system

Abstract

We provide the system. [Solution] An interface means for inputting user information, A means of receiving user input data and searching for recipes that match the criteria from a cooking database, An algorithmic means for scoring the searched recipes and selecting the optimal recipe, A means of presenting selected recipes to users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As a problem of cooking that many people face daily, there is a difficulty in finding an appropriate recipe considering one's own taste preferences, budget, and health condition. In particular, when trying to cook dishes from different countries or when following specific nutritional restrictions, it has become a problem to efficiently obtain such information. Also, searching for and customizing recipes manually requires time and effort and can be a burden for general consumers. The purpose of this invention is to provide a means for easily providing an optimal recipe based on the input information of the user in order to address these complex and individualized needs.

Means for Solving the Problems

[0005] This invention provides an interface for inputting user information, receives user input data, and searches a cooking database for recipes that match the specified criteria. Furthermore, it utilizes an algorithm to score the retrieved recipes and select the optimal recipe, thereby enabling the provision of the most suitable recipe to the user. It also incorporates a function to easily improve the cooking database and algorithm by incorporating user feedback. This allows for recipe suggestions tailored to the user's preferences, health needs, and budget, while also improving the cooking experience.

[0006] "User information" refers to individual data that users enter into the system, such as ingredients, budget, taste preferences, allergy information, and health goals.

[0007] "Interface means" refers to a digital or physical mechanism that provides a user interface for users to input information.

[0008] A "culinary database" is a collection of data containing recipe information for various dishes, and it holds detailed information about ingredients, nutrition, and cooking methods.

[0009] "Means of searching for recipes" refers to a mechanism or method for analyzing user input data and extracting recipes that match the specified criteria from a cooking database.

[0010] "Scoring" refers to the process of assigning an evaluation value to acquired recipes based on multiple criteria such as nutritional value, taste preference, and budget constraints.

[0011] "Algorithmic methods" refer to calculation methods and procedures used to select recipes, and in particular, to mechanisms that perform complex decisions using AI.

[0012] "Feedback" refers to opinions and evaluations provided by users after use, and the data collected for system improvement. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

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

[0016] 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), and the like.

[0017] 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.

[0018] 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, and the like.

[0019] 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).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

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

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0034] This invention is a system for providing cooking recipes tailored to the user's preferences and needs, and it recommends the optimal recipe based on user input information using various cooking data. The system's operation and specific examples are shown below.

[0035] First, the user uses the terminal's user interface to input information such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal collects this information, converts it to an appropriate data format, and sends it to the server.

[0036] Based on the received user information, the server initiates access to the cooking database. This database contains recipes for various dishes, including ingredients, cooking methods, and nutritional information. The server queries the database according to the user's request and retrieves a list of recipes that match the criteria.

[0037] Next, the server runs an algorithm to score the retrieved recipes. This algorithm utilizes AI to assign evaluation values ​​to recipes, taking into account factors such as nutritional balance, user preferences, and budget constraints. It then selects the recipe with the highest evaluation and generates several recommended recipes.

[0038] The server then sends the details of the recommended recipe to the terminal. The terminal displays this to the user, allowing them to view the ingredient list, cooking instructions, nutritional information, and more. This makes it easy for the user to select and cook a meal that suits their needs.

[0039] For example, suppose a user specifies "a low-calorie Italian dish that can be made in under 30 minutes with a budget of 1000 yen." In this case, the server searches its Italian recipe database for relevant recipes and presents the user with a recipe that meets the criteria. The user can also send feedback from their device after cooking, which the server can then use to update the database and improve the algorithm.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters information about their desired dish through the terminal's user interface, such as the ingredients they want to use, their budget, taste preferences, allergy information, and health goals.

[0043] Step 2:

[0044] The terminal receives user input data, converts it to the required data format (e.g., JSON format), and prepares it for transmission to the system.

[0045] Step 3:

[0046] The terminal sends the converted data to the server. The server then receives the information necessary for processing.

[0047] Step 4:

[0048] The server analyzes the received data and establishes a connection with the recipe database. Based on the analysis results, it identifies the categories of dishes and constraints that match the user's criteria.

[0049] Step 5:

[0050] Based on the specified conditions, the server queries the cooking database for suitable recipe candidates and retrieves the necessary detailed information (ingredients, cooking method, nutritional information, etc.).

[0051] Step 6:

[0052] The server runs an AI-powered algorithm to score the retrieved recipe candidates based on factors such as nutritional balance, user preference, and budget.

[0053] Step 7:

[0054] The server selects the optimal recipe based on the scoring results. It then organizes the details of the selected recipe and processes it into a format for providing to the user.

[0055] Step 8:

[0056] The server sends the selected recipe information to the terminal as a result of the processing.

[0057] Step 9:

[0058] The terminal receives recipe information from the server and displays the contents (list of ingredients, cooking instructions, nutritional information, etc.) to the user.

[0059] Step 10:

[0060] Users prepare dishes based on the provided recipes and enjoy the cooking experience. Afterward, they can send feedback from their device as needed.

[0061] (Example 1)

[0062] 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."

[0063] The challenge lies in providing users with the most relevant information quickly and accurately, and in continuously improving the accuracy of information retrieval and suggestions by incorporating user feedback. Conventional systems struggle to find appropriate information tailored to individual user needs, and lack effective methods for utilizing feedback.

[0064] 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.

[0065] In this invention, the server includes means for searching an information storage device based on data received from the user and obtaining information that matches the specified criteria; means for scoring the obtained information using an AI algorithm and selecting the optimal information; and means for receiving feedback from the user and improving the information storage device and the AI ​​algorithm. This enables the provision of information optimized to the individual needs of the user and continuous improvement of the system.

[0066] "User information" refers to data that details individual conditions and requests that users provide to the system, such as ingredients, budget, taste preferences, allergy information, and health goals.

[0067] A "terminal" is a computer device used by users to input information and exchange data with a server, and includes devices such as smartphones and personal computers.

[0068] An "interface means" is a function that provides a user interface for users to input information and enables data exchange between the user and the terminal.

[0069] A "server" is a remote computer system that performs various computational processes, such as processing received data, searching information storage devices, and scoring using AI algorithms.

[0070] An "information storage device" is a database or storage device for storing a wide variety of information, and is a data storage facility where recipes and related information are stored.

[0071] An "AI algorithm" is a program that uses machine learning techniques to perform advanced computational processing based on input information and derive the optimal result.

[0072] "Scoring" is the process of evaluating and quantifying data and information based on specific criteria, and is used to measure the suitability of recipes and suggestions.

[0073] "Feedback" refers to opinions and evaluations based on actual user experiences, and is data used to improve the system.

[0074] This invention is a system for providing information tailored to the individual needs of users. The system mainly consists of a terminal, a server, an information storage device (database), and an AI algorithm. The user first accesses an information processing interface using the terminal. The terminal receives input data from the user, such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal also formats this data appropriately and sends it to the server.

[0075] The server searches its information storage device for information that matches the specified criteria based on the data received from the user. This information storage device functions as a database and stores various types of information. The server then scores the retrieved information using an AI algorithm. This scoring process evaluates and quantifies how well the information matches the user's needs. The AI ​​algorithm can be written in a programming language such as Python and includes a machine learning model. This model takes into account factors such as nutritional balance, user preferences, and budget constraints.

[0076] The server selects the most relevant information based on the scoring results and sends the details to the terminal. This information includes specific recipes and product purchase information. The terminal displays the information to the user, helping them make the most appropriate choice. Furthermore, the user can provide feedback, which is received by the server and used to improve the information storage and algorithms.

[0077] For example, when a user is looking for "low-calorie Italian food that can be made in under 30 minutes for a budget of 1000 yen," the prompt entered into the server would be "Suggest low-calorie Italian food that can be made in under 30 minutes." As a result, the server would select recipes that match the criteria and present them to the user. Through this series of processes, the system can provide optimal information tailored to the user's needs.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user inputs information through the terminal's interface. This information includes ingredients used, budget, taste preferences, allergy information, and health goals. This data is received by the terminal. The terminal converts this user data into a specific format (e.g., JSON, XML) and formats it in a way that is easily interpretable by the server. The output of this step is the formatted data.

[0081] Step 2:

[0082] The terminal sends formatted user data to the server. This transmission utilizes an internet connection and a protocol (e.g., HTTP, HTTPS). Specifically, the terminal sends data to the server via an API. The input to this step is the formatted user data, and the output is the completion of the data transfer to the server.

[0083] Step 3:

[0084] The server queries the information storage device based on the user data it receives. It uses SQL or NoSQL queries to search for recipes and information that match the criteria. The server uses complex search expressions to retrieve data that matches the criteria. The input for this step is user data, and the output is a list of data that matches the criteria.

[0085] Step 4:

[0086] The server uses an AI algorithm to score the acquired information. The scoring process uses a machine learning model to consider factors such as nutritional value, user preferences, and budget, assigning an evaluation value to each recipe and piece of information. This data processing is performed using a specific programming language (e.g., Python). The input for this step is the acquired information, and the output is a list of information with assigned evaluation values.

[0087] Step 5:

[0088] The server selects the most highly rated information based on the scoring results and sends the details of that information to the terminal. This information includes the ingredients, cooking method, and nutritional information of the selected recipe. Specifically, the server uses an API to format the selected information and send it back to the terminal. The input for this step is the scoring results, and the output is the optimal information sent to the terminal.

[0089] Step 6:

[0090] The terminal displays information received from the server to the user. The user can then make necessary selections based on this displayed information. Specifically, the information is presented visually on the terminal screen for the user to read. The input for this step is information from the server, and the output is the display of information to the user.

[0091] Step 7:

[0092] Users can submit feedback from their terminal based on the information provided. This feedback is then sent back to the server and used to improve the system. The terminal formats the feedback data entered by the user and relays it to the server. The input in this step is the user's feedback, and the output is the completion of the transmission of the feedback data to the server.

[0093] (Application Example 1)

[0094] 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."

[0095] In today's lifestyle, consumers demand efficient and customized meal choices, while simultaneously facing challenges in effectively selecting, ordering, and procuring food within time and budget constraints. Therefore, there is a need for a means to provide appropriate and deliverable meal options based on individual user preferences and circumstances.

[0096] 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.

[0097] In this invention, the server includes an interface means for inputting user information, means for receiving user input data and searching for meals that match the criteria from a data storage medium, algorithm means for scoring the searched information and selecting the optimal meal, means for presenting the selected meal to the user, and means for presenting delivery options from the searched meal information. This makes it possible to effectively and efficiently present the user with the meal best suited to their individual criteria and its delivery options.

[0098] "An interface means for inputting user information" refers to an input device or application used by a user to provide their preferences and conditions.

[0099] "A means of receiving user input data and searching for meals that match the conditions from a data storage medium" refers to a function that searches stored meal information based on information received from the user and extracts options that are suitable for the conditions.

[0100] "An algorithmic means for scoring retrieved information and selecting the optimal meal" refers to a computational method for quantifying discovered meal information using multiple evaluation criteria and identifying the most suitable meal.

[0101] "Means for presenting selected meals to the user" refers to a part of a system that provides users with meal information identified by a selection algorithm, either visually or audibly.

[0102] "Means of presenting deliverable options from searched meal information" refers to a method of presenting meal options that are actually deliverable based on the user's residential area, using relevant culinary information.

[0103] This invention begins with a user inputting conditions such as food preferences, budget, taste and cooking time, and allergy information via a device such as a smartphone or tablet. The device's interface receives this information and transmits it to a server. Based on the received user input data, the server searches for options that match the conditions from a vast amount of food information stored on a data storage medium.

[0104] The server uses AI-powered algorithms to score the searched meal information against various evaluation criteria and identify the most appropriate dish. This algorithm utilizes machine learning libraries such as Scikit-learn and TENSORFLOW®. The resulting meal options are selected based on the user's location and the availability of deliverable menus.

[0105] The selected meal information is visually presented to the user via a terminal. The user can choose from the presented options to suit their preferences. This information includes details about the dishes and whether delivery is available, and it is possible to place an order.

[0106] For example, if a user requests "vegetarian Japanese food priced under 2000 yen that can be delivered within 30 minutes," the server will identify matching dishes from its database and display the corresponding delivery options.

[0107] Example of a prompt:

[0108] Given user preferences for a vegetarian Japanese dish under $20 that can be delivered within 30 minutes, propose suitable meal options along with delivery services.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input information such as ingredients, budget, taste preferences, allergy information, and health goals using the terminal's interface. This input data is aggregated on the terminal and sent to the server in an appropriate format. This process collects user selections and converts them into a data format that can be processed by a machine.

[0112] Step 2:

[0113] The server receives user input data. Based on the received data, it accesses the data storage medium and searches for meal information that matches the criteria. In this step, techniques such as SQL queries are used to extract the necessary data quickly and efficiently.

[0114] Step 3:

[0115] The server uses an AI-based algorithm to score the searched meal information. Here, weights are assigned to each option based on user preferences and constraints, and an evaluation is given accordingly. A machine learning library such as Scikit-learn is used to apply the model and calculate the goodness of fit for each option.

[0116] Step 4:

[0117] The server sends the optimal meal options to the terminal. This includes image data and text information, allowing the user to visually confirm the options. At this point, delivery options for the selected meals are also sent.

[0118] Step 5:

[0119] The user reviews the presented meal information and selects their desired option. After making their selection, the user confirms their choice and proceeds with the actual order or procurement of the food. This entire process is completed on the terminal, and the final data is sent to the server as feedback.

[0120] Step 6:

[0121] The server attempts to improve its data storage medium and AI algorithms based on feedback received from users. This improves the accuracy of recommendations in subsequent uses. The collected feedback data is used to retrain the model and update the database.

[0122] 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.

[0123] This invention is a system that provides an optimal cooking recipe by taking into account the user's input information and the user's emotional state. This system has a configuration that includes an interface means, a cooking database search means, a recipe selection algorithm means, and an emotion engine.

[0124] First, the user enters information about the dish through their device. This information includes ingredients, budget, preferences, allergy information, and health goals. The device converts this information into a data format and sends it to the server.

[0125] The server receives user input data and begins the process of accessing a recipe database to search for recipe candidates that match the criteria. The database contains detailed information about various dishes. Next, the server uses an emotion engine to recognize the user's emotional state. This emotional state is obtained through camera footage, voice input, or direct feedback from the user.

[0126] Based on the user's emotions recognized by the emotion engine, the server considers emotional data in the recipe scoring process. This scoring evaluates nutritional value, user preference, and whether it aligns with the user's current emotions. The dish category is adjusted according to the emotions, and the most suitable recipe is selected.

[0127] The server then processes the selected recipe and sends it to the terminal in a format adapted to the user's emotional state. The terminal displays the details of the suggested recipe to the user. The user can then browse the suggested recipes and enjoy preparing a meal that suits their mood at the time.

[0128] For example, if a user feels like relaxing, the emotion engine can recognize this and prioritize suggesting dishes suitable for a calm mood, such as soup or comfort food. In this way, the present invention improves user satisfaction by providing a personalized cooking experience that takes emotional states into account.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user accesses the device's interface and inputs subjective criteria such as ingredients, budget, taste preferences, allergy information, and health goals.

[0132] Step 2:

[0133] The terminal processes the user's input data and formats it into the necessary data format for transmission to the server.

[0134] Step 3:

[0135] The server receives data from the terminal and establishes a connection with the recipe database. The server then uses the database to search for the relevant recipe based on the user's criteria.

[0136] Step 4:

[0137] The server activates an emotion engine to recognize the user's emotions. This recognition is performed by analyzing the user's facial expressions and voice tone.

[0138] Step 5:

[0139] The server adjusts the scoring algorithm based on the recognized sentiment data. This ensures that recipe recommendations reflect the user's current mood.

[0140] Step 6:

[0141] The server selects the most suitable recipe based on the scoring results. Selection criteria include nutritional value, user preference, budget, and perceived emotional state.

[0142] Step 7:

[0143] The server processes the selected recipe to match the user's emotions and sends it to the terminal.

[0144] Step 8:

[0145] The terminal displays recipes received from the server to the user. The user can then use these recipes to create a dish that suits their mood.

[0146] Step 9:

[0147] After trying out a recipe, users can input feedback into a terminal to contribute to system improvements. The server uses this feedback to update its database and algorithms.

[0148] (Example 2)

[0149] 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".

[0150] In recent years, there has been a growing demand for personalized meal suggestions that cater to individual needs and emotions. However, conventional systems have struggled to adequately consider emotional states when selecting recipes. Furthermore, there has been a lack of methods to improve the accuracy of these systems by utilizing user feedback.

[0151] 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.

[0152] In this invention, the server includes a device for inputting user information, a device for searching for options that match the specified conditions from a recipe data collection, and a device for recognizing the emotional state and selecting the optimal option based on that state. This enables the selection of the optimal recipe according to the emotional state of each individual user and continuous improvement of the system through feedback.

[0153] "User information" refers to data that users use to input specific requirements and preferences, including information on ingredients, budget, tastes, and allergies.

[0154] "Device" refers to hardware or software components used for data input, retrieval, presentation, etc.

[0155] A "culinary data collection" is a database containing information about various dishes, and it is searchable according to the input criteria.

[0156] "Options" refers to individual recipes or cooking suggestions that match the specified criteria within the cooking data collection.

[0157] "Method" refers to the algorithm or process used to evaluate options and select the optimal one.

[0158] "Emotional state" refers to the user's current psychological and emotional condition and includes information used by the system to recognize and analyze it.

[0159] "Feedback" refers to opinions and suggestions for improvement from users, and is data used to improve the performance and accuracy of the system.

[0160] This invention is a system that provides optimal cooking suggestions by considering the user's condition information and emotional state. This system is realized by comprising a "device," a "cooking data collection," a "method," and an "emotion engine."

[0161] First, the user enters information about the dish via their device. This information includes elements such as ingredients, budget, preferences, allergy information, and health goals. The entered information is then converted by the device into a data format, such as JSON, and sent to the server.

[0162] When the server receives user input data, it accesses a "recipe data collection" to search for "options" that match the criteria. The search uses a database management system (DBMS) and employs techniques similar to SQL queries. Furthermore, the server uses an "emotion engine" to understand the user's emotional state. This emotional state is obtained using data from the camera and microphone connected to the device, and an emotion recognition AI model identifies emotions by analyzing facial expressions and voice tone.

[0163] Subsequently, the server uses a "method" to score the "options" based on the recognized emotions. It selects the optimal recipe using a generative AI model, evaluating nutritional value, user preferences, and suitability for the emotional state. The selected recipe is then processed in a format adapted to the user's emotions and sent to the terminal.

[0164] The device displays the received recipe information to the user in detail. Multimedia elements such as images, videos, and audio guides can be used as visual guidelines and supplementary information. This allows users to enjoy preparing the suggested recipes and have a meal experience that suits their mood.

[0165] For example, if a user enters conditions into their device such as "I want to relax, I have a low budget and would like a vegetarian menu," the system can analyze this and suggest gentle dishes such as "vegetable soup" or "pasta salad." An example of a prompt to the generative AI model might be, "The user is looking for a vegetarian, low-budget dinner. Please suggest a recipe that will help them relax."

[0166] This makes it possible for the present invention to realize personalized meal suggestions that are tailored to the individual's emotional state, thereby improving the meal preparation experience.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] Users input information about their cooking preferences through a terminal. This input includes ingredients, budget, tastes, allergy information, and health goals. The terminal converts this information into digital data format and sends it to the server using JSON format or similar.

[0170] Step 2:

[0171] The server receives user input data sent from the terminal. This data serves as the basis for searching for recipe candidates that match the specified criteria using a recipe database. The server uses a database management system and issues SQL queries to extract appropriate recipe information from a large database. As output, it obtains multiple recipe candidates that match the criteria.

[0172] Step 3:

[0173] The server uses the camera and microphone to recognize the user's emotional state. This involves using an emotion recognition AI model to analyze the user's facial expressions and voice tone. The results of this analysis are then organized into data indicating the emotional state. Specifically, it determines emotional states such as joy, relaxation, and stress. This data is the output used in the subsequent scoring process.

[0174] Step 4:

[0175] The server scores the recipe candidates obtained in step 2 based on the emotional state recognized in step 3. This scoring process analyzes nutritional value, user preferences, budget, and suitability for the emotional state. A generative AI model supports this process, comprehensively evaluating these factors to select the most suitable recipe. As a result, the optimal recipe for the user is output.

[0176] Step 5:

[0177] The server processes the selected recipe into a format suitable for the user's emotions. For example, visual effects or background music may be added to enhance relaxation. This processed recipe information is then sent to the terminal.

[0178] Step 6:

[0179] The device receives processed recipe information and displays it to the user. This display incorporates multimedia elements such as images, videos, and audio guides. Based on this detailed information, the user can visually confirm the suggested recipe and enjoy the cooking process.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0182] In modern life, the variety of food options is vast, making it difficult to choose the optimal meal based on user preferences and emotions. Furthermore, food delivery services often lack suggestions that consider user emotions, highlighting the need for improved customer satisfaction.

[0183] 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.

[0184] In this invention, the server includes an information processing device for inputting user information, means for receiving user input data and searching for meal suggestions that match the criteria from a set of meal data, and calculation means for recognizing the user's emotional state and scoring the searched meal suggestions based on that state. This makes it possible to suggest meal suggestions that correspond to the user's emotional state.

[0185] "User information" is a general term for data that includes users' dietary preferences, ingredients, budget, allergies, and health goals.

[0186] An "information processing device" is a terminal device that allows users to input information about their meals and send it to a server.

[0187] A "meal data collection" is a database containing recipes and nutritional information related to various meals.

[0188] "Emotional state" refers to a user's temporary psychological and emotional state, and is data based on facial expressions, voice, or user feedback.

[0189] The "computation means" is a configuration that executes an algorithm to score the optimal meal suggestion based on the user's input data and emotional state.

[0190] A "meal suggestion" is a recommendation of the optimal meal selected based on the user's conditions and feelings.

[0191] The system for implementing this invention consists of an information processing device that receives user input information and a server in a data center. The user inputs dietary conditions (e.g., ingredients, budget, preferences, allergies, health goals, etc.) through the information processing device. The information processing device converts this data into an appropriate format and transmits it to the server.

[0192] The server searches for meal suggestions that match the user's input data by comparing it against a set of meal data. Furthermore, the server uses a camera and voice input devices for emotion analysis to identify the user's emotional state. This emotion data is processed by an emotion analysis engine within the server. This engine uses generative AI models such as NVIDIA's Emotion AI to analyze the user's emotions in real time.

[0193] Based on user input and emotional data, the server evaluates meal suggestions and selects the optimal suggestion using a scoring algorithm. This process involves complex data calculations using software such as Python and OpenCV. The selected meal suggestions are sent to an information processing device as personalized content according to the user's emotional state and displayed to the user.

[0194] For example, if a user feels "a little tired" and communicates this to their smartphone, the information processing device sends this information to the server, which then suggests a relaxing meal tailored to the user's emotional state, such as a warm soup or comfort food. This allows users to enjoy a meal that better suits their emotions, contributing to increased satisfaction with the delivery service.

[0195] An example of a prompt message is, "How are you feeling today? Whether you want to refresh, relax, or feel energized, we'll suggest a dish that suits your mood."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user uses an information processing device to input dietary information (ingredients, budget, preferences, allergies, health goals, etc.). This information is converted into a digital format and sent to the server. The input is in text format, and the output is in a structured data format.

[0199] Step 2:

[0200] The information processing device uses the device's camera and microphone to collect the user's facial expressions and audio data for emotion analysis. This data is processed in real time to analyze the user's emotional state and transmit it to the server as emotion data. The input is camera video and audio data, and the output is quantitative data indicating the user's emotional state.

[0201] Step 3:

[0202] The server receives the user's input conditions and sentiment data, and searches the food data set for meal suggestions that match the conditions. This process uses a database search algorithm, and the output is a list of candidate meal suggestions.

[0203] Step 4:

[0204] The server uses an emotion analysis engine to evaluate emotional data and scores a list of meal suggestions based on the emotions expressed. Leveraging NVIDIA's Emotion AI model, it calculates the degree to which each meal suggestion matches the user's emotions and outputs a list of meal suggestions with scores.

[0205] Step 5:

[0206] The server selects the optimal meal plan from a scored list of meal plans. Using a score comparison algorithm written in Python, it outputs the meal plan with the highest score.

[0207] Step 6:

[0208] The information processing device presents the user with the optimal meal suggestions received from the server. This allows the user to select and order a meal that suits their mood. The output is the meal suggestions displayed on the user interface.

[0209] 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.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention is a system for providing cooking recipes tailored to the user's preferences and needs, and it recommends the optimal recipe based on user input information using various cooking data. The system's operation and specific examples are shown below.

[0226] First, the user uses the terminal's user interface to input information such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal collects this information, converts it to an appropriate data format, and sends it to the server.

[0227] Based on the received user information, the server initiates access to the cooking database. This database contains recipes for various dishes, including ingredients, cooking methods, and nutritional information. The server queries the database according to the user's request and retrieves a list of recipes that match the criteria.

[0228] Next, the server runs an algorithm to score the retrieved recipes. This algorithm utilizes AI to assign evaluation values ​​to recipes, taking into account factors such as nutritional balance, user preferences, and budget constraints. It then selects the recipe with the highest evaluation and generates several recommended recipes.

[0229] The server then sends the details of the recommended recipe to the terminal. The terminal displays this to the user, allowing them to view the ingredient list, cooking instructions, nutritional information, and more. This makes it easy for the user to select and cook a meal that suits their needs.

[0230] For example, suppose a user specifies "a low-calorie Italian dish that can be made in under 30 minutes with a budget of 1000 yen." In this case, the server searches its Italian recipe database for relevant recipes and presents the user with a recipe that meets the criteria. The user can also send feedback from their device after cooking, which the server can then use to update the database and improve the algorithm.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user enters information about their desired dish through the terminal's user interface, such as the ingredients they want to use, their budget, taste preferences, allergy information, and health goals.

[0234] Step 2:

[0235] The terminal receives user input data, converts it to the required data format (e.g., JSON format), and prepares it for transmission to the system.

[0236] Step 3:

[0237] The terminal sends the converted data to the server. The server then receives the information necessary for processing.

[0238] Step 4:

[0239] The server analyzes the received data and establishes a connection with the recipe database. Based on the analysis results, it identifies the categories of dishes and constraints that match the user's criteria.

[0240] Step 5:

[0241] Based on the specified conditions, the server queries the cooking database for suitable recipe candidates and retrieves the necessary detailed information (ingredients, cooking method, nutritional information, etc.).

[0242] Step 6:

[0243] The server runs an AI-powered algorithm to score the retrieved recipe candidates based on factors such as nutritional balance, user preference, and budget.

[0244] Step 7:

[0245] The server selects the optimal recipe based on the scoring results. It then organizes the details of the selected recipe and processes it into a format for providing to the user.

[0246] Step 8:

[0247] The server sends the selected recipe information to the terminal as a result of the processing.

[0248] Step 9:

[0249] The terminal receives recipe information from the server and displays the contents (list of ingredients, cooking instructions, nutritional information, etc.) to the user.

[0250] Step 10:

[0251] Users prepare dishes based on the provided recipes and enjoy the cooking experience. Afterward, they can send feedback from their device as needed.

[0252] (Example 1)

[0253] 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."

[0254] The challenge lies in providing users with the most relevant information quickly and accurately, and in continuously improving the accuracy of information retrieval and suggestions by incorporating user feedback. Conventional systems struggle to find appropriate information tailored to individual user needs, and lack effective methods for utilizing feedback.

[0255] 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.

[0256] In this invention, the server includes means for searching an information storage device based on data received from the user and obtaining information that matches the specified criteria; means for scoring the obtained information using an AI algorithm and selecting the optimal information; and means for receiving feedback from the user and improving the information storage device and the AI ​​algorithm. This enables the provision of information optimized to the individual needs of the user and continuous improvement of the system.

[0257] "User information" refers to data that details individual conditions and requests that users provide to the system, such as ingredients, budget, taste preferences, allergy information, and health goals.

[0258] A "terminal" is a computer device used by users to input information and exchange data with a server, and includes devices such as smartphones and personal computers.

[0259] An "interface means" is a function that provides a user interface for users to input information and enables data exchange between the user and the terminal.

[0260] A "server" is a remote computer system that performs various computational processes, such as processing received data, searching information storage devices, and scoring using AI algorithms.

[0261] An "information storage device" is a database or storage device for storing a wide variety of information, and is a data storage facility where recipes and related information are stored.

[0262] An "AI algorithm" is a program that uses machine learning techniques to perform advanced computational processing based on input information and derive the optimal result.

[0263] "Scoring" is the process of evaluating and quantifying data and information based on specific criteria, and is used to measure the suitability of recipes and suggestions.

[0264] "Feedback" refers to opinions and evaluations based on actual user experiences, and is data used to improve the system.

[0265] This invention is a system for providing information tailored to the individual needs of users. The system mainly consists of a terminal, a server, an information storage device (database), and an AI algorithm. The user first accesses an information processing interface using the terminal. The terminal receives input data from the user, such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal also formats this data appropriately and sends it to the server.

[0266] The server searches its information storage device for information that matches the specified criteria based on the data received from the user. This information storage device functions as a database and stores various types of information. The server then scores the retrieved information using an AI algorithm. This scoring process evaluates and quantifies how well the information matches the user's needs. The AI ​​algorithm can be written in a programming language such as Python and includes a machine learning model. This model takes into account factors such as nutritional balance, user preferences, and budget constraints.

[0267] The server selects the most relevant information based on the scoring results and sends the details to the terminal. This information includes specific recipes and product purchase information. The terminal displays the information to the user, helping them make the most appropriate choice. Furthermore, the user can provide feedback, which is received by the server and used to improve the information storage and algorithms.

[0268] For example, when a user is looking for "low-calorie Italian food that can be made in under 30 minutes for a budget of 1000 yen," the prompt entered into the server would be "Suggest low-calorie Italian food that can be made in under 30 minutes." As a result, the server would select recipes that match the criteria and present them to the user. Through this series of processes, the system can provide optimal information tailored to the user's needs.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The user inputs information through the terminal's interface. This information includes ingredients used, budget, taste preferences, allergy information, and health goals. This data is received by the terminal. The terminal converts this user data into a specific format (e.g., JSON, XML) and formats it in a way that is easily interpretable by the server. The output of this step is the formatted data.

[0272] Step 2:

[0273] The terminal sends formatted user data to the server. This transmission utilizes an internet connection and a protocol (e.g., HTTP, HTTPS). Specifically, the terminal sends data to the server via an API. The input to this step is the formatted user data, and the output is the completion of the data transfer to the server.

[0274] Step 3:

[0275] The server queries the information storage device based on the user data it receives. It uses SQL or NoSQL queries to search for recipes and information that match the criteria. The server uses complex search expressions to retrieve data that matches the criteria. The input for this step is user data, and the output is a list of data that matches the criteria.

[0276] Step 4:

[0277] The server uses an AI algorithm to score the acquired information. The scoring process uses a machine learning model to consider factors such as nutritional value, user preferences, and budget, assigning an evaluation value to each recipe and piece of information. This data processing is performed using a specific programming language (e.g., Python). The input for this step is the acquired information, and the output is a list of information with assigned evaluation values.

[0278] Step 5:

[0279] Based on the scoring results, the server selects the most highly evaluated information and sends the details of that information to the terminal. The information includes the ingredients, cooking methods, and nutritional information of the selected recipe. Specifically, the server uses an API to format the selected information and return it to the terminal. The input for this step is the scoring result, and the output is the optimal information sent to the terminal.

[0280] Step 6:

[0281] The terminal displays the information received from the server to the user. The user can make necessary selections based on this displayed information. As a specific operation, the information is visually presented on the terminal screen for the user to read. The input for this step is the information from the server, and the output is the display of information to the user.

[0282] Step 7:

[0283] The user can send feedback from the terminal based on the provided information. The feedback is sent to the server again and used to improve the system. The terminal formats the feedback data entered by the user and relays it to the server. The input for this step is the user's feedback, and the output is the completion of sending the feedback data to the server.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In modern lifestyles, while consumers seek efficient and customized meal choices, it has become a challenge to effectively select dishes within time and budget constraints and to easily place orders and procure them. Therefore, there is a need for means to provide appropriate dish selections and available dishes based on the individual preferences and conditions of users.

[0287] 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.

[0288] In this invention, the server includes an interface means for inputting user information, means for receiving user input data and searching for meals that match the criteria from a data storage medium, algorithm means for scoring the searched information and selecting the optimal meal, means for presenting the selected meal to the user, and means for presenting delivery options from the searched meal information. This makes it possible to effectively and efficiently present the user with the meal best suited to their individual criteria and its delivery options.

[0289] "An interface means for inputting user information" refers to an input device or application used by a user to provide their preferences and conditions.

[0290] "A means of receiving user input data and searching for meals that match the conditions from a data storage medium" refers to a function that searches stored meal information based on information received from the user and extracts options that are suitable for the conditions.

[0291] "An algorithmic means for scoring retrieved information and selecting the optimal meal" refers to a computational method for quantifying discovered meal information using multiple evaluation criteria and identifying the most suitable meal.

[0292] "Means for presenting selected meals to the user" refers to a part of a system that provides users with meal information identified by a selection algorithm, either visually or audibly.

[0293] "Means of presenting deliverable options from searched meal information" refers to a method of presenting meal options that are actually deliverable based on the user's residential area, using relevant culinary information.

[0294] This invention begins with a user inputting conditions such as food preferences, budget, taste and cooking time, and allergy information via a device such as a smartphone or tablet. The device's interface receives this information and transmits it to a server. Based on the received user input data, the server searches for options that match the conditions from a vast amount of food information stored on a data storage medium.

[0295] The server uses AI-powered algorithms to score the searched meal information against various evaluation criteria and identify the most appropriate dish. This algorithm utilizes machine learning libraries such as Scikit-learn and TensorFlow. The resulting meal options are selected, including delivery options that take into account the user's residential area.

[0296] The selected meal information is visually presented to the user via a terminal. The user can choose from the presented options to suit their preferences. This information includes details about the dishes and whether delivery is available, and it is possible to place an order.

[0297] For example, if a user requests "vegetarian Japanese food priced under 2000 yen that can be delivered within 30 minutes," the server will identify matching dishes from its database and display the corresponding delivery options.

[0298] Example of a prompt:

[0299] Given user preferences for a vegetarian Japanese dish under $20 that can be delivered within 30 minutes, propose suitable meal options along with delivery services.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The user uses the interface of the terminal to input information such as ingredients, budget, taste preferences, allergy information, and health goals. These input data are aggregated on the terminal and sent to the server in an appropriate format. In this process, the user's selections are collected and converted into a data format that can be processed by a machine.

[0303] Step 2:

[0304] The server receives the user's input data. Based on the received data, it accesses the data storage medium and searches for diet information that meets the conditions. In this step, techniques such as SQL queries are used to extract the necessary data quickly and efficiently.

[0305] Step 3:

[0306] The server performs scoring on the retrieved diet information using an algorithm with AI. Here, weighting is performed according to the user's preferences and constraints, and evaluations are assigned to the options. A machine learning library such as Scikit-learn is used to apply the model and calculate the fitness of each option.

[0307] Step 4:

[0308] [[ID=V29]] The server sends the diet option determined to be optimal to the terminal. The transmission includes image data and text information so that the user can visually confirm. At this point, the available options regarding the selected diet are also sent simultaneously.

[0309] Step 5:[[ID=3V5]]

[0310] The user checks the presented diet information and selects the desired option(s). After selection, the user finalizes the selection and places an actual order or arranges for cooking ingredients. This operation is completed on the terminal, and the final data is sent to the server as feedback.

[0311] Step 6:

[0312] The server attempts to improve its data storage medium and AI algorithms based on feedback received from users. This improves the accuracy of recommendations in subsequent uses. The collected feedback data is used to retrain the model and update the database.

[0313] 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.

[0314] This invention is a system that provides an optimal cooking recipe by taking into account the user's input information and the user's emotional state. This system has a configuration that includes an interface means, a cooking database search means, a recipe selection algorithm means, and an emotion engine.

[0315] First, the user enters information about the dish through their device. This information includes ingredients, budget, preferences, allergy information, and health goals. The device converts this information into a data format and sends it to the server.

[0316] The server receives user input data and begins the process of accessing a recipe database to search for recipe candidates that match the criteria. The database contains detailed information about various dishes. Next, the server uses an emotion engine to recognize the user's emotional state. This emotional state is obtained through camera footage, voice input, or direct feedback from the user.

[0317] Based on the user's emotions recognized by the emotion engine, the server considers emotional data in the recipe scoring process. This scoring evaluates nutritional value, user preference, and whether it aligns with the user's current emotions. The dish category is adjusted according to the emotions, and the most suitable recipe is selected.

[0318] The server then processes the selected recipe and sends it to the terminal in a format adapted to the user's emotional state. The terminal displays the details of the suggested recipe to the user. The user can then browse the suggested recipes and enjoy preparing a meal that suits their mood at the time.

[0319] For example, if a user feels like relaxing, the emotion engine can recognize this and prioritize suggesting dishes suitable for a calm mood, such as soup or comfort food. In this way, the present invention improves user satisfaction by providing a personalized cooking experience that takes emotional states into account.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user accesses the device's interface and inputs subjective criteria such as ingredients, budget, taste preferences, allergy information, and health goals.

[0323] Step 2:

[0324] The terminal processes the user's input data and formats it into the necessary data format for transmission to the server.

[0325] Step 3:

[0326] The server receives data from the terminal and establishes a connection with the recipe database. The server then uses the database to search for the relevant recipe based on the user's criteria.

[0327] Step 4:

[0328] The server activates an emotion engine to recognize the user's emotions. This recognition is performed by analyzing the user's facial expressions and voice tone.

[0329] Step 5:

[0330] The server adjusts the scoring algorithm based on the recognized sentiment data. This ensures that recipe recommendations reflect the user's current mood.

[0331] Step 6:

[0332] The server selects the most suitable recipe based on the scoring results. Selection criteria include nutritional value, user preference, budget, and perceived emotional state.

[0333] Step 7:

[0334] The server processes the selected recipe to match the user's emotions and sends it to the terminal.

[0335] Step 8:

[0336] The terminal displays recipes received from the server to the user. The user can then use these recipes to create a dish that suits their mood.

[0337] Step 9:

[0338] After trying out a recipe, users can input feedback into a terminal to contribute to system improvements. The server uses this feedback to update its database and algorithms.

[0339] (Example 2)

[0340] 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".

[0341] In recent years, there has been a growing demand for personalized meal suggestions that cater to individual needs and emotions. However, conventional systems have struggled to adequately consider emotional states when selecting recipes. Furthermore, there has been a lack of methods to improve the accuracy of these systems by utilizing user feedback.

[0342] 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.

[0343] In this invention, the server includes a device for inputting user information, a device for searching for options that match the specified conditions from a recipe data collection, and a device for recognizing the emotional state and selecting the optimal option based on that state. This enables the selection of the optimal recipe according to the emotional state of each individual user and continuous improvement of the system through feedback.

[0344] "User information" refers to data that users use to input specific requirements and preferences, including information on ingredients, budget, tastes, and allergies.

[0345] "Device" refers to hardware or software components used for data input, retrieval, presentation, etc.

[0346] A "culinary data collection" is a database containing information about various dishes, and it is searchable according to the input criteria.

[0347] "Options" refers to individual recipes or cooking suggestions that match the specified criteria within the cooking data collection.

[0348] "Method" refers to the algorithm or process used to evaluate options and select the optimal one.

[0349] "Emotional state" refers to the user's current psychological and emotional condition and includes information used by the system to recognize and analyze it.

[0350] "Feedback" refers to opinions and suggestions for improvement from users, and is data used to improve the performance and accuracy of the system.

[0351] This invention is a system that provides optimal cooking suggestions by considering the user's condition information and emotional state. This system is realized by comprising a "device," a "cooking data collection," a "method," and an "emotion engine."

[0352] First, the user enters information about the dish via their device. This information includes elements such as ingredients, budget, preferences, allergy information, and health goals. The entered information is then converted by the device into a data format, such as JSON, and sent to the server.

[0353] When the server receives user input data, it accesses a "recipe data collection" to search for "options" that match the criteria. The search uses a database management system (DBMS) and employs techniques similar to SQL queries. Furthermore, the server uses an "emotion engine" to understand the user's emotional state. This emotional state is obtained using data from the camera and microphone connected to the device, and an emotion recognition AI model identifies emotions by analyzing facial expressions and voice tone.

[0354] Subsequently, the server uses a "method" to score the "options" based on the recognized emotions. It selects the optimal recipe using a generative AI model, evaluating nutritional value, user preferences, and suitability for the emotional state. The selected recipe is then processed in a format adapted to the user's emotions and sent to the terminal.

[0355] The device displays the received recipe information to the user in detail. Multimedia elements such as images, videos, and audio guides can be used as visual guidelines and supplementary information. This allows users to enjoy preparing the suggested recipes and have a meal experience that suits their mood.

[0356] For example, if a user enters conditions into their device such as "I want to relax, I have a low budget and would like a vegetarian menu," the system can analyze this and suggest gentle dishes such as "vegetable soup" or "pasta salad." An example of a prompt to the generative AI model might be, "The user is looking for a vegetarian, low-budget dinner. Please suggest a recipe that will help them relax."

[0357] This makes it possible for the present invention to realize personalized meal suggestions that are tailored to the individual's emotional state, thereby improving the meal preparation experience.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] Users input information about their cooking preferences through a terminal. This input includes ingredients, budget, tastes, allergy information, and health goals. The terminal converts this information into digital data format and sends it to the server using JSON format or similar.

[0361] Step 2:

[0362] The server receives user input data sent from the terminal. This data serves as the basis for searching for recipe candidates that match the specified criteria using a recipe database. The server uses a database management system and issues SQL queries to extract appropriate recipe information from a large database. As output, it obtains multiple recipe candidates that match the criteria.

[0363] Step 3:

[0364] The server uses the camera and microphone to recognize the user's emotional state. This involves using an emotion recognition AI model to analyze the user's facial expressions and voice tone. The results of this analysis are then organized into data indicating the emotional state. Specifically, it determines emotional states such as joy, relaxation, and stress. This data is the output used in the subsequent scoring process.

[0365] Step 4:

[0366] The server scores the recipe candidates obtained in step 2 based on the emotional state recognized in step 3. This scoring process analyzes nutritional value, user preferences, budget, and suitability for the emotional state. A generative AI model supports this process, comprehensively evaluating these factors to select the most suitable recipe. As a result, the optimal recipe for the user is output.

[0367] Step 5:

[0368] The server processes the selected recipe into a format suitable for the user's emotions. For example, visual effects or background music may be added to enhance relaxation. This processed recipe information is then sent to the terminal.

[0369] Step 6:

[0370] The device receives processed recipe information and displays it to the user. This display incorporates multimedia elements such as images, videos, and audio guides. Based on this detailed information, the user can visually confirm the suggested recipe and enjoy the cooking process.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0373] In modern life, the variety of food options is vast, making it difficult to choose the optimal meal based on user preferences and emotions. Furthermore, food delivery services often lack suggestions that consider user emotions, highlighting the need for improved customer satisfaction.

[0374] 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.

[0375] In this invention, the server includes an information processing device for inputting user information, means for receiving user input data and searching for meal suggestions that match the criteria from a set of meal data, and calculation means for recognizing the user's emotional state and scoring the searched meal suggestions based on that state. This makes it possible to suggest meal suggestions that correspond to the user's emotional state.

[0376] "User information" is a general term for data that includes users' dietary preferences, ingredients, budget, allergies, and health goals.

[0377] An "information processing device" is a terminal device that allows users to input information about their meals and send it to a server.

[0378] A "meal data collection" is a database containing recipes and nutritional information related to various meals.

[0379] "Emotional state" refers to a user's temporary psychological and emotional state, and is data based on facial expressions, voice, or user feedback.

[0380] The "computation means" is a configuration that executes an algorithm to score the optimal meal suggestion based on the user's input data and emotional state.

[0381] A "meal suggestion" is a recommendation of the optimal meal selected based on the user's conditions and feelings.

[0382] The system for implementing this invention consists of an information processing device that receives user input information and a server in a data center. The user inputs dietary conditions (e.g., ingredients, budget, preferences, allergies, health goals, etc.) through the information processing device. The information processing device converts this data into an appropriate format and transmits it to the server.

[0383] The server searches for meal suggestions that match the user's input data by comparing it against a set of meal data. Furthermore, the server uses a camera and voice input devices for emotion analysis to identify the user's emotional state. This emotion data is processed by an emotion analysis engine within the server. This engine uses generative AI models such as NVIDIA's Emotion AI to analyze the user's emotions in real time.

[0384] Based on user input and emotional data, the server evaluates meal suggestions and selects the optimal suggestion using a scoring algorithm. This process involves complex data calculations using software such as Python and OpenCV. The selected meal suggestions are sent to an information processing device as personalized content according to the user's emotional state and displayed to the user.

[0385] For example, if a user feels "a little tired" and communicates this to their smartphone, the information processing device sends this information to the server, which then suggests a relaxing meal tailored to the user's emotional state, such as a warm soup or comfort food. This allows users to enjoy a meal that better suits their emotions, contributing to increased satisfaction with the delivery service.

[0386] An example of a prompt message is, "How are you feeling today? Whether you want to refresh, relax, or feel energized, we'll suggest a dish that suits your mood."

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The user uses an information processing device to input dietary information (ingredients, budget, preferences, allergies, health goals, etc.). This information is converted into a digital format and sent to the server. The input is in text format, and the output is in a structured data format.

[0390] Step 2:

[0391] The information processing device uses the device's camera and microphone to collect the user's facial expressions and audio data for emotion analysis. This data is processed in real time to analyze the user's emotional state and transmit it to the server as emotion data. The input is camera video and audio data, and the output is quantitative data indicating the user's emotional state.

[0392] Step 3:

[0393] The server receives the user's input conditions and sentiment data, and searches the food data set for meal suggestions that match the conditions. This process uses a database search algorithm, and the output is a list of candidate meal suggestions.

[0394] Step 4:

[0395] The server uses an emotion analysis engine to evaluate emotional data and scores a list of meal suggestions based on the emotions expressed. Leveraging NVIDIA's Emotion AI model, it calculates the degree to which each meal suggestion matches the user's emotions and outputs a list of meal suggestions with scores.

[0396] Step 5:

[0397] The server selects the optimal meal plan from a scored list of meal plans. Using a score comparison algorithm written in Python, it outputs the meal plan with the highest score.

[0398] Step 6:

[0399] The information processing device presents the user with the optimal meal suggestions received from the server. This allows the user to select and order a meal that suits their mood. The output is the meal suggestions displayed on the user interface.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] This invention is a system for providing cooking recipes tailored to the user's preferences and needs, and it recommends the optimal recipe based on user input information using various cooking data. The system's operation and specific examples are shown below.

[0417] First, the user uses the terminal's user interface to input information such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal collects this information, converts it to an appropriate data format, and sends it to the server.

[0418] Based on the received user information, the server initiates access to the cooking database. This database contains recipes for various dishes, including ingredients, cooking methods, and nutritional information. The server queries the database according to the user's request and retrieves a list of recipes that match the criteria.

[0419] Next, the server runs an algorithm to score the retrieved recipes. This algorithm utilizes AI to assign evaluation values ​​to recipes, taking into account factors such as nutritional balance, user preferences, and budget constraints. It then selects the recipe with the highest evaluation and generates several recommended recipes.

[0420] The server then sends the details of the recommended recipe to the terminal. The terminal displays this to the user, allowing them to view the ingredient list, cooking instructions, nutritional information, and more. This makes it easy for the user to select and cook a meal that suits their needs.

[0421] For example, suppose a user specifies "a low-calorie Italian dish that can be made in under 30 minutes with a budget of 1000 yen." In this case, the server searches its Italian recipe database for relevant recipes and presents the user with a recipe that meets the criteria. The user can also send feedback from their device after cooking, which the server can then use to update the database and improve the algorithm.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user enters information about their desired dish through the terminal's user interface, such as the ingredients they want to use, their budget, taste preferences, allergy information, and health goals.

[0425] Step 2:

[0426] The terminal receives user input data, converts it to the required data format (e.g., JSON format), and prepares it for transmission to the system.

[0427] Step 3:

[0428] The terminal sends the converted data to the server. The server then receives the information necessary for processing.

[0429] Step 4:

[0430] The server analyzes the received data and establishes a connection with the recipe database. Based on the analysis results, it identifies the categories of dishes and constraints that match the user's criteria.

[0431] Step 5:

[0432] Based on the specified conditions, the server queries the cooking database for suitable recipe candidates and retrieves the necessary detailed information (ingredients, cooking method, nutritional information, etc.).

[0433] Step 6:

[0434] The server runs an AI-powered algorithm to score the retrieved recipe candidates based on factors such as nutritional balance, user preference, and budget.

[0435] Step 7:

[0436] The server selects the optimal recipe based on the scoring results. It then organizes the details of the selected recipe and processes it into a format for providing to the user.

[0437] Step 8:

[0438] The server sends the selected recipe information to the terminal as a result of the processing.

[0439] Step 9:

[0440] The terminal receives recipe information from the server and displays the contents (list of ingredients, cooking instructions, nutritional information, etc.) to the user.

[0441] Step 10:

[0442] Users prepare dishes based on the provided recipes and enjoy the cooking experience. Afterward, they can send feedback from their device as needed.

[0443] (Example 1)

[0444] 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."

[0445] The challenge lies in providing users with the most relevant information quickly and accurately, and in continuously improving the accuracy of information retrieval and suggestions by incorporating user feedback. Conventional systems struggle to find appropriate information tailored to individual user needs, and lack effective methods for utilizing feedback.

[0446] 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.

[0447] In this invention, the server includes means for searching an information storage device based on data received from the user and obtaining information that matches the specified criteria; means for scoring the obtained information using an AI algorithm and selecting the optimal information; and means for receiving feedback from the user and improving the information storage device and the AI ​​algorithm. This enables the provision of information optimized to the individual needs of the user and continuous improvement of the system.

[0448] "User information" refers to data that details individual conditions and requests that users provide to the system, such as ingredients, budget, taste preferences, allergy information, and health goals.

[0449] A "terminal" is a computer device used by users to input information and exchange data with a server, and includes devices such as smartphones and personal computers.

[0450] An "interface means" is a function that provides a user interface for users to input information and enables data exchange between the user and the terminal.

[0451] A "server" is a remote computer system that performs various computational processes, such as processing received data, searching information storage devices, and scoring using AI algorithms.

[0452] An "information storage device" is a database or storage device for storing a wide variety of information, and is a data storage facility where recipes and related information are stored.

[0453] An "AI algorithm" is a program that uses machine learning techniques to perform advanced computational processing based on input information and derive the optimal result.

[0454] "Scoring" is the process of evaluating and quantifying data and information based on specific criteria, and is used to measure the suitability of recipes and suggestions.

[0455] "Feedback" refers to opinions and evaluations based on actual user experiences, and is data used to improve the system.

[0456] This invention is a system for providing information tailored to the individual needs of users. The system mainly consists of a terminal, a server, an information storage device (database), and an AI algorithm. The user first accesses an information processing interface using the terminal. The terminal receives input data from the user, such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal also formats this data appropriately and sends it to the server.

[0457] The server searches its information storage device for information that matches the specified criteria based on the data received from the user. This information storage device functions as a database and stores various types of information. The server then scores the retrieved information using an AI algorithm. This scoring process evaluates and quantifies how well the information matches the user's needs. The AI ​​algorithm can be written in a programming language such as Python and includes a machine learning model. This model takes into account factors such as nutritional balance, user preferences, and budget constraints.

[0458] The server selects the most relevant information based on the scoring results and sends the details to the terminal. This information includes specific recipes and product purchase information. The terminal displays the information to the user, helping them make the most appropriate choice. Furthermore, the user can provide feedback, which is received by the server and used to improve the information storage and algorithms.

[0459] For example, when a user is looking for "low-calorie Italian food that can be made in under 30 minutes for a budget of 1000 yen," the prompt entered into the server would be "Suggest low-calorie Italian food that can be made in under 30 minutes." As a result, the server would select recipes that match the criteria and present them to the user. Through this series of processes, the system can provide optimal information tailored to the user's needs.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] The user inputs information through the terminal's interface. This information includes ingredients used, budget, taste preferences, allergy information, and health goals. This data is received by the terminal. The terminal converts this user data into a specific format (e.g., JSON, XML) and formats it in a way that is easily interpretable by the server. The output of this step is the formatted data.

[0463] Step 2:

[0464] The terminal sends formatted user data to the server. This transmission utilizes an internet connection and a protocol (e.g., HTTP, HTTPS). Specifically, the terminal sends data to the server via an API. The input to this step is the formatted user data, and the output is the completion of the data transfer to the server.

[0465] Step 3:

[0466] The server queries the information storage device based on the user data it receives. It uses SQL or NoSQL queries to search for recipes and information that match the criteria. The server uses complex search expressions to retrieve data that matches the criteria. The input for this step is user data, and the output is a list of data that matches the criteria.

[0467] Step 4:

[0468] The server uses an AI algorithm to score the acquired information. The scoring process uses a machine learning model to consider factors such as nutritional value, user preferences, and budget, assigning an evaluation value to each recipe and piece of information. This data processing is performed using a specific programming language (e.g., Python). The input for this step is the acquired information, and the output is a list of information with assigned evaluation values.

[0469] Step 5:

[0470] The server selects the most highly rated information based on the scoring results and sends the details of that information to the terminal. This information includes the ingredients, cooking method, and nutritional information of the selected recipe. Specifically, the server uses an API to format the selected information and send it back to the terminal. The input for this step is the scoring results, and the output is the optimal information sent to the terminal.

[0471] Step 6:

[0472] The terminal displays information received from the server to the user. The user can then make necessary selections based on this displayed information. Specifically, the information is presented visually on the terminal screen for the user to read. The input for this step is information from the server, and the output is the display of information to the user.

[0473] Step 7:

[0474] Users can submit feedback from their terminal based on the information provided. This feedback is then sent back to the server and used to improve the system. The terminal formats the feedback data entered by the user and relays it to the server. The input in this step is the user's feedback, and the output is the completion of the transmission of the feedback data to the server.

[0475] (Application Example 1)

[0476] 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."

[0477] In today's lifestyle, consumers demand efficient and customized meal choices, while simultaneously facing challenges in effectively selecting, ordering, and procuring food within time and budget constraints. Therefore, there is a need for a means to provide appropriate and deliverable meal options based on individual user preferences and circumstances.

[0478] 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.

[0479] In this invention, the server includes an interface means for inputting user information, means for receiving user input data and searching for meals that match the criteria from a data storage medium, algorithm means for scoring the searched information and selecting the optimal meal, means for presenting the selected meal to the user, and means for presenting delivery options from the searched meal information. This makes it possible to effectively and efficiently present the user with the meal best suited to their individual criteria and its delivery options.

[0480] "An interface means for inputting user information" refers to an input device or application used by a user to provide their preferences and conditions.

[0481] "A means of receiving user input data and searching for meals that match the conditions from a data storage medium" refers to a function that searches stored meal information based on information received from the user and extracts options that are suitable for the conditions.

[0482] "An algorithmic means for scoring retrieved information and selecting the optimal meal" refers to a computational method for quantifying discovered meal information using multiple evaluation criteria and identifying the most suitable meal.

[0483] "Means for presenting selected meals to the user" refers to a part of a system that provides users with meal information identified by a selection algorithm, either visually or audibly.

[0484] "Means of presenting deliverable options from searched meal information" refers to a method of presenting meal options that are actually deliverable based on the user's residential area, using relevant culinary information.

[0485] This invention begins with a user inputting conditions such as food preferences, budget, taste and cooking time, and allergy information via a device such as a smartphone or tablet. The device's interface receives this information and transmits it to a server. Based on the received user input data, the server searches for options that match the conditions from a vast amount of food information stored on a data storage medium.

[0486] The server uses AI-powered algorithms to score the searched meal information against various evaluation criteria and identify the most appropriate dish. This algorithm utilizes machine learning libraries such as Scikit-learn and TensorFlow. The resulting meal options are selected, including delivery options that take into account the user's residential area.

[0487] The selected meal information is visually presented to the user via a terminal. The user can choose from the presented options to suit their preferences. This information includes details about the dishes and whether delivery is available, and it is possible to place an order.

[0488] For example, if a user requests "vegetarian Japanese food priced under 2000 yen that can be delivered within 30 minutes," the server will identify matching dishes from its database and display the corresponding delivery options.

[0489] Example of a prompt:

[0490] Given user preferences for a vegetarian Japanese dish under $20 that can be delivered within 30 minutes, propose suitable meal options along with delivery services.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] Users input information such as ingredients, budget, taste preferences, allergy information, and health goals using the terminal's interface. This input data is aggregated on the terminal and sent to the server in an appropriate format. This process collects user selections and converts them into a data format that can be processed by a machine.

[0494] Step 2:

[0495] The server receives user input data. Based on the received data, it accesses the data storage medium and searches for meal information that matches the criteria. In this step, techniques such as SQL queries are used to extract the necessary data quickly and efficiently.

[0496] Step 3:

[0497] The server uses an AI-based algorithm to score the searched meal information. Here, weights are assigned to each option based on user preferences and constraints, and an evaluation is given accordingly. A machine learning library such as Scikit-learn is used to apply the model and calculate the goodness of fit for each option.

[0498] Step 4:

[0499] The server sends the optimal meal options to the terminal. This includes image data and text information, allowing the user to visually confirm the options. At this point, delivery options for the selected meals are also sent.

[0500] Step 5:

[0501] The user reviews the presented meal information and selects their desired option. After making their selection, the user confirms their choice and proceeds with the actual order or procurement of the food. This entire process is completed on the terminal, and the final data is sent to the server as feedback.

[0502] Step 6:

[0503] The server attempts to improve its data storage medium and AI algorithms based on feedback received from users. This improves the accuracy of recommendations in subsequent uses. The collected feedback data is used to retrain the model and update the database.

[0504] 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.

[0505] This invention is a system that provides an optimal cooking recipe by taking into account the user's input information and the user's emotional state. This system has a configuration that includes an interface means, a cooking database search means, a recipe selection algorithm means, and an emotion engine.

[0506] First, the user enters information about the dish through their device. This information includes ingredients, budget, preferences, allergy information, and health goals. The device converts this information into a data format and sends it to the server.

[0507] The server receives user input data and begins the process of accessing a recipe database to search for recipe candidates that match the criteria. The database contains detailed information about various dishes. Next, the server uses an emotion engine to recognize the user's emotional state. This emotional state is obtained through camera footage, voice input, or direct feedback from the user.

[0508] Based on the user's emotions recognized by the emotion engine, the server considers emotional data in the recipe scoring process. This scoring evaluates nutritional value, user preference, and whether it aligns with the user's current emotions. The dish category is adjusted according to the emotions, and the most suitable recipe is selected.

[0509] The server then processes the selected recipe and sends it to the terminal in a format adapted to the user's emotional state. The terminal displays the details of the suggested recipe to the user. The user can then browse the suggested recipes and enjoy preparing a meal that suits their mood at the time.

[0510] For example, if a user feels like relaxing, the emotion engine can recognize this and prioritize suggesting dishes suitable for a calm mood, such as soup or comfort food. In this way, the present invention improves user satisfaction by providing a personalized cooking experience that takes emotional states into account.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] The user accesses the device's interface and inputs subjective criteria such as ingredients, budget, taste preferences, allergy information, and health goals.

[0514] Step 2:

[0515] The terminal processes the user's input data and formats it into the necessary data format for transmission to the server.

[0516] Step 3:

[0517] The server receives data from the terminal and establishes a connection with the recipe database. The server then uses the database to search for the relevant recipe based on the user's criteria.

[0518] Step 4:

[0519] The server activates an emotion engine to recognize the user's emotions. This recognition is performed by analyzing the user's facial expressions and voice tone.

[0520] Step 5:

[0521] The server adjusts the scoring algorithm based on the recognized sentiment data. This ensures that recipe recommendations reflect the user's current mood.

[0522] Step 6:

[0523] The server selects the most suitable recipe based on the scoring results. Selection criteria include nutritional value, user preference, budget, and perceived emotional state.

[0524] Step 7:

[0525] The server processes the selected recipe to match the user's emotions and sends it to the terminal.

[0526] Step 8:

[0527] The terminal displays recipes received from the server to the user. The user can then use these recipes to create a dish that suits their mood.

[0528] Step 9:

[0529] After trying out a recipe, users can input feedback into a terminal to contribute to system improvements. The server uses this feedback to update its database and algorithms.

[0530] (Example 2)

[0531] 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."

[0532] In recent years, there has been a growing demand for personalized meal suggestions that cater to individual needs and emotions. However, conventional systems have struggled to adequately consider emotional states when selecting recipes. Furthermore, there has been a lack of methods to improve the accuracy of these systems by utilizing user feedback.

[0533] 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.

[0534] In this invention, the server includes a device for inputting user information, a device for searching for options that match the specified conditions from a recipe data collection, and a device for recognizing the emotional state and selecting the optimal option based on that state. This enables the selection of the optimal recipe according to the emotional state of each individual user and continuous improvement of the system through feedback.

[0535] "User information" refers to data that users use to input specific requirements and preferences, including information on ingredients, budget, tastes, and allergies.

[0536] "Device" refers to hardware or software components used for data input, retrieval, presentation, etc.

[0537] A "culinary data collection" is a database containing information about various dishes, and it is searchable according to the input criteria.

[0538] "Options" refers to individual recipes or cooking suggestions that match the specified criteria within the cooking data collection.

[0539] "Method" refers to the algorithm or process used to evaluate options and select the optimal one.

[0540] "Emotional state" refers to the user's current psychological and emotional condition and includes information used by the system to recognize and analyze it.

[0541] "Feedback" refers to opinions and suggestions for improvement from users, and is data used to improve the performance and accuracy of the system.

[0542] This invention is a system that provides optimal cooking suggestions by considering the user's condition information and emotional state. This system is realized by comprising a "device," a "cooking data collection," a "method," and an "emotion engine."

[0543] First, the user enters information about the dish via their device. This information includes elements such as ingredients, budget, preferences, allergy information, and health goals. The entered information is then converted by the device into a data format, such as JSON, and sent to the server.

[0544] When the server receives user input data, it accesses a "recipe data collection" to search for "options" that match the criteria. The search uses a database management system (DBMS) and employs techniques similar to SQL queries. Furthermore, the server uses an "emotion engine" to understand the user's emotional state. This emotional state is obtained using data from the camera and microphone connected to the device, and an emotion recognition AI model identifies emotions by analyzing facial expressions and voice tone.

[0545] Subsequently, the server uses a "method" to score the "options" based on the recognized emotions. It selects the optimal recipe using a generative AI model, evaluating nutritional value, user preferences, and suitability for the emotional state. The selected recipe is then processed in a format adapted to the user's emotions and sent to the terminal.

[0546] The device displays the received recipe information to the user in detail. Multimedia elements such as images, videos, and audio guides can be used as visual guidelines and supplementary information. This allows users to enjoy preparing the suggested recipes and have a meal experience that suits their mood.

[0547] For example, if a user enters conditions into their device such as "I want to relax, I have a low budget and would like a vegetarian menu," the system can analyze this and suggest gentle dishes such as "vegetable soup" or "pasta salad." An example of a prompt to the generative AI model might be, "The user is looking for a vegetarian, low-budget dinner. Please suggest a recipe that will help them relax."

[0548] This makes it possible for the present invention to realize personalized meal suggestions that are tailored to the individual's emotional state, thereby improving the meal preparation experience.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] Users input information about their cooking preferences through a terminal. This input includes ingredients, budget, tastes, allergy information, and health goals. The terminal converts this information into digital data format and sends it to the server using JSON format or similar.

[0552] Step 2:

[0553] The server receives user input data sent from the terminal. This data serves as the basis for searching for recipe candidates that match the specified criteria using a recipe database. The server uses a database management system and issues SQL queries to extract appropriate recipe information from a large database. As output, it obtains multiple recipe candidates that match the criteria.

[0554] Step 3:

[0555] The server uses the camera and microphone to recognize the user's emotional state. This involves using an emotion recognition AI model to analyze the user's facial expressions and voice tone. The results of this analysis are then organized into data indicating the emotional state. Specifically, it determines emotional states such as joy, relaxation, and stress. This data is the output used in the subsequent scoring process.

[0556] Step 4:

[0557] The server scores the recipe candidates obtained in step 2 based on the emotional state recognized in step 3. This scoring process analyzes nutritional value, user preferences, budget, and suitability for the emotional state. A generative AI model supports this process, comprehensively evaluating these factors to select the most suitable recipe. As a result, the optimal recipe for the user is output.

[0558] Step 5:

[0559] The server processes the selected recipe into a format suitable for the user's emotions. For example, visual effects or background music may be added to enhance relaxation. This processed recipe information is then sent to the terminal.

[0560] Step 6:

[0561] The device receives processed recipe information and displays it to the user. This display incorporates multimedia elements such as images, videos, and audio guides. Based on this detailed information, the user can visually confirm the suggested recipe and enjoy the cooking process.

[0562] (Application Example 2)

[0563] 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."

[0564] In modern life, the variety of food options is vast, making it difficult to choose the optimal meal based on user preferences and emotions. Furthermore, food delivery services often lack suggestions that consider user emotions, highlighting the need for improved customer satisfaction.

[0565] 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.

[0566] In this invention, the server includes an information processing device for inputting user information, means for receiving user input data and searching for meal suggestions that match the criteria from a set of meal data, and calculation means for recognizing the user's emotional state and scoring the searched meal suggestions based on that state. This makes it possible to suggest meal suggestions that correspond to the user's emotional state.

[0567] "User information" is a general term for data that includes users' dietary preferences, ingredients, budget, allergies, and health goals.

[0568] An "information processing device" is a terminal device that allows users to input information about their meals and send it to a server.

[0569] A "meal data collection" is a database containing recipes and nutritional information related to various meals.

[0570] "Emotional state" refers to a user's temporary psychological and emotional state, and is data based on facial expressions, voice, or user feedback.

[0571] The "computation means" is a configuration that executes an algorithm to score the optimal meal suggestion based on the user's input data and emotional state.

[0572] A "meal suggestion" is a recommendation of the optimal meal selected based on the user's conditions and feelings.

[0573] The system for implementing this invention consists of an information processing device that receives user input information and a server in a data center. The user inputs dietary conditions (e.g., ingredients, budget, preferences, allergies, health goals, etc.) through the information processing device. The information processing device converts this data into an appropriate format and transmits it to the server.

[0574] The server searches for meal suggestions that match the user's input data by comparing it against a set of meal data. Furthermore, the server uses a camera and voice input devices for emotion analysis to identify the user's emotional state. This emotion data is processed by an emotion analysis engine within the server. This engine uses generative AI models such as NVIDIA's Emotion AI to analyze the user's emotions in real time.

[0575] Based on user input and emotional data, the server evaluates meal suggestions and selects the optimal suggestion using a scoring algorithm. This process involves complex data calculations using software such as Python and OpenCV. The selected meal suggestions are sent to an information processing device as personalized content according to the user's emotional state and displayed to the user.

[0576] For example, if a user feels "a little tired" and communicates this to their smartphone, the information processing device sends this information to the server, which then suggests a relaxing meal tailored to the user's emotional state, such as a warm soup or comfort food. This allows users to enjoy a meal that better suits their emotions, contributing to increased satisfaction with the delivery service.

[0577] An example of a prompt message is, "How are you feeling today? Whether you want to refresh, relax, or feel energized, we'll suggest a dish that suits your mood."

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The user uses an information processing device to input dietary information (ingredients, budget, preferences, allergies, health goals, etc.). This information is converted into a digital format and sent to the server. The input is in text format, and the output is in a structured data format.

[0581] Step 2:

[0582] The information processing device uses the device's camera and microphone to collect the user's facial expressions and audio data for emotion analysis. This data is processed in real time to analyze the user's emotional state and transmit it to the server as emotion data. The input is camera video and audio data, and the output is quantitative data indicating the user's emotional state.

[0583] Step 3:

[0584] The server receives the user's input conditions and sentiment data, and searches the food data set for meal suggestions that match the conditions. This process uses a database search algorithm, and the output is a list of candidate meal suggestions.

[0585] Step 4:

[0586] The server uses an emotion analysis engine to evaluate emotional data and scores a list of meal suggestions based on the emotions expressed. Leveraging NVIDIA's Emotion AI model, it calculates the degree to which each meal suggestion matches the user's emotions and outputs a list of meal suggestions with scores.

[0587] Step 5:

[0588] The server selects the optimal meal plan from a scored list of meal plans. Using a score comparison algorithm written in Python, it outputs the meal plan with the highest score.

[0589] Step 6:

[0590] The information processing device presents the user with the optimal meal suggestions received from the server. This allows the user to select and order a meal that suits their mood. The output is the meal suggestions displayed on the user interface.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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".

[0608] This invention is a system for providing cooking recipes tailored to the user's preferences and needs, and it recommends the optimal recipe based on user input information using various cooking data. The system's operation and specific examples are shown below.

[0609] First, the user uses the terminal's user interface to input information such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal collects this information, converts it to an appropriate data format, and sends it to the server.

[0610] Based on the received user information, the server initiates access to the cooking database. This database contains recipes for various dishes, including ingredients, cooking methods, and nutritional information. The server queries the database according to the user's request and retrieves a list of recipes that match the criteria.

[0611] Next, the server runs an algorithm to score the retrieved recipes. This algorithm utilizes AI to assign evaluation values ​​to recipes, taking into account factors such as nutritional balance, user preferences, and budget constraints. It then selects the recipe with the highest evaluation and generates several recommended recipes.

[0612] The server then sends the details of the recommended recipe to the terminal. The terminal displays this to the user, allowing them to view the ingredient list, cooking instructions, nutritional information, and more. This makes it easy for the user to select and cook a meal that suits their needs.

[0613] For example, suppose a user specifies "a low-calorie Italian dish that can be made in under 30 minutes with a budget of 1000 yen." In this case, the server searches its Italian recipe database for relevant recipes and presents the user with a recipe that meets the criteria. The user can also send feedback from their device after cooking, which the server can then use to update the database and improve the algorithm.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The user enters information about their desired dish through the terminal's user interface, such as the ingredients they want to use, their budget, taste preferences, allergy information, and health goals.

[0617] Step 2:

[0618] The terminal receives user input data, converts it to the required data format (e.g., JSON format), and prepares it for transmission to the system.

[0619] Step 3:

[0620] The terminal sends the converted data to the server. The server then receives the information necessary for processing.

[0621] Step 4:

[0622] The server analyzes the received data and establishes a connection with the recipe database. Based on the analysis results, it identifies the categories of dishes and constraints that match the user's criteria.

[0623] Step 5:

[0624] Based on the specified conditions, the server queries the cooking database for suitable recipe candidates and retrieves the necessary detailed information (ingredients, cooking method, nutritional information, etc.).

[0625] Step 6:

[0626] The server runs an AI-powered algorithm to score the retrieved recipe candidates based on factors such as nutritional balance, user preference, and budget.

[0627] Step 7:

[0628] The server selects the optimal recipe based on the scoring results. It then organizes the details of the selected recipe and processes it into a format for providing to the user.

[0629] Step 8:

[0630] The server sends the selected recipe information to the terminal as a result of the processing.

[0631] Step 9:

[0632] The terminal receives recipe information from the server and displays the contents (list of ingredients, cooking instructions, nutritional information, etc.) to the user.

[0633] Step 10:

[0634] Users prepare dishes based on the provided recipes and enjoy the cooking experience. Afterward, they can send feedback from their device as needed.

[0635] (Example 1)

[0636] 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".

[0637] The challenge lies in providing users with the most relevant information quickly and accurately, and in continuously improving the accuracy of information retrieval and suggestions by incorporating user feedback. Conventional systems struggle to find appropriate information tailored to individual user needs, and lack effective methods for utilizing feedback.

[0638] 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.

[0639] In this invention, the server includes means for searching an information storage device based on data received from the user and obtaining information that matches the specified criteria; means for scoring the obtained information using an AI algorithm and selecting the optimal information; and means for receiving feedback from the user and improving the information storage device and the AI ​​algorithm. This enables the provision of information optimized to the individual needs of the user and continuous improvement of the system.

[0640] "User information" refers to data that details individual conditions and requests that users provide to the system, such as ingredients, budget, taste preferences, allergy information, and health goals.

[0641] A "terminal" is a computer device used by users to input information and exchange data with a server, and includes devices such as smartphones and personal computers.

[0642] An "interface means" is a function that provides a user interface for users to input information and enables data exchange between the user and the terminal.

[0643] A "server" is a remote computer system that performs various computational processes, such as processing received data, searching information storage devices, and scoring using AI algorithms.

[0644] An "information storage device" is a database or storage device for storing a wide variety of information, and is a data storage facility where recipes and related information are stored.

[0645] An "AI algorithm" is a program that uses machine learning techniques to perform advanced computational processing based on input information and derive the optimal result.

[0646] "Scoring" is the process of evaluating and quantifying data and information based on specific criteria, and is used to measure the suitability of recipes and suggestions.

[0647] "Feedback" refers to opinions and evaluations based on actual user experiences, and is data used to improve the system.

[0648] This invention is a system for providing information tailored to the individual needs of users. The system mainly consists of a terminal, a server, an information storage device (database), and an AI algorithm. The user first accesses an information processing interface using the terminal. The terminal receives input data from the user, such as ingredients, budget, taste preferences, allergy information, and health goals. The terminal also formats this data appropriately and sends it to the server.

[0649] The server searches its information storage device for information that matches the specified criteria based on the data received from the user. This information storage device functions as a database and stores various types of information. The server then scores the retrieved information using an AI algorithm. This scoring process evaluates and quantifies how well the information matches the user's needs. The AI ​​algorithm can be written in a programming language such as Python and includes a machine learning model. This model takes into account factors such as nutritional balance, user preferences, and budget constraints.

[0650] The server selects the most relevant information based on the scoring results and sends the details to the terminal. This information includes specific recipes and product purchase information. The terminal displays the information to the user, helping them make the most appropriate choice. Furthermore, the user can provide feedback, which is received by the server and used to improve the information storage and algorithms.

[0651] For example, when a user is looking for "low-calorie Italian food that can be made in under 30 minutes for a budget of 1000 yen," the prompt entered into the server would be "Suggest low-calorie Italian food that can be made in under 30 minutes." As a result, the server would select recipes that match the criteria and present them to the user. Through this series of processes, the system can provide optimal information tailored to the user's needs.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The user inputs information through the terminal's interface. This information includes ingredients used, budget, taste preferences, allergy information, and health goals. This data is received by the terminal. The terminal converts this user data into a specific format (e.g., JSON, XML) and formats it in a way that is easily interpretable by the server. The output of this step is the formatted data.

[0655] Step 2:

[0656] The terminal sends formatted user data to the server. This transmission utilizes an internet connection and a protocol (e.g., HTTP, HTTPS). Specifically, the terminal sends data to the server via an API. The input to this step is the formatted user data, and the output is the completion of the data transfer to the server.

[0657] Step 3:

[0658] The server queries the information storage device based on the user data it receives. It uses SQL or NoSQL queries to search for recipes and information that match the criteria. The server uses complex search expressions to retrieve data that matches the criteria. The input for this step is user data, and the output is a list of data that matches the criteria.

[0659] Step 4:

[0660] The server uses an AI algorithm to score the acquired information. The scoring process uses a machine learning model to consider factors such as nutritional value, user preferences, and budget, assigning an evaluation value to each recipe and piece of information. This data processing is performed using a specific programming language (e.g., Python). The input for this step is the acquired information, and the output is a list of information with assigned evaluation values.

[0661] Step 5:

[0662] The server selects the most highly rated information based on the scoring results and sends the details of that information to the terminal. This information includes the ingredients, cooking method, and nutritional information of the selected recipe. Specifically, the server uses an API to format the selected information and send it back to the terminal. The input for this step is the scoring results, and the output is the optimal information sent to the terminal.

[0663] Step 6:

[0664] The terminal displays information received from the server to the user. The user can then make necessary selections based on this displayed information. Specifically, the information is presented visually on the terminal screen for the user to read. The input for this step is information from the server, and the output is the display of information to the user.

[0665] Step 7:

[0666] Users can submit feedback from their terminal based on the information provided. This feedback is then sent back to the server and used to improve the system. The terminal formats the feedback data entered by the user and relays it to the server. The input in this step is the user's feedback, and the output is the completion of the transmission of the feedback data to the server.

[0667] (Application Example 1)

[0668] 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".

[0669] In today's lifestyle, consumers demand efficient and customized meal choices, while simultaneously facing challenges in effectively selecting, ordering, and procuring food within time and budget constraints. Therefore, there is a need for a means to provide appropriate and deliverable meal options based on individual user preferences and circumstances.

[0670] 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.

[0671] In this invention, the server includes an interface means for inputting user information, means for receiving user input data and searching for meals that match the criteria from a data storage medium, algorithm means for scoring the searched information and selecting the optimal meal, means for presenting the selected meal to the user, and means for presenting delivery options from the searched meal information. This makes it possible to effectively and efficiently present the user with the meal best suited to their individual criteria and its delivery options.

[0672] "An interface means for inputting user information" refers to an input device or application used by a user to provide their preferences and conditions.

[0673] "A means of receiving user input data and searching for meals that match the conditions from a data storage medium" refers to a function that searches stored meal information based on information received from the user and extracts options that are suitable for the conditions.

[0674] "An algorithmic means for scoring retrieved information and selecting the optimal meal" refers to a computational method for quantifying discovered meal information using multiple evaluation criteria and identifying the most suitable meal.

[0675] "Means for presenting selected meals to the user" refers to a part of a system that provides users with meal information identified by a selection algorithm, either visually or audibly.

[0676] "Means of presenting deliverable options from searched meal information" refers to a method of presenting meal options that are actually deliverable based on the user's residential area, using relevant culinary information.

[0677] This invention begins with a user inputting conditions such as food preferences, budget, taste and cooking time, and allergy information via a device such as a smartphone or tablet. The device's interface receives this information and transmits it to a server. Based on the received user input data, the server searches for options that match the conditions from a vast amount of food information stored on a data storage medium.

[0678] The server uses AI-powered algorithms to score the searched meal information against various evaluation criteria and identify the most appropriate dish. This algorithm utilizes machine learning libraries such as Scikit-learn and TensorFlow. The resulting meal options are selected, including delivery options that take into account the user's residential area.

[0679] The selected meal information is visually presented to the user via a terminal. The user can choose from the presented options to suit their preferences. This information includes details about the dishes and whether delivery is available, and it is possible to place an order.

[0680] For example, if a user requests "vegetarian Japanese food priced under 2000 yen that can be delivered within 30 minutes," the server will identify matching dishes from its database and display the corresponding delivery options.

[0681] Example of a prompt:

[0682] Given user preferences for a vegetarian Japanese dish under $20 that can be delivered within 30 minutes, propose suitable meal options along with delivery services.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] Users input information such as ingredients, budget, taste preferences, allergy information, and health goals using the terminal's interface. This input data is aggregated on the terminal and sent to the server in an appropriate format. This process collects user selections and converts them into a data format that can be processed by a machine.

[0686] Step 2:

[0687] The server receives user input data. Based on the received data, it accesses the data storage medium and searches for meal information that matches the criteria. In this step, techniques such as SQL queries are used to extract the necessary data quickly and efficiently.

[0688] Step 3:

[0689] The server uses an AI-based algorithm to score the searched meal information. Here, weights are assigned to each option based on user preferences and constraints, and an evaluation is given accordingly. A machine learning library such as Scikit-learn is used to apply the model and calculate the goodness of fit for each option.

[0690] Step 4:

[0691] The server sends the optimal meal options to the terminal. This includes image data and text information, allowing the user to visually confirm the options. At this point, delivery options for the selected meals are also sent.

[0692] Step 5:

[0693] The user reviews the presented meal information and selects their desired option. After making their selection, the user confirms their choice and proceeds with the actual order or procurement of the food. This entire process is completed on the terminal, and the final data is sent to the server as feedback.

[0694] Step 6:

[0695] The server attempts to improve its data storage medium and AI algorithms based on feedback received from users. This improves the accuracy of recommendations in subsequent uses. The collected feedback data is used to retrain the model and update the database.

[0696] 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.

[0697] This invention is a system that provides an optimal cooking recipe by taking into account the user's input information and the user's emotional state. This system has a configuration that includes an interface means, a cooking database search means, a recipe selection algorithm means, and an emotion engine.

[0698] First, the user enters information about the dish through their device. This information includes ingredients, budget, preferences, allergy information, and health goals. The device converts this information into a data format and sends it to the server.

[0699] The server receives user input data and begins the process of accessing a recipe database to search for recipe candidates that match the criteria. The database contains detailed information about various dishes. Next, the server uses an emotion engine to recognize the user's emotional state. This emotional state is obtained through camera footage, voice input, or direct feedback from the user.

[0700] Based on the user's emotions recognized by the emotion engine, the server considers emotional data in the recipe scoring process. This scoring evaluates nutritional value, user preference, and whether it aligns with the user's current emotions. The dish category is adjusted according to the emotions, and the most suitable recipe is selected.

[0701] The server then processes the selected recipe and sends it to the terminal in a format adapted to the user's emotional state. The terminal displays the details of the suggested recipe to the user. The user can then browse the suggested recipes and enjoy preparing a meal that suits their mood at the time.

[0702] For example, if a user feels like relaxing, the emotion engine can recognize this and prioritize suggesting dishes suitable for a calm mood, such as soup or comfort food. In this way, the present invention improves user satisfaction by providing a personalized cooking experience that takes emotional states into account.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user accesses the device's interface and inputs subjective criteria such as ingredients, budget, taste preferences, allergy information, and health goals.

[0706] Step 2:

[0707] The terminal processes the user's input data and formats it into the necessary data format for transmission to the server.

[0708] Step 3:

[0709] The server receives data from the terminal and establishes a connection with the recipe database. The server then uses the database to search for the relevant recipe based on the user's criteria.

[0710] Step 4:

[0711] The server activates an emotion engine to recognize the user's emotions. This recognition is performed by analyzing the user's facial expressions and voice tone.

[0712] Step 5:

[0713] The server adjusts the scoring algorithm based on the recognized sentiment data. This ensures that recipe recommendations reflect the user's current mood.

[0714] Step 6:

[0715] The server selects the most suitable recipe based on the scoring results. Selection criteria include nutritional value, user preference, budget, and perceived emotional state.

[0716] Step 7:

[0717] The server processes the selected recipe to match the user's emotions and sends it to the terminal.

[0718] Step 8:

[0719] The terminal displays recipes received from the server to the user. The user can then use these recipes to create a dish that suits their mood.

[0720] Step 9:

[0721] After trying out a recipe, users can input feedback into a terminal to contribute to system improvements. The server uses this feedback to update its database and algorithms.

[0722] (Example 2)

[0723] 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".

[0724] In recent years, there has been a growing demand for personalized meal suggestions that cater to individual needs and emotions. However, conventional systems have struggled to adequately consider emotional states when selecting recipes. Furthermore, there has been a lack of methods to improve the accuracy of these systems by utilizing user feedback.

[0725] 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.

[0726] In this invention, the server includes a device for inputting user information, a device for searching for options that match the specified conditions from a recipe data collection, and a device for recognizing the emotional state and selecting the optimal option based on that state. This enables the selection of the optimal recipe according to the emotional state of each individual user and continuous improvement of the system through feedback.

[0727] "User information" refers to data that users use to input specific requirements and preferences, including information on ingredients, budget, tastes, and allergies.

[0728] "Device" refers to hardware or software components used for data input, retrieval, presentation, etc.

[0729] A "culinary data collection" is a database containing information about various dishes, and it is searchable according to the input criteria.

[0730] "Options" refers to individual recipes or cooking suggestions that match the specified criteria within the cooking data collection.

[0731] "Method" refers to the algorithm or process used to evaluate options and select the optimal one.

[0732] "Emotional state" refers to the user's current psychological and emotional condition and includes information used by the system to recognize and analyze it.

[0733] "Feedback" refers to opinions and suggestions for improvement from users, and is data used to improve the performance and accuracy of the system.

[0734] This invention is a system that provides optimal cooking suggestions by considering the user's condition information and emotional state. This system is realized by comprising a "device," a "cooking data collection," a "method," and an "emotion engine."

[0735] First, the user enters information about the dish via their device. This information includes elements such as ingredients, budget, preferences, allergy information, and health goals. The entered information is then converted by the device into a data format, such as JSON, and sent to the server.

[0736] When the server receives user input data, it accesses a "recipe data collection" to search for "options" that match the criteria. The search uses a database management system (DBMS) and employs techniques similar to SQL queries. Furthermore, the server uses an "emotion engine" to understand the user's emotional state. This emotional state is obtained using data from the camera and microphone connected to the device, and an emotion recognition AI model identifies emotions by analyzing facial expressions and voice tone.

[0737] Subsequently, the server uses a "method" to score the "options" based on the recognized emotions. It selects the optimal recipe using a generative AI model, evaluating nutritional value, user preferences, and suitability for the emotional state. The selected recipe is then processed in a format adapted to the user's emotions and sent to the terminal.

[0738] The device displays the received recipe information to the user in detail. Multimedia elements such as images, videos, and audio guides can be used as visual guidelines and supplementary information. This allows users to enjoy preparing the suggested recipes and have a meal experience that suits their mood.

[0739] For example, if a user enters conditions into their device such as "I want to relax, I have a low budget and would like a vegetarian menu," the system can analyze this and suggest gentle dishes such as "vegetable soup" or "pasta salad." An example of a prompt to the generative AI model might be, "The user is looking for a vegetarian, low-budget dinner. Please suggest a recipe that will help them relax."

[0740] This makes it possible for the present invention to realize personalized meal suggestions that are tailored to the individual's emotional state, thereby improving the meal preparation experience.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] Users input information about their cooking preferences through a terminal. This input includes ingredients, budget, tastes, allergy information, and health goals. The terminal converts this information into digital data format and sends it to the server using JSON format or similar.

[0744] Step 2:

[0745] The server receives user input data sent from the terminal. This data serves as the basis for searching for recipe candidates that match the specified criteria using a recipe database. The server uses a database management system and issues SQL queries to extract appropriate recipe information from a large database. As output, it obtains multiple recipe candidates that match the criteria.

[0746] Step 3:

[0747] The server uses the camera and microphone to recognize the user's emotional state. This involves using an emotion recognition AI model to analyze the user's facial expressions and voice tone. The results of this analysis are then organized into data indicating the emotional state. Specifically, it determines emotional states such as joy, relaxation, and stress. This data is the output used in the subsequent scoring process.

[0748] Step 4:

[0749] The server scores the recipe candidates obtained in step 2 based on the emotional state recognized in step 3. This scoring process analyzes nutritional value, user preferences, budget, and suitability for the emotional state. A generative AI model supports this process, comprehensively evaluating these factors to select the most suitable recipe. As a result, the optimal recipe for the user is output.

[0750] Step 5:

[0751] The server processes the selected recipe into a format suitable for the user's emotions. For example, visual effects or background music may be added to enhance relaxation. This processed recipe information is then sent to the terminal.

[0752] Step 6:

[0753] The device receives processed recipe information and displays it to the user. This display incorporates multimedia elements such as images, videos, and audio guides. Based on this detailed information, the user can visually confirm the suggested recipe and enjoy the cooking process.

[0754] (Application Example 2)

[0755] 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".

[0756] In modern life, the variety of food options is vast, making it difficult to choose the optimal meal based on user preferences and emotions. Furthermore, food delivery services often lack suggestions that consider user emotions, highlighting the need for improved customer satisfaction.

[0757] 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.

[0758] In this invention, the server includes an information processing device for inputting user information, means for receiving user input data and searching for meal suggestions that match the criteria from a set of meal data, and calculation means for recognizing the user's emotional state and scoring the searched meal suggestions based on that state. This makes it possible to suggest meal suggestions that correspond to the user's emotional state.

[0759] "User information" is a general term for data that includes users' dietary preferences, ingredients, budget, allergies, and health goals.

[0760] An "information processing device" is a terminal device that allows users to input information about their meals and send it to a server.

[0761] A "meal data collection" is a database containing recipes and nutritional information related to various meals.

[0762] "Emotional state" refers to a user's temporary psychological and emotional state, and is data based on facial expressions, voice, or user feedback.

[0763] The "computation means" is a configuration that executes an algorithm to score the optimal meal suggestion based on the user's input data and emotional state.

[0764] A "meal suggestion" is a recommendation of the optimal meal selected based on the user's conditions and feelings.

[0765] The system for implementing this invention consists of an information processing device that receives user input information and a server in a data center. The user inputs dietary conditions (e.g., ingredients, budget, preferences, allergies, health goals, etc.) through the information processing device. The information processing device converts this data into an appropriate format and transmits it to the server.

[0766] The server searches for meal suggestions that match the user's input data by comparing it against a set of meal data. Furthermore, the server uses a camera and voice input devices for emotion analysis to identify the user's emotional state. This emotion data is processed by an emotion analysis engine within the server. This engine uses generative AI models such as NVIDIA's Emotion AI to analyze the user's emotions in real time.

[0767] Based on user input and emotional data, the server evaluates meal suggestions and selects the optimal suggestion using a scoring algorithm. This process involves complex data calculations using software such as Python and OpenCV. The selected meal suggestions are sent to an information processing device as personalized content according to the user's emotional state and displayed to the user.

[0768] For example, if a user feels "a little tired" and communicates this to their smartphone, the information processing device sends this information to the server, which then suggests a relaxing meal tailored to the user's emotional state, such as a warm soup or comfort food. This allows users to enjoy a meal that better suits their emotions, contributing to increased satisfaction with the delivery service.

[0769] An example of a prompt message is, "How are you feeling today? Whether you want to refresh, relax, or feel energized, we'll suggest a dish that suits your mood."

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The user uses an information processing device to input dietary information (ingredients, budget, preferences, allergies, health goals, etc.). This information is converted into a digital format and sent to the server. The input is in text format, and the output is in a structured data format.

[0773] Step 2:

[0774] The information processing device uses the device's camera and microphone to collect the user's facial expressions and audio data for emotion analysis. This data is processed in real time to analyze the user's emotional state and transmit it to the server as emotion data. The input is camera video and audio data, and the output is quantitative data indicating the user's emotional state.

[0775] Step 3:

[0776] The server receives the user's input conditions and sentiment data, and searches the food data set for meal suggestions that match the conditions. This process uses a database search algorithm, and the output is a list of candidate meal suggestions.

[0777] Step 4:

[0778] The server uses an emotion analysis engine to evaluate emotional data and scores a list of meal suggestions based on the emotions expressed. Leveraging NVIDIA's Emotion AI model, it calculates the degree to which each meal suggestion matches the user's emotions and outputs a list of meal suggestions with scores.

[0779] Step 5:

[0780] The server selects the optimal meal plan from a scored list of meal plans. Using a score comparison algorithm written in Python, it outputs the meal plan with the highest score.

[0781] Step 6:

[0782] The information processing device presents the user with the optimal meal suggestions received from the server. This allows the user to select and order a meal that suits their mood. The output is the meal suggestions displayed on the user interface.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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."

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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 as being incorporated by reference.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] An interface means for inputting user information,

[0807] A means of receiving user input data and searching for recipes that match the criteria from a cooking database,

[0808] An algorithmic means for scoring the searched recipes and selecting the optimal recipe,

[0809] A means of presenting selected recipes to users,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for receiving user feedback and improving the recipe database and algorithms.

[0813] (Claim 3)

[0814] The system according to claim 1, comprising means for selecting a recipe based on multiple conditions, taking into account nutritional value, taste preferences, and budget constraints.

[0815] "Example 1"

[0816] (Claim 1)

[0817] A terminal interface means for inputting user information,

[0818] A terminal means that receives user input data, converts it into a data format, and transmits it,

[0819] A server means that searches an information storage device based on data received from a user and retrieves information that matches the specified conditions,

[0820] A server that scores information acquired using an AI algorithm and selects the most suitable information,

[0821] A server means that transmits the details of the selected information to the terminal and presents it to the user,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising a server means for receiving user feedback and improving information storage devices and AI algorithms.

[0825] (Claim 3)

[0826] The system according to claim 1, comprising a server means for selecting information based on multiple conditions, taking into account quality characteristics, personal preferences, and budget constraints.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] An interface means for inputting user information,

[0830] A means for receiving user input data and searching for meals that match the criteria from a data storage medium,

[0831] An algorithmic means for scoring the searched information and selecting the optimal meal,

[0832] A means of presenting selected meals to the user,

[0833] A means of presenting delivery options based on searched meal information,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, further comprising means for receiving user feedback and improving data storage media and algorithms.

[0837] (Claim 3)

[0838] The system according to claim 1, having means for selecting meals based on multiple conditions, taking into account nutritional value, preferences, and cost constraints, and associating deliverable services.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] A device for inputting user information,

[0842] A device that receives user input data and searches for a matching option from a recipe data collection,

[0843] A method for evaluating the searched options and selecting the best option,

[0844] A device for recognizing the user's emotional state and making selections based on it,

[0845] A device for presenting selected options to the user,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising a device for receiving user feedback and improving data collection and methods.

[0849] (Claim 3)

[0850] The system according to claim 1, comprising a device for selecting based on multiple conditions, taking into account nutritional value, taste preferences, budget constraints, and the user's emotional state.

[0851] "Application example 2 of combining emotional engines"

[0852] (Claim 1)

[0853] An information processing device for inputting user information,

[0854] A means for receiving user input data and searching for meal suggestions that match the conditions from a collection of meal data,

[0855] A computation means for recognizing the user's emotional state and scoring the searched meal suggestions based on that state,

[0856] Methods for selecting the optimal meal plan,

[0857] A means of presenting selected meal options to the user,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising analytical means for processing user emotional data, for suggesting meals to a user according to their mood.

[0861] (Claim 3)

[0862] The system according to claim 1, comprising a computing device for emotional analysis and means for selecting a meal plan based on multiple conditions, taking into account nutritional value, preferences, and budget constraints. [Explanation of Symbols]

[0863] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An interface means for inputting user information, A means of receiving user input data and searching for recipes that match the criteria from a cooking database, An algorithmic means for scoring the searched recipes and selecting the optimal recipe, A means of presenting selected recipes to users, A system that includes this.

2. The system according to claim 1, further comprising means for receiving user feedback and improving the recipe database and algorithms.

3. The system according to claim 1, comprising means for selecting a recipe based on multiple conditions, taking into account nutritional value, taste preferences, and budget constraints.

Images