing online payment platforms such as the Stripe API. This seamless data processing and payment process allows users to easily obtain the necessary ingredients and recreate a healthy, nostalgic taste experience.

[0522] For example, if a user wants to recreate a classic soup from a long-established restaurant in a healthy way, they can input its characteristics, and the server will generate a corresponding recipe. The user can then immediately purchase the necessary ingredients within the app.

[0523] Examples of prompts to input into a generative AI model include the following:

[0524] "Based on the user's past taste experiences, generate health-conscious recipes and list the necessary ingredients."

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] Users access the application using their smartphones and input data about their taste preferences, health status, and the dishes they wish to recreate. The input data is formatted by the user interface and temporarily stored on the device. This process yields tangible, raw data based on the user's requests.

[0528] Step 2:

[0529] The terminal sends the formatted data to the server. The server analyzes the received data, converts it to JSON format, and prepares it for processing. The input data includes the user's desired taste characteristics and health status, and the output provides information for database searching.

[0530] Step 3:

[0531] The server compares region-specific taste data, food ingredient data, and seasoning data stored in the database with data received from the user. Here, the server uses SQL queries to extract relevant data and compare it with the input. The output of this process is a list of potential recipes that match the user's request.

[0532] Step 4:

[0533] The server uses machine learning algorithms to generate optimal ingredient and seasoning combinations based on the matched data. The generative AI model uses prompts to analyze recipe candidates and output combinations suitable for the input. The output is a list of optimized ingredients and seasonings.

[0534] Step 5:

[0535] The server adjusts the recipe based on the output combinations, taking into account the user's health information. It filters for allergy information and nutritional restrictions to generate a customized recipe. The output consists of health-conscious cooking instructions.

[0536] Step 6:

[0537] The server sends the final recipe and required ingredient information to the terminal, which displays the data on the user's screen. The user can then review the recipe and it becomes ready to use. Furthermore, an option is provided to purchase the necessary ingredients directly using an electronic payment system. The output consists of user-accessible display information.

[0538] 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.

[0539] This invention incorporates an emotion engine into a system that reproduces a user's past taste experiences and provides health-conscious recipes, thereby offering cooking suggestions tailored to the user's emotional state. The system is implemented by combining the user's terminal, a data processing server, and an emotion recognition engine.

[0540] Users access the system from a terminal with a dedicated interface, inputting information about taste memories, dishes they want to recreate, and their health status, as well as providing their own emotional data through an emotion sensor built into the terminal. The terminal then compiles this information and sends it to the server.

[0541] Upon receiving this data, the server compares it against a database containing regionally specific taste data, ingredient information, and seasoning information, while also taking into account emotional data analyzed by the emotion engine. The server integrates this information and uses an artificial intelligence model to generate the optimal combination of ingredients and seasonings. Furthermore, the emotion engine further customizes the recipe to match the user's emotional state based on the emotional data.

[0542] For example, if the emotion engine detects that the user is feeling down, the server might suggest adding fragrant spices to improve their mood. The recipes provided in this way not only recreate memories but also offer an experience best suited to the user's current emotional state.

[0543] The server ultimately sends the adjusted recipe to the device, which then presents it to the user. The user cooks based on the recipe displayed on the device, enjoying the taste of the past while also gaining an emotionally stimulating dining experience.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The user accesses the device and inputs data such as taste memory information, the dish they want to recreate, and their health status through a dedicated interface. The device also collects emotional data such as the user's facial expressions and voice tone.

[0547] Step 2:

[0548] The device formats the taste information entered by the user, health data, and emotional data collected through the emotion sensor, and then sends it to the server.

[0549] Step 3:

[0550] The server receives and stores data sent from the terminal. This includes data related to taste memories, health information, and emotional data generated using the emotion engine.

[0551] Step 4:

[0552] Based on the data received by the server, regionally specific taste data, ingredient information, and seasoning information are extracted from the database and integrated with emotional data.

[0553] Step 5:

[0554] The server uses an artificial intelligence model to analyze and integrate data, generating the optimal combination of ingredients and seasonings. The emotion engine adjusts the recipe based on emotional data. For example, if the user is stressed, it recommends ingredients with relaxing effects.

[0555] Step 6:

[0556] The server considers the generated combinations, adjusts recipes to suit the user's health condition, and optimizes the cooking procedure.

[0557] Step 7:

[0558] The server sends a refined and optimized recipe to the terminal, which then presents it to the user. The displayed recipe includes the ingredients used, their proportions, and cooking instructions. It also offers ingredient suggestions tailored to the user's mood.

[0559] Step 8:

[0560] Users cook according to the recipe displayed on their device. This allows for the recreation of past flavors, while also considering health and providing a dining experience tailored to their emotional state.

[0561] (Example 2)

[0562] 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."

[0563] Conventional cooking suggestion systems focused on recreating users' past taste experiences, but lacked recipe suggestions that took into account the user's health condition or temporary emotional state. As a result, users may gain satisfaction based on past memories, but struggle to obtain a dining experience optimized for their current physical and mental state.

[0564] 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.

[0565] In this invention, the server includes means for collecting the user's taste memories, means for integrating information on regionally specific tastes, ingredients, and seasonings, and means for adjusting the generated formula based on the user's health condition and further considering the user's emotional state. This makes it possible to reproduce the user's past taste experiences while providing an optimal recipe tailored to their health and emotional state.

[0566] "Users" refer to individuals or groups who utilize this system and are the entities that receive suggestions for recreating taste experiences and providing health-conscious recipes.

[0567] "Memory" refers to information about tastes that the user has experienced in the past, including personal memories of specific meals or dishes.

[0568] "Regionally specific tastes" refer to the taste profiles and eating habits that are generally recognized in a particular geographical area.

[0569] "Ingredients" refers to the basic building blocks or components of a dish, and generally includes both fresh and processed foods.

[0570] "Seasonings" are ingredients used to adjust the taste and aroma of food, and include spices, herbs, sauces, and other seasonings.

[0571] An "information processing device" refers to a computer system or electronic device that receives, analyzes, and integrates data to generate optimal output.

[0572] "Health status" refers to all health-related conditions, including the user's physical condition, pre-existing conditions, and nutrient needs, and is an important factor in optimizing recipes.

[0573] "Emotional state" refers to the user's mood and psychological state, which can influence their choice of dishes and suggestions for ingredients.

[0574] "Combination" refers to the combination and proportion of ingredients and seasonings, and is an important element that affects the final taste and quality of the dish.

[0575] "Cooking instructions" refer to the steps and processes for preparing a particular dish, and are a set of instructions provided to the user.

[0576] This invention aims to provide recipes based on a user's health and emotional state, while recreating their past taste experiences. The system operates by combining the user's terminal, a server responsible for data processing, and an engine for analyzing emotions.

[0577] Users access the system using a terminal with a dedicated interface. The terminal has the functionality to allow users to input data such as taste memory information, dishes they wish to recreate, and health status. Furthermore, the terminal is equipped with an emotion sensor, enabling it to acquire the user's emotional data in real time. This data is organized and integrated by the terminal and then transmitted to the server.

[0578] The server processes the received data. First, it compares the user's input information with a database containing information on regionally specific tastes, ingredients, and seasonings. Next, an emotion engine installed within the server analyzes the user's emotional data. The analysis results are integrated into the recipe generation process by a generative AI model.

[0579] The generative AI model integrates database matching results and emotion analysis results to derive an optimized combination of ingredients and seasonings for the user. This process considers the user's health condition and provides a taste experience that corresponds to their current emotional state.

[0580] As a concrete example, a prompt to a generative AI model might be used in the following form: "Please suggest a recipe suitable for a user who is feeling down." This model could then suggest specific spices or ingredients to alleviate feelings of sadness.

[0581] Finally, the server sends the adjusted recipe to the device. The device then displays the recipe, including specific cooking instructions, to the user, helping them to recreate past taste experiences while also considering their health and emotional state.

[0582] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0583] Step 1:

[0584] The device collects information from the user.

[0585] Specifically, the user inputs information about their past taste experiences, dishes they want to recreate, and their health status through the device's interface. For example, they might input "soup with lots of carrots and onions" or "I want to maintain my health with low-calorie meals." The device also uses an emotion sensor to measure the user's emotional data. The input taste and emotional data are then compiled on the device.

[0586] Step 2:

[0587] The terminal sends the organized data to the server.

[0588] The device organizes taste information, health-related requests, and emotional data collected from the user into a single data package. This data package is then transmitted to the server in digital format. Specifically, the output data package might contain information such as "Taste: Sweet, Ingredients: Carrots, Onions, Health Information: Low Calorie, Emotional State: Want to Relax."

[0589] Step 3:

[0590] The server analyzes the received data and compares it against the database.

[0591] When the server receives a data package, it first consults its internal database. It then performs a process of matching the entered data with regionally specific taste data, ingredient information, and seasoning information. As a result of this database matching, appropriate dish and ingredient options are listed. The output result is "Recommended ingredients: bay leaf, olive oil, related recipe: French soup."

[0592] Step 4:

[0593] The server uses an emotion engine to analyze the emotion data.

[0594] The server activates an emotion engine to analyze the user's emotional data. The data processing performed by the emotion engine includes analyzing the type and intensity of emotions. As a result of the analysis, suggestions such as "Ingredients effective for improving the user's mood: mint, honey" are output.

[0595] Step 5:

[0596] The server generates recipes using a generative AI model.

[0597] The server integrates the emotion analysis results and database matching results, and uses a generative AI model to generate the optimal recipe. This model suggests ingredients and seasonings according to specific emotional and health states. An example of a generated recipe would be "Relaxing Carrot and Onion Soup with Aromatic Steam."

[0598] Step 6:

[0599] The server sends the final generated recipe to the terminal.

[0600] The server sends the completed and generated recipe to the terminal in digital format. This allows the terminal to present the user with the latest recipe. The output will be "Soup Recipe Details: Ingredients, Instructions, Special Notes (Explanation of Emotional Improvement Effects)".

[0601] Step 7:

[0602] The device displays recipes customized for the user.

[0603] The terminal displays recipes received from the server to the user, providing detailed instructions for the necessary cooking steps. Specifically, the terminal displays the cooking steps step by step, along with information related to the recipe, such as emotional enhancement and health maintenance. The displayed result might be, "How to make carrot soup: Cut, sauté, and simmer vegetables; recommended for relaxation."

[0604] (Application Example 2)

[0605] 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."

[0606] Conventional technologies have been unable to effectively consider the user's emotional state or health condition when recreating their taste experience. In particular, there is no system in place where cooking robots automatically prepare meals using this information, making it difficult to optimize cooking for individual users. Therefore, providing a personalized dining experience that takes into account the user's health and emotions has been a challenge.

[0607] 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.

[0608] In this invention, the server includes means for collecting memory data related to the user's taste, means for adjusting the generated formula data based on the user's health information and emotional information, and means for providing the user with cooking procedures that correspond to their emotional state based on the adjusted formula data. This makes it possible for the cooking robot to automatically prepare a dish suitable for the user and provide a personalized dining experience that corresponds to the user's emotions and health state.

[0609] A "user" is a person who operates the system and is the subject of the dining experience.

[0610] "Taste-related memory data" refers to data that includes information about tastes that the user has experienced in the past.

[0611] "Region-specific taste data" refers to data that records the taste characteristics generally recognized in a particular region.

[0612] "Ingredient information data" refers to data that shows information such as attributes and nutritional value of ingredients used in cooking.

[0613] "Seasoning information data" refers to data that records information about seasonings used to flavor dishes.

[0614] Artificial intelligence is a technology that gives computers the ability to learn and make decisions like humans.

[0615] "Formulation data" refers to data that records the combinations of ingredients and seasonings necessary to make a specific dish.

[0616] "Health information" refers to data that is related to the user's physical health status.

[0617] "Emotional information" refers to data that indicates the user's current emotional state.

[0618] "Cooking instructions" refer to information that outlines the specific operations and steps required to prepare a particular dish.

[0619] A "robot" is a mechanical device that has the ability to perform specific tasks automatically.

[0620] A "server" is a central processing unit used for processing and storing data.

[0621] The system that realizes this invention exchanges data between the user and the robot to provide a personalized cooking experience.

[0622] The system primarily consists of a user terminal, a server for data processing, and a robot responsible for cooking. The user terminal has a dedicated interface and provides a means for inputting the user's taste memory data, health information, and emotional information. The terminal is also equipped with an emotion sensor that detects emotional information from facial expressions and voice.

[0623] The server receives and processes this data. The server is equipped with a database containing region-specific taste data, ingredient information, and seasoning information, which allows it to generate optimal recipes for users using an emotion engine and an AI model. The AI ​​model uses a generative AI model to suggest dishes based on the user's emotional state through prompt messages. These generated suggestions are then sent to the cooking robot.

[0624] The robot is equipped with the necessary hardware for cooking and automatically prepares ingredients and cooks based on recipes generated by an AI model. The robot uses the cooking process to create a meal tailored to the user's healthcare needs and emotional state.

[0625] For example, if an emotion sensor detects a user's emotional state (such as fatigue) after they return from work, the server will suggest a meal that helps reduce stress. For instance, a relaxing herbal tea or a highly nutritious meal might be prepared. An example of a prompt to the generative AI model would be: "The user has returned home from work and is tired. Please suggest a recipe that is ideal for relaxation and nutritional replenishment."

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

[0627] Step 1:

[0628] The user uses an interface installed on their device to input memory data about taste, their current emotional state, and health information. The input data is organized into structured data that reflects taste preferences and health status, and then sent from the device to the server.

[0629] Step 2:

[0630] The server receives data sent from the terminal and first uses an emotion recognition engine to analyze the user's emotional information. It converts the emotional information data, which is the input, into emotion parameters and outputs the user's current emotional state.

[0631] Step 3:

[0632] The server uses emotion parameters as prompts for the AI ​​model, generating an example prompt: "The user is tired. Please suggest a dish that will have a relaxing effect on this state." Next, the AI ​​model is executed based on this prompt to generate recipe data.

[0633] Step 4:

[0634] The server compares the generated recipe data with regionally specific taste data and ingredient information, and adjusts the recipes while taking into account the user's health information. It uses the generated recipe data and health status data as input, and outputs the adjusted recipe data.

[0635] Step 5:

[0636] The adjusted recipe data is sent from the server to the robot. The robot follows the recipe steps and automatically performs cooking operations such as weighing, heating, and mixing the necessary ingredients. After cooking is complete, the dish is served to the user.

[0637] Step 6:

[0638] The user then provides feedback through their device regarding the provided dish. The server stores this feedback data and uses it as training data for future recipe suggestions.

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

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

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

[0642] [Fourth Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

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

[0656] This invention constitutes a system that provides health-conscious meal recipes while reproducing the past taste experiences of individual users. The system is implemented as a user terminal, a server that processes data, and a program that coordinates them.

[0657] Users access the system from a terminal with a dedicated interface and input data about their taste memories, health status, and the dishes they wish to recreate. The terminal formats this data and sends it to the server.

[0658] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. The server integrates this information and analyzes it using an artificial intelligence model to generate the optimal combination of ingredients and seasonings.

[0659] The generated combinations are adjusted as needed, taking into account the user's health information (e.g., salt restrictions or allergy information). This adjustment process allows users to enjoy nostalgic flavors from the past without compromising their health.

[0660] Finally, the server generates a refined recipe, which includes specific ingredient proportions and cooking instructions, and sends it to the terminal. The terminal displays the received recipe to the user and provides guidance for cooking.

[0661] For example, if a user wants to recreate a ramen dish that takes health into consideration, they would input the characteristics of a ramen they previously enjoyed (e.g., soy sauce-based with less oil). The server would then analyze this information, combining it with a general recipe for that past soy sauce ramen and currently available ingredient information, to create a recipe for a healthier soup. In this way, users can safely enjoy their favorite flavors while maintaining their health.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The user accesses the terminal and inputs data about their taste preferences, desired dishes, and health status through a dedicated interface. The terminal organizes this information into a form.

[0665] Step 2:

[0666] The device collects data entered by the user and sends the formatted dataset to the server. This includes information about specific taste characteristics and health restrictions.

[0667] Step 3:

[0668] The server stores the data received from the terminal in a data storage for analysis and compares it with a database containing regionally specific taste data, ingredient information, and seasoning information.

[0669] Step 4:

[0670] Based on information extracted from the database, the server uses an artificial intelligence model to calculate the optimal combination of ingredients and seasonings to reproduce the characteristics of the flavor.

[0671] Step 5:

[0672] The server generates combinations that are compared with the user's health information, adjusts the ingredient data as needed, and creates health-conscious recipes. For example, if the user needs to reduce salt intake, alternative seasonings will be used.

[0673] Step 6:

[0674] The server sends the adjusted recipe to the terminal, which then displays it to the user. The displayed recipe includes detailed instructions for cooking and information on the necessary ingredients.

[0675] Step 7:

[0676] Users view the recipe displayed on their device and begin cooking according to the guidance. This allows them to enjoy familiar flavors while maintaining a healthy diet.

[0677] (Example 1)

[0678] 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".

[0679] There is a need to faithfully reproduce each user's past taste experiences while also suggesting healthy meals. However, conventional systems have been unable to fully utilize individual taste memories and health information, and have been unable to provide optimal combinations of ingredients and seasonings. As a result, users have faced the challenge of finding it difficult to balance taste satisfaction with maintaining good health.

[0680] 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.

[0681] In this invention, the server includes means for collecting taste-related information from users, means for comparing the collected information with region-specific taste information, ingredient information, and seasoning information, means for generating optimal blend data using a generative AI model, means for adjusting the blend data based on health restriction information, and means for providing cooking instructions based on the adjusted blend data. This makes it possible for individual users to enjoy healthy meals while reminiscing about their rich past taste experiences.

[0682] "Users" refers to individuals who use the system to recreate their own taste experiences and obtain health-conscious meals.

[0683] "Information related to taste" refers to all information, including data on the user's past taste experiences and preferences, as well as data on dishes they wish to recreate.

[0684] An "information processing device" refers to a computer system that performs processing such as data matching, analysis, and generation based on received information.

[0685] "Regional taste information" refers to data on common ingredients and flavor harmonies in each region.

[0686] "Food ingredient information" refers to information that includes data on the nutritional value, availability, and cooking methods of various food ingredients.

[0687] "Seasoning information" refers to data on various seasonings, including their properties, suitable dishes to combine them with, and usage methods.

[0688] A "generative AI model" refers to artificial intelligence technology that analyzes input data and generates optimal blending data.

[0689] "Formulation data" refers to the optimal combination of ingredients and seasonings for a particular dish.

[0690] "Health restriction information" refers to individual health-related data such as the user's health status, dietary restrictions, and allergy information.

[0691] "Cooking instructions" refers to information that details the steps for preparing a dish based on the generated and adjusted formula data.

[0692] This invention is a system that uses terminals, servers, and a generative AI model that connects them to reproduce past taste experiences for individual users while suggesting health-conscious meals.

[0693] Users access the system through a terminal and input information about their past favorite flavors and dishes they wish to recreate. This terminal runs dedicated application software, formats the user data, and sends it to the server. The terminal operates on a standard operating system and communicates with the server via an internet connection.

[0694] The server processes the received data and matches it against a database of regional taste information, ingredients, and seasonings. The server possesses powerful processing capabilities and uses a generative AI model to analyze the received data and generate optimal ingredient and seasoning combinations to recreate each user's past taste experiences. This generation process takes into account the user's health-related information, such as salt intake restrictions and allergy information.

[0695] The generated combination of ingredients and seasonings is further adjusted on the server side to optimize it so as not to have any adverse health effects. This process allows users to safely obtain an ideal taste experience that goes beyond their individual health constraints. The server sends the adjusted recipe back to the terminal, which then presents it to the user, providing specific cooking instructions and guidance.

[0696] As a concrete example, consider a case where a user wants to recreate a healthy ramen. If the user inputs information that they previously preferred soy sauce-based ramen with less oil, the server analyzes this information and suggests a healthier alternative recipe. The generating AI model selects ingredients from the available ingredient database to create a soup recipe that is low in salt while maintaining the characteristics of the original flavor.

[0697] An example of a prompt message is, "Can you suggest a recipe that recreates the taste of a low-oil soy sauce ramen I used to enjoy, while also being health-conscious?" In this way, users can enjoy the taste of their past memories while maintaining their health.

[0698] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0699] Step 1: Enter user data

[0700] The user accesses the terminal and inputs information about their past taste preferences, current health status, and the dishes they wish to recreate. This input data is formatted on the terminal and prepared for transmission to the server. Specifically, the user's selected taste characteristics and health constraints (e.g., low-sodium) are stored as input data.

[0701] Step 2: Sending and receiving data

[0702] The terminal sends the formatted data to the server via the internet. The server verifies this received data, converts it to the required format, and prepares it for matching against the database. Specifically, the terminal sends data to the server using an HTTP request.

[0703] Step 3: Matching the title

[0704] The server compares the received data with a database containing regionally specific taste information, ingredient information, and seasoning information. This comparison prepares the basic information necessary to satisfy the user's request. Inputs include regional information and ingredient types, and output is a list of matched ingredients.

[0705] Step 4: Analysis using a generative AI model

[0706] The server uses a generative AI model to analyze the matched data and derive the optimal combination of ingredients and seasonings based on the user's taste experience and health constraints. This process involves a large amount of data calculation, and specific ingredient / seasoning data is generated as output.

[0707] Step 5: Adjusting the recipe

[0708] The server adjusts the generated formula data based on the user's health information, particularly considering salt restrictions and allergy information. The specific output is a list of the adjusted ingredient formulations and seasonings.

[0709] Step 6: Generate and serve cooking instructions

[0710] The server generates specific cooking instructions based on the adjusted formula data. These instructions are sent to the terminal and provided to the user. The terminal acts as a guide, assisting with cooking visually or audibly. The generating AI model prepares user guidance using appropriate prompts to show the user exactly how to proceed with cooking.

[0711] (Application Example 1)

[0712] 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".

[0713] For today's busy consumers, easily preparing healthy meals while recreating past culinary experiences is difficult. Furthermore, purchasing the appropriate ingredients requires multiple methods and is time-consuming. In this situation, there is a need for a way to recreate past flavors while considering individual health conditions, and to quickly procure the necessary ingredients.

[0714] 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.

[0715] In this invention, the server includes means for collecting memory data related to the user's taste, means for integrating regionally specific taste data, information data on food ingredients, and information data on seasonings, means for generating formulation data for cooking using a machine learning algorithm, and means for adjusting the generated formulation data and purchasing ingredients directly via an electronic payment system. This enables the user to healthily recreate past taste experiences and purchase necessary ingredients quickly and smoothly.

[0716] A "user" is an individual who uses this system to recreate past taste experiences and prepare meals that are mindful of their health.

[0717] "Memory data" refers to information about a user's past taste experiences, including data on their preferred flavors and ingredients.

[0718] "Region-specific taste data" refers to information about the characteristics of taste and cooking styles in a specific geographical area.

[0719] "Food ingredient information data" refers to detailed information about ingredients, such as their type, nutritional value, and frequency of use.

[0720] "Seasoning information data" refers to data that includes information about the type of seasoning, its ingredients, and its flavor characteristics.

[0721] A "machine learning algorithm" is an artificial intelligence technology that analyzes data, identifies patterns and relationships, and then generates new insights and predictions.

[0722] "Formulation data" refers to information compiled about the appropriate combinations of ingredients and seasonings needed for cooking.

[0723] An "electronic payment system" is an online platform used to settle payments for goods and services via the internet.

[0724] This invention is a system that provides health-conscious meal recipes while taking into account the user's past taste experiences. The system consists of a user terminal, a server that processes data, and a program that coordinates them.

[0725] Users can access the system via smartphones or other computing devices and input data about their taste memories, health status, and dishes they wish to recreate. The user's device then formats this data and sends it to the server.

[0726] The server processes received data using Python and Flask. On the server, user input data is stored in a PostgreSQL database and compared against region-specific taste data, food ingredient data, and seasoning data. At this stage, by referring to the information stored in the database, basic information is obtained to recreate past taste experiences.

[0727] The server further analyzes the integrated data using machine learning algorithms to generate the optimal combination of ingredients and seasonings. Based on this, a generative AI model provides the optimal blend data. The generated blend data is then adjusted as needed based on the user's health information (e.g., salt restriction or allergy information).

[0728] The adjusted recipe is sent to the user's smartphone, where they can view it on their screen. Furthermore, they can purchase the necessary ingredients directly through an electronic payment system, utilizing existing online payment platforms such as the Stripe API. This seamless data processing and payment process allows users to easily obtain the necessary ingredients and recreate a healthy, nostalgic taste experience.

[0729] For example, if a user wants to recreate a classic soup from a long-established restaurant in a healthy way, they can input its characteristics, and the server will generate a corresponding recipe. The user can then immediately purchase the necessary ingredients within the app.

[0730] Examples of prompts to input into a generative AI model include the following:

[0731] "Based on the user's past taste experiences, generate health-conscious recipes and list the necessary ingredients."

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] Users access the application using their smartphones and input data about their taste preferences, health status, and the dishes they wish to recreate. The input data is formatted by the user interface and temporarily stored on the device. This process yields tangible, raw data based on the user's requests.

[0735] Step 2:

[0736] The terminal sends the formatted data to the server. The server analyzes the received data, converts it to JSON format, and prepares it for processing. The input data includes the user's desired taste characteristics and health status, and the output provides information for database searching.

[0737] Step 3:

[0738] The server compares region-specific taste data, food ingredient data, and seasoning data stored in the database with data received from the user. Here, the server uses SQL queries to extract relevant data and compare it with the input. The output of this process is a list of potential recipes that match the user's request.

[0739] Step 4:

[0740] The server uses machine learning algorithms to generate optimal ingredient and seasoning combinations based on the matched data. The generative AI model uses prompts to analyze recipe candidates and output combinations suitable for the input. The output is a list of optimized ingredients and seasonings.

[0741] Step 5:

[0742] The server adjusts the recipe based on the output combinations, taking into account the user's health information. It filters for allergy information and nutritional restrictions to generate a customized recipe. The output consists of health-conscious cooking instructions.

[0743] Step 6:

[0744] The server sends the final recipe and required ingredient information to the terminal, which displays the data on the user's screen. The user can then review the recipe and it becomes ready to use. Furthermore, an option is provided to purchase the necessary ingredients directly using an electronic payment system. The output consists of user-accessible display information.

[0745] 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.

[0746] This invention incorporates an emotion engine into a system that reproduces a user's past taste experiences and provides health-conscious recipes, thereby offering cooking suggestions tailored to the user's emotional state. The system is implemented by combining the user's terminal, a data processing server, and an emotion recognition engine.

[0747] Users access the system from a terminal with a dedicated interface, inputting information about taste memories, dishes they want to recreate, and their health status, as well as providing their own emotional data through an emotion sensor built into the terminal. The terminal then compiles this information and sends it to the server.

[0748] Upon receiving this data, the server compares it against a database containing regionally specific taste data, ingredient information, and seasoning information, while also taking into account emotional data analyzed by the emotion engine. The server integrates this information and uses an artificial intelligence model to generate the optimal combination of ingredients and seasonings. Furthermore, the emotion engine further customizes the recipe to match the user's emotional state based on the emotional data.

[0749] For example, if the emotion engine detects that the user is feeling down, the server might suggest adding fragrant spices to improve their mood. The recipes provided in this way not only recreate memories but also offer an experience best suited to the user's current emotional state.

[0750] The server ultimately sends the adjusted recipe to the device, which then presents it to the user. The user cooks based on the recipe displayed on the device, enjoying the taste of the past while also gaining an emotionally stimulating dining experience.

[0751] The following describes the processing flow.

[0752] Step 1:

[0753] The user accesses the device and inputs data such as taste memory information, the dish they want to recreate, and their health status through a dedicated interface. The device also collects emotional data such as the user's facial expressions and voice tone.

[0754] Step 2:

[0755] The device formats the taste information entered by the user, health data, and emotional data collected through the emotion sensor, and then sends it to the server.

[0756] Step 3:

[0757] The server receives and stores data sent from the terminal. This includes data related to taste memories, health information, and emotional data generated using the emotion engine.

[0758] Step 4:

[0759] Based on the data received by the server, regionally specific taste data, ingredient information, and seasoning information are extracted from the database and integrated with emotional data.

[0760] Step 5:

[0761] The server uses an artificial intelligence model to analyze and integrate data, generating the optimal combination of ingredients and seasonings. The emotion engine adjusts the recipe based on emotional data. For example, if the user is stressed, it recommends ingredients with relaxing effects.

[0762] Step 6:

[0763] The server considers the generated combinations, adjusts recipes to suit the user's health condition, and optimizes the cooking procedure.

[0764] Step 7:

[0765] The server sends a refined and optimized recipe to the terminal, which then presents it to the user. The displayed recipe includes the ingredients used, their proportions, and cooking instructions. It also offers ingredient suggestions tailored to the user's mood.

[0766] Step 8:

[0767] Users cook according to the recipe displayed on their device. This allows for the recreation of past flavors, while also considering health and providing a dining experience tailored to their emotional state.

[0768] (Example 2)

[0769] 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".

[0770] Conventional cooking suggestion systems focused on recreating users' past taste experiences, but lacked recipe suggestions that took into account the user's health condition or temporary emotional state. As a result, users may gain satisfaction based on past memories, but struggle to obtain a dining experience optimized for their current physical and mental state.

[0771] 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.

[0772] In this invention, the server includes means for collecting the user's taste memories, means for integrating information on regionally specific tastes, ingredients, and seasonings, and means for adjusting the generated formula based on the user's health condition and further considering the user's emotional state. This makes it possible to reproduce the user's past taste experiences while providing an optimal recipe tailored to their health and emotional state.

[0773] "Users" refer to individuals or groups who utilize this system and are the entities that receive suggestions for recreating taste experiences and providing health-conscious recipes.

[0774] "Memory" refers to information about tastes that the user has experienced in the past, including personal memories of specific meals or dishes.

[0775] "Regionally specific tastes" refer to the taste profiles and eating habits that are generally recognized in a particular geographical area.

[0776] "Ingredients" refers to the basic building blocks or components of a dish, and generally includes both fresh and processed foods.

[0777] "Seasonings" are ingredients used to adjust the taste and aroma of food, and include spices, herbs, sauces, and other seasonings.

[0778] An "information processing device" refers to a computer system or electronic device that receives, analyzes, and integrates data to generate optimal output.

[0779] "Health status" refers to all health-related conditions, including the user's physical condition, pre-existing conditions, and nutrient needs, and is an important factor in optimizing recipes.

[0780] "Emotional state" refers to the user's mood and psychological state, which can influence their choice of dishes and suggestions for ingredients.

[0781] "Combination" refers to the combination and proportion of ingredients and seasonings, and is an important element that affects the final taste and quality of the dish.

[0782] "Cooking instructions" refer to the steps and processes for preparing a particular dish, and are a set of instructions provided to the user.

[0783] This invention aims to provide recipes based on a user's health and emotional state, while recreating their past taste experiences. The system operates by combining the user's terminal, a server responsible for data processing, and an engine for analyzing emotions.

[0784] Users access the system using a terminal with a dedicated interface. The terminal has the functionality to allow users to input data such as taste memory information, dishes they wish to recreate, and health status. Furthermore, the terminal is equipped with an emotion sensor, enabling it to acquire the user's emotional data in real time. This data is organized and integrated by the terminal and then transmitted to the server.

[0785] The server processes the received data. First, it compares the user's input information with a database containing information on regionally specific tastes, ingredients, and seasonings. Next, an emotion engine installed within the server analyzes the user's emotional data. The analysis results are integrated into the recipe generation process by a generative AI model.

[0786] The generative AI model integrates database matching results and emotion analysis results to derive an optimized combination of ingredients and seasonings for the user. This process considers the user's health condition and provides a taste experience that corresponds to their current emotional state.

[0787] As a concrete example, a prompt to a generative AI model might be used in the following form: "Please suggest a recipe suitable for a user who is feeling down." This model could then suggest specific spices or ingredients to alleviate feelings of sadness.

[0788] Finally, the server sends the adjusted recipe to the device. The device then displays the recipe, including specific cooking instructions, to the user, helping them to recreate past taste experiences while also considering their health and emotional state.

[0789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0790] Step 1:

[0791] The device collects information from the user.

[0792] Specifically, the user inputs information about their past taste experiences, dishes they want to recreate, and their health status through the device's interface. For example, they might input "soup with lots of carrots and onions" or "I want to maintain my health with low-calorie meals." The device also uses an emotion sensor to measure the user's emotional data. The input taste and emotional data are then compiled on the device.

[0793] Step 2:

[0794] The terminal sends the organized data to the server.

[0795] The device organizes taste information, health-related requests, and emotional data collected from the user into a single data package. This data package is then transmitted to the server in digital format. Specifically, the output data package might contain information such as "Taste: Sweet, Ingredients: Carrots, Onions, Health Information: Low Calorie, Emotional State: Want to Relax."

[0796] Step 3:

[0797] The server analyzes the received data and compares it against the database.

[0798] When the server receives a data package, it first consults its internal database. It then performs a process of matching the entered data with regionally specific taste data, ingredient information, and seasoning information. As a result of this database matching, appropriate dish and ingredient options are listed. The output result is "Recommended ingredients: bay leaf, olive oil, related recipe: French soup."

[0799] Step 4:

[0800] The server uses an emotion engine to analyze the emotion data.

[0801] The server activates an emotion engine to analyze the user's emotional data. The data processing performed by the emotion engine includes analyzing the type and intensity of emotions. As a result of the analysis, suggestions such as "Ingredients effective for improving the user's mood: mint, honey" are output.

[0802] Step 5:

[0803] The server generates recipes using a generative AI model.

[0804] The server integrates the emotion analysis results and database matching results, and uses a generative AI model to generate the optimal recipe. This model suggests ingredients and seasonings according to specific emotional and health states. An example of a generated recipe would be "Relaxing Carrot and Onion Soup with Aromatic Steam."

[0805] Step 6:

[0806] The server sends the final generated recipe to the terminal.

[0807] The server sends the completed and generated recipe to the terminal in digital format. This allows the terminal to present the user with the latest recipe. The output will be "Soup Recipe Details: Ingredients, Instructions, Special Notes (Explanation of Emotional Improvement Effects)".

[0808] Step 7:

[0809] The device displays recipes customized for the user.

[0810] The terminal displays recipes received from the server to the user, providing detailed instructions for the necessary cooking steps. Specifically, the terminal displays the cooking steps step by step, along with information related to the recipe, such as emotional enhancement and health maintenance. The displayed result might be, "How to make carrot soup: Cut, sauté, and simmer vegetables; recommended for relaxation."

[0811] (Application Example 2)

[0812] 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".

[0813] Conventional technologies have been unable to effectively consider the user's emotional state or health condition when recreating their taste experience. In particular, there is no system in place where cooking robots automatically prepare meals using this information, making it difficult to optimize cooking for individual users. Therefore, providing a personalized dining experience that takes into account the user's health and emotions has been a challenge.

[0814] 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.

[0815] In this invention, the server includes means for collecting memory data related to the user's taste, means for adjusting the generated formula data based on the user's health information and emotional information, and means for providing the user with cooking procedures that correspond to their emotional state based on the adjusted formula data. This makes it possible for the cooking robot to automatically prepare a dish suitable for the user and provide a personalized dining experience that corresponds to the user's emotions and health state.

[0816] A "user" is a person who operates the system and is the subject of the dining experience.

[0817] "Taste-related memory data" refers to data that includes information about tastes that the user has experienced in the past.

[0818] "Region-specific taste data" refers to data that records the taste characteristics generally recognized in a particular region.

[0819] "Ingredient information data" refers to data that shows information such as attributes and nutritional value of ingredients used in cooking.

[0820] "Seasoning information data" refers to data that records information about seasonings used to flavor dishes.

[0821] Artificial intelligence is a technology that gives computers the ability to learn and make decisions like humans.

[0822] "Formulation data" refers to data that records the combinations of ingredients and seasonings necessary to make a specific dish.

[0823] "Health information" refers to data that is related to the user's physical health status.

[0824] "Emotional information" refers to data that indicates the user's current emotional state.

[0825] "Cooking instructions" refer to information that outlines the specific operations and steps required to prepare a particular dish.

[0826] A "robot" is a mechanical device that has the ability to perform specific tasks automatically.

[0827] A "server" is a central processing unit used for processing and storing data.

[0828] The system that realizes this invention exchanges data between the user and the robot to provide a personalized cooking experience.

[0829] The system primarily consists of a user terminal, a server for data processing, and a robot responsible for cooking. The user terminal has a dedicated interface and provides a means for inputting the user's taste memory data, health information, and emotional information. The terminal is also equipped with an emotion sensor that detects emotional information from facial expressions and voice.

[0830] The server receives and processes this data. The server is equipped with a database containing region-specific taste data, ingredient information, and seasoning information, which allows it to generate optimal recipes for users using an emotion engine and an AI model. The AI ​​model uses a generative AI model to suggest dishes based on the user's emotional state through prompt messages. These generated suggestions are then sent to the cooking robot.

[0831] The robot is equipped with the necessary hardware for cooking and automatically prepares ingredients and cooks based on recipes generated by an AI model. The robot uses the cooking process to create a meal tailored to the user's healthcare needs and emotional state.

[0832] For example, if an emotion sensor detects a user's emotional state (such as fatigue) after they return from work, the server will suggest a meal that helps reduce stress. For instance, a relaxing herbal tea or a highly nutritious meal might be prepared. An example of a prompt to the generative AI model would be: "The user has returned home from work and is tired. Please suggest a recipe that is ideal for relaxation and nutritional replenishment."

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

[0834] Step 1:

[0835] The user uses an interface installed on their device to input memory data about taste, their current emotional state, and health information. The input data is organized into structured data that reflects taste preferences and health status, and then sent from the device to the server.

[0836] Step 2:

[0837] The server receives data sent from the terminal and first uses an emotion recognition engine to analyze the user's emotional information. It converts the emotional information data, which is the input, into emotion parameters and outputs the user's current emotional state.

[0838] Step 3:

[0839] The server uses emotion parameters as prompts for the AI ​​model, generating an example prompt: "The user is tired. Please suggest a dish that will have a relaxing effect on this state." Next, the AI ​​model is executed based on this prompt to generate recipe data.

[0840] Step 4:

[0841] The server compares the generated recipe data with regionally specific taste data and ingredient information, and adjusts the recipes while taking into account the user's health information. It uses the generated recipe data and health status data as input, and outputs the adjusted recipe data.

[0842] Step 5:

[0843] The adjusted recipe data is sent from the server to the robot. The robot follows the recipe steps and automatically performs cooking operations such as weighing, heating, and mixing the necessary ingredients. After cooking is complete, the dish is served to the user.

[0844] Step 6:

[0845] The user then provides feedback through their device regarding the provided dish. The server stores this feedback data and uses it as training data for future recipe suggestions.

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

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

[0848] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

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

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

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

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

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

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

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

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

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

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

[0859] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0868] (Claim 1)

[0869] A means of collecting memory data about users' tastes,

[0870] Based on the aforementioned memory data, a means for integrating regionally specific taste data, ingredient information data, and seasoning information data,

[0871] A means for generating cooking ingredient data using artificial intelligence based on the aforementioned integrated data,

[0872] A means of adjusting the generated formulation data based on the user's health information,

[0873] A means for providing users with cooking procedures based on the adjusted formulation data,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein a user inputs information about the taste, which is then transmitted to a server, and the server receives the input information.

[0877] (Claim 3)

[0878] The system according to claim 1, which optimizes the cooking procedure based on formulation data generated by artificial intelligence, taking into account the user's health information.

[0879] "Example 1"

[0880] (Claim 1)

[0881] A means of collecting information related to the user's taste,

[0882] Means for formatting the aforementioned information and transmitting it to a processing device,

[0883] The processing device includes means for comparing the information received with region-specific taste information, ingredient information, and seasoning information.

[0884] A means for generating formula data for cooking using a generation AI model based on the aforementioned matched data,

[0885] The generated formulation data is provided as a means for adjusting it based on the user's health restriction information,

[0886] A means for providing users with cooking procedures based on the adjusted formulation data,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein a user transmits information about taste to an information processing device, and the information processing device receives the information.

[0890] (Claim 3)

[0891] The system according to claim 1, which optimizes the cooking procedure based on the formulation data generated by the generation AI model, taking into account the user's health restriction information.

[0892] "Application Example 1"

[0893] (Claim 1)

[0894] A means of collecting memory data about users' tastes,

[0895] A means for integrating regionally specific taste data, food ingredient information data, and seasoning information data based on the aforementioned memory data,

[0896] A means for generating cooking ingredient data using a machine learning algorithm based on the aforementioned integrated data,

[0897] A means of adjusting the generated formulation data based on the user's health information,

[0898] The system provides users with cooking instructions based on the adjusted formulation data, and a means of directly purchasing ingredients via an electronic payment system.

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein information regarding taste entered by a user is transmitted to a central device, and the central device receives the input information.

[0902] (Claim 3)

[0903] The system according to claim 1, which optimizes the cooking procedure based on formulation data generated by artificial intelligence, taking into account the user's health information, and performs the purchase procedure through an electronic payment system.

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

[0905] (Claim 1)

[0906] A means of collecting users' memories about taste,

[0907] Based on the aforementioned memory, a means for integrating information on regionally specific tastes, ingredients, and seasonings,

[0908] A means for generating a cooking formula using an information processing device based on the aforementioned integrated information,

[0909] A means for adjusting the generated formula based on the user's health condition and further considering the user's emotional state,

[0910] Means for providing users with a cooking method based on the adjusted formulation,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the user inputs information about taste and information about emotions, which are transmitted to a data processing device, and the data processing device receives the input information.

[0914] (Claim 3)

[0915] The system according to claim 1, which optimizes the cooking method based on the formulation generated by the information processing device, taking into account the user's health condition and emotional state.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] A means of collecting memory data about users' tastes,

[0919] Based on the aforementioned memory data, a means for integrating regionally specific taste data, ingredient information data, and seasoning information data,

[0920] A means for generating cooking ingredient data using artificial intelligence based on the aforementioned integrated data,

[0921] A means for adjusting the generated formulation data based on the user's health information and emotional information,

[0922] A means for providing users with cooking procedures that correspond to their emotional state, based on the adjusted formulation data mentioned above.

[0923] A means for automatically cooking food by having a robot perform the aforementioned cooking procedure,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, wherein the user inputs information about taste and information about emotions, which are transmitted to a server, and the server receives the input information.

[0927] (Claim 3)

[0928] The system according to claim 1, which optimizes the cooking procedure based on formulation data generated by artificial intelligence, taking into account the user's health information and emotional information, and has a robot perform the cooking based on that procedure. [Explanation of Symbols]

[0929] 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 of collecting memory data about users' tastes, A means for integrating regionally specific taste data, food ingredient information data, and seasoning information data based on the aforementioned memory data, A means for generating cooking ingredient data using a machine learning algorithm based on the aforementioned integrated data, A means of adjusting the generated formulation data based on the user's health information, The system provides users with cooking instructions based on the adjusted formulation data, and a means of directly purchasing ingredients via an electronic payment system. A system that includes this.

2. The system according to claim 1, wherein information regarding taste entered by a user is transmitted to a central device, and the central device receives the input information.

3. The system according to claim 1, which optimizes the cooking procedure based on formulation data generated by artificial intelligence, taking into account the user's health information, and performs the purchase procedure through an electronic payment system.

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